01. Data Exploration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>nameOrig</th>\n",
       "      <th>oldBalanceOrig</th>\n",
       "      <th>newBalanceOrig</th>\n",
       "      <th>nameDest</th>\n",
       "      <th>oldBalanceDest</th>\n",
       "      <th>newBalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>isFlaggedFraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>9839.64</td>\n",
       "      <td>C1231006815</td>\n",
       "      <td>170136.0</td>\n",
       "      <td>160296.36</td>\n",
       "      <td>M1979787155</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>1864.28</td>\n",
       "      <td>C1666544295</td>\n",
       "      <td>21249.0</td>\n",
       "      <td>19384.72</td>\n",
       "      <td>M2044282225</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>181.00</td>\n",
       "      <td>C1305486145</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C553264065</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>181.00</td>\n",
       "      <td>C840083671</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C38997010</td>\n",
       "      <td>21182.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>11668.14</td>\n",
       "      <td>C2048537720</td>\n",
       "      <td>41554.0</td>\n",
       "      <td>29885.86</td>\n",
       "      <td>M1230701703</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   step      type    amount     nameOrig  oldBalanceOrig  newBalanceOrig  \\\n",
       "0     1   PAYMENT   9839.64  C1231006815        170136.0       160296.36   \n",
       "1     1   PAYMENT   1864.28  C1666544295         21249.0        19384.72   \n",
       "2     1  TRANSFER    181.00  C1305486145           181.0            0.00   \n",
       "3     1  CASH_OUT    181.00   C840083671           181.0            0.00   \n",
       "4     1   PAYMENT  11668.14  C2048537720         41554.0        29885.86   \n",
       "\n",
       "      nameDest  oldBalanceDest  newBalanceDest  isFraud  isFlaggedFraud  \n",
       "0  M1979787155             0.0             0.0        0               0  \n",
       "1  M2044282225             0.0             0.0        0               0  \n",
       "2   C553264065             0.0             0.0        1               0  \n",
       "3    C38997010         21182.0             0.0        1               0  \n",
       "4  M1230701703             0.0             0.0        0               0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"/Users/Abhirup/Desktop/Projects/HSBC DS Assignment/PS_20174392719_1491204439457_log.csv\")\n",
    "df.sort_values(by=['step'])\n",
    "df = df.rename(columns={'oldbalanceOrg':'oldBalanceOrig', 'newbalanceOrig':'newBalanceOrig', \\\n",
    "                        'oldbalanceDest':'oldBalanceDest', 'newbalanceDest':'newBalanceDest'})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6362620, 11)\n",
      "step                int64\n",
      "type               object\n",
      "amount            float64\n",
      "nameOrig           object\n",
      "oldBalanceOrig    float64\n",
      "newBalanceOrig    float64\n",
      "nameDest           object\n",
      "oldBalanceDest    float64\n",
      "newBalanceDest    float64\n",
      "isFraud             int64\n",
      "isFlaggedFraud      int64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)\n",
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      2708\n",
       "2      1014\n",
       "3       552\n",
       "4       565\n",
       "5       665\n",
       "       ... \n",
       "739      10\n",
       "740       6\n",
       "741      22\n",
       "742      14\n",
       "743       8\n",
       "Name: step, Length: 743, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['step'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.257, 186.5]    2247887\n",
       "(186.5, 372.0]    3038001\n",
       "(372.0, 557.5]     882793\n",
       "(557.5, 743.0]     193939\n",
       "Name: step, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['step'].value_counts(bins=4).sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>nameOrig</th>\n",
       "      <th>oldBalanceOrig</th>\n",
       "      <th>newBalanceOrig</th>\n",
       "      <th>nameDest</th>\n",
       "      <th>oldBalanceDest</th>\n",
       "      <th>newBalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>isFlaggedFraud</th>\n",
       "      <th>week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>9839.64</td>\n",
       "      <td>C1231006815</td>\n",
       "      <td>170136.0</td>\n",
       "      <td>160296.36</td>\n",
       "      <td>M1979787155</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>1864.28</td>\n",
       "      <td>C1666544295</td>\n",
       "      <td>21249.0</td>\n",
       "      <td>19384.72</td>\n",
       "      <td>M2044282225</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>181.00</td>\n",
       "      <td>C1305486145</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C553264065</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>181.00</td>\n",
       "      <td>C840083671</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C38997010</td>\n",
       "      <td>21182.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>11668.14</td>\n",
       "      <td>C2048537720</td>\n",
       "      <td>41554.0</td>\n",
       "      <td>29885.86</td>\n",
       "      <td>M1230701703</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   step      type    amount     nameOrig  oldBalanceOrig  newBalanceOrig  \\\n",
       "0     1   PAYMENT   9839.64  C1231006815        170136.0       160296.36   \n",
       "1     1   PAYMENT   1864.28  C1666544295         21249.0        19384.72   \n",
       "2     1  TRANSFER    181.00  C1305486145           181.0            0.00   \n",
       "3     1  CASH_OUT    181.00   C840083671           181.0            0.00   \n",
       "4     1   PAYMENT  11668.14  C2048537720         41554.0        29885.86   \n",
       "\n",
       "      nameDest  oldBalanceDest  newBalanceDest  isFraud  isFlaggedFraud  week  \n",
       "0  M1979787155             0.0             0.0        0               0   1.0  \n",
       "1  M2044282225             0.0             0.0        0               0   1.0  \n",
       "2   C553264065             0.0             0.0        1               0   1.0  \n",
       "3    C38997010         21182.0             0.0        1               0   1.0  \n",
       "4  M1230701703             0.0             0.0        0               0   1.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Since Steps are hours, make bins of observations to weeks\n",
    "for i in range(4):\n",
    "    df.loc[(df['step']>186*i) & (df['step']<=186*(i+1)),'week'] =i+1\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0    2247887\n",
      "2.0    3038001\n",
      "3.0     882807\n",
      "4.0     193925\n",
      "Name: week, dtype: int64\n",
      "week\n",
      "1.0    2122\n",
      "2.0    2031\n",
      "3.0    2012\n",
      "4.0    2048\n",
      "Name: isFraud, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df['week'].value_counts().sort_index())\n",
    "print(df.groupby('week')['isFraud'].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Findings1: Frauds are evenly distributed across weeks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "step              0\n",
       "type              0\n",
       "amount            0\n",
       "nameOrig          0\n",
       "oldBalanceOrig    0\n",
       "newBalanceOrig    0\n",
       "nameDest          0\n",
       "oldBalanceDest    0\n",
       "newBalanceDest    0\n",
       "isFraud           0\n",
       "isFlaggedFraud    0\n",
       "week              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Findings 2: No missing Value in Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "week\n",
      "1.0    2247887\n",
      "2.0    3038001\n",
      "3.0     882807\n",
      "4.0     193925\n",
      "Name: nameOrig, dtype: int64\n",
      "week\n",
      "1.0    2246734\n",
      "2.0    3035880\n",
      "3.0     882609\n",
      "4.0     193920\n",
      "Name: nameOrig, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.groupby('week')['nameOrig'].count())\n",
    "print(df.groupby('week')['nameOrig'].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Customers Transacting both in Week 1 and Week 2\n",
      "3244\n",
      "\n",
      "Customers Transacting both in Week 1 and Week 3\n",
      "865\n",
      "\n",
      "Customers Transacting both in Week 2 and Week 3\n",
      "1185\n",
      "\n",
      "Customers Transacting both in Week 2 and Week 4\n",
      "270\n",
      "\n",
      "Customers Transacting both in Week 3 and Week 4\n",
      "73\n",
      "\n",
      "Customers Transacting both in Week 3 and Week 5\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,4):\n",
    "    print(\"\\nCustomers Transacting both in Week %s and Week %s\"%(i,i+1))\n",
    "    print(pd.merge(df.loc[df['week']==i,'nameOrig'],df.loc[df['week']==i+1,'nameOrig'],on='nameOrig')\n",
    "      ['nameOrig'].nunique())\n",
    "    print(\"\\nCustomers Transacting both in Week %s and Week %s\"%(i,i+2))\n",
    "    print(pd.merge(df.loc[df['week']==i,'nameOrig'],df.loc[df['week']==i+2,'nameOrig'],on='nameOrig')\n",
    "      ['nameOrig'].nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Findings 3: not many customers repeating transactions every week. historical me vs me variables will be sparse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Fraud Instances of Common Customers across Weeks\n",
      "week\n",
      "1.0    1\n",
      "2.0    3\n",
      "3.0    3\n",
      "4.0    0\n",
      "Name: isFraud, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "common_cust_df=pd.DataFrame(columns=['nameOrig'])\n",
    "for i in range(1,4):\n",
    "    common_cust_df=common_cust_df.append(pd.merge(df.loc[df['week']==i,'nameOrig'],df.loc[df['week']==i+1,'nameOrig'],\n",
    "                                                  on='nameOrig'))\n",
    "                                \n",
    "common_cust_list = common_cust_df['nameOrig'].tolist()\n",
    "print(\"\\nFraud Instances of Common Customers across Weeks\")\n",
    "print(df[df['nameOrig'].isin(common_cust_list)].groupby('week')['isFraud'].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Findings 4: less fraud among repeat transacting customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "week  type    \n",
       "1.0   CASH_IN        0\n",
       "      CASH_OUT    1069\n",
       "      DEBIT          0\n",
       "      PAYMENT        0\n",
       "      TRANSFER    1053\n",
       "2.0   CASH_IN        0\n",
       "      CASH_OUT    1017\n",
       "      DEBIT          0\n",
       "      PAYMENT        0\n",
       "      TRANSFER    1014\n",
       "3.0   CASH_IN        0\n",
       "      CASH_OUT    1006\n",
       "      DEBIT          0\n",
       "      PAYMENT        0\n",
       "      TRANSFER    1006\n",
       "4.0   CASH_IN        0\n",
       "      CASH_OUT    1024\n",
       "      DEBIT          0\n",
       "      PAYMENT        0\n",
       "      TRANSFER    1024\n",
       "Name: isFraud, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['week','type'])['isFraud'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Findings 5: only CASH_OUT and TRANSFER have Fraud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "isFraud\n",
      "0     0\n",
      "1    16\n",
      "Name: isFlaggedFraud, dtype: int64\n",
      "(0, 12)\n"
     ]
    }
   ],
   "source": [
    "print(df.groupby(['isFraud'])['isFlaggedFraud'].sum())\n",
    "print(df.loc[(df['isFraud']==0) & (df['isFlaggedFraud']==1),:].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TRANSFER    16\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['isFlaggedFraud']==1]['type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Findings 6: isFlaggedFraud is only 1 when isFraud is 1 and type is TRANSFER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count         16.000000\n",
       "mean     4861597.730625\n",
       "std      3572499.102347\n",
       "min       353874.220000\n",
       "25%      2242748.722500\n",
       "50%      4234245.095000\n",
       "75%      7883451.380000\n",
       "max     10000000.000000\n",
       "Name: amount, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('float_format', '{:f}'.format)\n",
    "df[df['isFlaggedFraud']==1]['amount'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    322893\n",
       "1      2246\n",
       "Name: isFraud, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[(df['amount']>=353874.22) & (df['type']=='TRANSFER'),'isFraud'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Findings 7: all transactions above the minimum amount when isFlaggedFraud==1 is not Fraud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Type of transactions where isFlaggedFraud=1\n",
      "TRANSFER    16\n",
      "Name: type, dtype: int64\n",
      "\n",
      "Max amount transacted in a TRANSFER where isFlaggedFraud is 0\n",
      "92445516.64\n"
     ]
    }
   ],
   "source": [
    "print('\\nType of transactions where isFlaggedFraud=1')\n",
    "print((df.loc[df['isFlaggedFraud']==1,'type']).value_counts())\n",
    "print('\\nMax amount transacted in a TRANSFER where isFlaggedFraud is 0')\n",
    "print(df.loc[(df['type'] == 'TRANSFER') & (df['isFlaggedFraud'] == 0),'amount'].max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Findings 8: isFlaggedFraud definition is not consistent with the definition provided"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>nameOrig</th>\n",
       "      <th>oldBalanceOrig</th>\n",
       "      <th>newBalanceOrig</th>\n",
       "      <th>nameDest</th>\n",
       "      <th>oldBalanceDest</th>\n",
       "      <th>newBalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>isFlaggedFraud</th>\n",
       "      <th>week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2736446</td>\n",
       "      <td>212</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>4953893.080000</td>\n",
       "      <td>C728984460</td>\n",
       "      <td>4953893.080000</td>\n",
       "      <td>4953893.080000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>TRANSFER</td>\n",
       "      <td>1343002.080000</td>\n",
       "      <td>C1100582606</td>\n",
       "      <td>1343002.080000</td>\n",
       "      <td>1343002.080000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>1</td>\n",
       "      <td>2.000000</td>\n",
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       "    <tr>\n",
       "      <td>3760288</td>\n",
       "      <td>279</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>536624.410000</td>\n",
       "      <td>C1035541766</td>\n",
       "      <td>536624.410000</td>\n",
       "      <td>536624.410000</td>\n",
       "      <td>C1100697970</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5563713</td>\n",
       "      <td>387</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>4892193.090000</td>\n",
       "      <td>C908544136</td>\n",
       "      <td>4892193.090000</td>\n",
       "      <td>4892193.090000</td>\n",
       "      <td>C891140444</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>1</td>\n",
       "      <td>3.000000</td>\n",
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       "    <tr>\n",
       "      <td>5996407</td>\n",
       "      <td>425</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>10000000.000000</td>\n",
       "      <td>C689608084</td>\n",
       "      <td>19585040.370000</td>\n",
       "      <td>19585040.370000</td>\n",
       "      <td>C1392803603</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5996409</td>\n",
       "      <td>425</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>9585040.370000</td>\n",
       "      <td>C452586515</td>\n",
       "      <td>19585040.370000</td>\n",
       "      <td>19585040.370000</td>\n",
       "      <td>C1109166882</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6168499</td>\n",
       "      <td>554</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>3576297.100000</td>\n",
       "      <td>C193696150</td>\n",
       "      <td>3576297.100000</td>\n",
       "      <td>3576297.100000</td>\n",
       "      <td>C484597480</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.000000</td>\n",
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       "    <tr>\n",
       "      <td>6205439</td>\n",
       "      <td>586</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>353874.220000</td>\n",
       "      <td>C1684585475</td>\n",
       "      <td>353874.220000</td>\n",
       "      <td>353874.220000</td>\n",
       "      <td>C1770418982</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.000000</td>\n",
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       "    <tr>\n",
       "      <td>6266413</td>\n",
       "      <td>617</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>2542664.270000</td>\n",
       "      <td>C786455622</td>\n",
       "      <td>2542664.270000</td>\n",
       "      <td>2542664.270000</td>\n",
       "      <td>C661958277</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>6281482</td>\n",
       "      <td>646</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>10000000.000000</td>\n",
       "      <td>C19004745</td>\n",
       "      <td>10399045.080000</td>\n",
       "      <td>10399045.080000</td>\n",
       "      <td>C1806199534</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.000000</td>\n",
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       "    <tr>\n",
       "      <td>6281484</td>\n",
       "      <td>646</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>399045.080000</td>\n",
       "      <td>C724693370</td>\n",
       "      <td>10399045.080000</td>\n",
       "      <td>10399045.080000</td>\n",
       "      <td>C1909486199</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6296014</td>\n",
       "      <td>671</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>3441041.460000</td>\n",
       "      <td>C917414431</td>\n",
       "      <td>3441041.460000</td>\n",
       "      <td>3441041.460000</td>\n",
       "      <td>C1082139865</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6351225</td>\n",
       "      <td>702</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>3171085.590000</td>\n",
       "      <td>C1892216157</td>\n",
       "      <td>3171085.590000</td>\n",
       "      <td>3171085.590000</td>\n",
       "      <td>C1308068787</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6362460</td>\n",
       "      <td>730</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>10000000.000000</td>\n",
       "      <td>C2140038573</td>\n",
       "      <td>17316255.050000</td>\n",
       "      <td>17316255.050000</td>\n",
       "      <td>C1395467927</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6362462</td>\n",
       "      <td>730</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>7316255.050000</td>\n",
       "      <td>C1869569059</td>\n",
       "      <td>17316255.050000</td>\n",
       "      <td>17316255.050000</td>\n",
       "      <td>C1861208726</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6362584</td>\n",
       "      <td>741</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>5674547.890000</td>\n",
       "      <td>C992223106</td>\n",
       "      <td>5674547.890000</td>\n",
       "      <td>5674547.890000</td>\n",
       "      <td>C1366804249</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         step      type          amount     nameOrig  oldBalanceOrig  \\\n",
       "2736446   212  TRANSFER  4953893.080000   C728984460  4953893.080000   \n",
       "3247297   250  TRANSFER  1343002.080000  C1100582606  1343002.080000   \n",
       "3760288   279  TRANSFER   536624.410000  C1035541766   536624.410000   \n",
       "5563713   387  TRANSFER  4892193.090000   C908544136  4892193.090000   \n",
       "5996407   425  TRANSFER 10000000.000000   C689608084 19585040.370000   \n",
       "5996409   425  TRANSFER  9585040.370000   C452586515 19585040.370000   \n",
       "6168499   554  TRANSFER  3576297.100000   C193696150  3576297.100000   \n",
       "6205439   586  TRANSFER   353874.220000  C1684585475   353874.220000   \n",
       "6266413   617  TRANSFER  2542664.270000   C786455622  2542664.270000   \n",
       "6281482   646  TRANSFER 10000000.000000    C19004745 10399045.080000   \n",
       "6281484   646  TRANSFER   399045.080000   C724693370 10399045.080000   \n",
       "6296014   671  TRANSFER  3441041.460000   C917414431  3441041.460000   \n",
       "6351225   702  TRANSFER  3171085.590000  C1892216157  3171085.590000   \n",
       "6362460   730  TRANSFER 10000000.000000  C2140038573 17316255.050000   \n",
       "6362462   730  TRANSFER  7316255.050000  C1869569059 17316255.050000   \n",
       "6362584   741  TRANSFER  5674547.890000   C992223106  5674547.890000   \n",
       "\n",
       "         newBalanceOrig     nameDest  oldBalanceDest  newBalanceDest  isFraud  \\\n",
       "2736446  4953893.080000   C639921569        0.000000        0.000000        1   \n",
       "3247297  1343002.080000  C1147517658        0.000000        0.000000        1   \n",
       "3760288   536624.410000  C1100697970        0.000000        0.000000        1   \n",
       "5563713  4892193.090000   C891140444        0.000000        0.000000        1   \n",
       "5996407 19585040.370000  C1392803603        0.000000        0.000000        1   \n",
       "5996409 19585040.370000  C1109166882        0.000000        0.000000        1   \n",
       "6168499  3576297.100000   C484597480        0.000000        0.000000        1   \n",
       "6205439   353874.220000  C1770418982        0.000000        0.000000        1   \n",
       "6266413  2542664.270000   C661958277        0.000000        0.000000        1   \n",
       "6281482 10399045.080000  C1806199534        0.000000        0.000000        1   \n",
       "6281484 10399045.080000  C1909486199        0.000000        0.000000        1   \n",
       "6296014  3441041.460000  C1082139865        0.000000        0.000000        1   \n",
       "6351225  3171085.590000  C1308068787        0.000000        0.000000        1   \n",
       "6362460 17316255.050000  C1395467927        0.000000        0.000000        1   \n",
       "6362462 17316255.050000  C1861208726        0.000000        0.000000        1   \n",
       "6362584  5674547.890000  C1366804249        0.000000        0.000000        1   \n",
       "\n",
       "         isFlaggedFraud     week  \n",
       "2736446               1 2.000000  \n",
       "3247297               1 2.000000  \n",
       "3760288               1 2.000000  \n",
       "5563713               1 3.000000  \n",
       "5996407               1 3.000000  \n",
       "5996409               1 3.000000  \n",
       "6168499               1 3.000000  \n",
       "6205439               1 4.000000  \n",
       "6266413               1 4.000000  \n",
       "6281482               1 4.000000  \n",
       "6281484               1 4.000000  \n",
       "6296014               1 4.000000  \n",
       "6351225               1 4.000000  \n",
       "6362460               1 4.000000  \n",
       "6362462               1 4.000000  \n",
       "6362584               1 4.000000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['isFlaggedFraud']==1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Findings 9: For all isFlaggedFraud==1, oldBalanceDest=newBalanceDest=0. No consistency in other variables from the snapshot above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4158"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.loc[(df['type'] == 'TRANSFER') & (df['isFlaggedFraud'] == 0) \n",
    "           & (df['oldBalanceDest'] == 0) & (df['newBalanceDest'] == 0)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Findings 10: There are other TRANSFERS where isFlaggedFraud==0 and oldBalanceDest=newBalanceDest=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count   4260171.000000\n",
       "mean        122.040104\n",
       "std        4149.277081\n",
       "min           0.000000\n",
       "25%           0.074596\n",
       "50%           0.740758\n",
       "75%           6.655752\n",
       "max     3925475.600000\n",
       "Name: amt_bal_ratio, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating feature Amount/Origination Old Balance\n",
    "df.loc[df['oldBalanceOrig']>0,'amt_bal_ratio']=df['amount']/df['oldBalanceOrig']\n",
    "df.loc[df['oldBalanceOrig']==0,'amt_bal_ratio']= np.nan\n",
    "df['amt_bal_ratio'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Max and Min Amount/Origination Old Balance ratio when isFlaggedFraud==1:\n",
      "1.0\n",
      "0.03837324263238986\n",
      "\n",
      "Max and Min Amount/Origination Old Balance ratio when isFlaggedFraud==1 and TRANSFER and oldBalanceOrig>0:\n",
      "1854642.67\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "print (\"\\nMax and Min Amount/Origination Old Balance ratio when isFlaggedFraud==1:\")\n",
    "print(df.loc[df['isFlaggedFraud'] == 1,'amt_bal_ratio'].max())\n",
    "print(df.loc[df['isFlaggedFraud'] == 1,'amt_bal_ratio'].min())\n",
    "print (\"\\nMax and Min Amount/Origination Old Balance ratio when isFlaggedFraud==1 and TRANSFER and oldBalanceOrig>0:\")\n",
    "print(df.loc[(df['isFlaggedFraud'] == 0) & (df['type'] == 'TRANSFER') \n",
    "             & (df['oldBalanceOrig']>0),'amt_bal_ratio'].max())\n",
    "print(round(df.loc[(df['isFlaggedFraud'] == 0) & (df['type'] == 'TRANSFER') \n",
    "             & (df['oldBalanceOrig']>0),'amt_bal_ratio'].min()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Findings11: No particular threshold for amt_bal_ratio when isFlaggedFraud==1. Range overlaps when isFlaggedFraud==0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2151495\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "print(len(df.loc[df['nameDest'].str[0]=='M']))\n",
    "print(len(df.loc[df['nameOrig'].str[0]=='M']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Findings 12: Merchants are only in Destination Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PAYMENT    2151495\n",
      "Name: type, dtype: int64\n",
      "0    2151495\n",
      "Name: isFraud, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.loc[df['nameDest'].str[0]=='M','type'].value_counts())\n",
    "print(df.loc[df['nameDest'].str[0]=='M','isFraud'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Findings 13: Only PAYMENT is done towards Merchants and no Fraud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C    6362620\n",
      "Name: nameOrig, dtype: int64\n",
      "C    4211125\n",
      "M    2151495\n",
      "Name: nameDest, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df['nameOrig'].str[0].value_counts())\n",
    "print(df['nameDest'].str[0].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Findings14: Only Customer in Transaction Origin and Customer and Merchant in Transaction Destination. All Frauds is happening in Transactions between Customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>nameOrig</th>\n",
       "      <th>oldBalanceOrig</th>\n",
       "      <th>newBalanceOrig</th>\n",
       "      <th>nameDest</th>\n",
       "      <th>oldBalanceDest</th>\n",
       "      <th>newBalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>isFlaggedFraud</th>\n",
       "      <th>week</th>\n",
       "      <th>amt_bal_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>4024.360000</td>\n",
       "      <td>C1265012928</td>\n",
       "      <td>2671.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>M1176932104</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>DEBIT</td>\n",
       "      <td>9644.940000</td>\n",
       "      <td>C1900366749</td>\n",
       "      <td>4465.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C997608398</td>\n",
       "      <td>10845.000000</td>\n",
       "      <td>157982.120000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.160121</td>\n",
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       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>11633.760000</td>\n",
       "      <td>C1716932897</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>M801569151</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.148786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>229133.940000</td>\n",
       "      <td>C905080434</td>\n",
       "      <td>15325.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C476402209</td>\n",
       "      <td>5083.000000</td>\n",
       "      <td>51513.440000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.951644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>1563.820000</td>\n",
       "      <td>C761750706</td>\n",
       "      <td>450.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>M1731217984</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.475156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>215310.300000</td>\n",
       "      <td>C1670993182</td>\n",
       "      <td>705.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C1100439041</td>\n",
       "      <td>22425.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>305.404681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>311685.890000</td>\n",
       "      <td>C1984094095</td>\n",
       "      <td>10835.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C932583850</td>\n",
       "      <td>6267.000000</td>\n",
       "      <td>2719172.890000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>28.766580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>6061.130000</td>\n",
       "      <td>C1043358826</td>\n",
       "      <td>443.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>M1558079303</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.682009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>8901.990000</td>\n",
       "      <td>C1632497828</td>\n",
       "      <td>2958.910000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>M33419717</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.008537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>8603.420000</td>\n",
       "      <td>C1608633989</td>\n",
       "      <td>253.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>M1615617512</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>34.005613</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    step      type        amount     nameOrig  oldBalanceOrig  newBalanceOrig  \\\n",
       "8      1   PAYMENT   4024.360000  C1265012928     2671.000000        0.000000   \n",
       "10     1     DEBIT   9644.940000  C1900366749     4465.000000        0.000000   \n",
       "13     1   PAYMENT  11633.760000  C1716932897    10127.000000        0.000000   \n",
       "15     1  CASH_OUT 229133.940000   C905080434    15325.000000        0.000000   \n",
       "16     1   PAYMENT   1563.820000   C761750706      450.000000        0.000000   \n",
       "19     1  TRANSFER 215310.300000  C1670993182      705.000000        0.000000   \n",
       "24     1  TRANSFER 311685.890000  C1984094095    10835.000000        0.000000   \n",
       "25     1   PAYMENT   6061.130000  C1043358826      443.000000        0.000000   \n",
       "28     1   PAYMENT   8901.990000  C1632497828     2958.910000        0.000000   \n",
       "36     1   PAYMENT   8603.420000  C1608633989      253.000000        0.000000   \n",
       "\n",
       "       nameDest  oldBalanceDest  newBalanceDest  isFraud  isFlaggedFraud  \\\n",
       "8   M1176932104        0.000000        0.000000        0               0   \n",
       "10   C997608398    10845.000000   157982.120000        0               0   \n",
       "13   M801569151        0.000000        0.000000        0               0   \n",
       "15   C476402209     5083.000000    51513.440000        0               0   \n",
       "16  M1731217984        0.000000        0.000000        0               0   \n",
       "19  C1100439041    22425.000000        0.000000        0               0   \n",
       "24   C932583850     6267.000000  2719172.890000        0               0   \n",
       "25  M1558079303        0.000000        0.000000        0               0   \n",
       "28    M33419717        0.000000        0.000000        0               0   \n",
       "36  M1615617512        0.000000        0.000000        0               0   \n",
       "\n",
       "       week  amt_bal_ratio  \n",
       "8  1.000000       1.506687  \n",
       "10 1.000000       2.160121  \n",
       "13 1.000000       1.148786  \n",
       "15 1.000000      14.951644  \n",
       "16 1.000000       3.475156  \n",
       "19 1.000000     305.404681  \n",
       "24 1.000000      28.766580  \n",
       "25 1.000000      13.682009  \n",
       "28 1.000000       3.008537  \n",
       "36 1.000000      34.005613  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['amt_bal_ratio']>1].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count   1976647.000000\n",
       "mean      56032.342897\n",
       "std      125606.619465\n",
       "min           0.000000\n",
       "25%           0.000000\n",
       "50%           0.000000\n",
       "75%           0.000000\n",
       "max     1753988.800000\n",
       "Name: newBalanceOrig, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['amt_bal_ratio']>1]['newBalanceOrig'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "# Transactions where Amount > Old Balance Origin with New Balance Origin==0\n",
      "1512573\n",
      "\n",
      "# Transactions where Amount > Old Balance Origin with New Balance Origin not equal to 0\n",
      "464074\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n# Transactions where Amount > Old Balance Origin with New Balance Origin==0\")\n",
    "print(df.loc[(df['amt_bal_ratio']>1)&(df['newBalanceOrig']==0),'step'].count())\n",
    "print(\"\\n# Transactions where Amount > Old Balance Origin with New Balance Origin not equal to 0\")\n",
    "print(df.loc[(df['amt_bal_ratio']>1)&(df['newBalanceOrig']!=0),'step'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "# Type of Transaction Didtribution where Amount > Old Balance Origin with New Balance Origin==0\n",
      "CASH_OUT    955337\n",
      "PAYMENT     326879\n",
      "TRANSFER    224729\n",
      "DEBIT         5628\n",
      "Name: type, dtype: int64\n",
      "\n",
      "# Type of Transaction Didtribution where Amount > Old Balance Origin with New Balance Origin not equal to 0\n",
      "CASH_IN     464072\n",
      "CASH_OUT         2\n",
      "Name: type, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n# Type of Transaction Didtribution where Amount > Old Balance Origin with New Balance Origin==0\")\n",
    "print(df.loc[(df['amt_bal_ratio']>1)&(df['newBalanceOrig']==0),'type'].value_counts())\n",
    "print(\"\\n# Type of Transaction Didtribution where Amount > Old Balance Origin with New Balance Origin not equal to 0\")\n",
    "print(df.loc[(df['amt_bal_ratio']>1)&(df['newBalanceOrig']!=0),'type'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Findings 15: New Balance Orig is not equal to zero when Amount > Old Balance Origin due to CASH IN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>nameOrig</th>\n",
       "      <th>oldBalanceOrig</th>\n",
       "      <th>newBalanceOrig</th>\n",
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       "      <td>34076</td>\n",
       "      <td>8</td>\n",
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       "      <td>34779</td>\n",
       "      <td>8</td>\n",
       "      <td>CASH_OUT</td>\n",
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       "      <td>106659.840000</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       step      type        amount    nameOrig  oldBalanceOrig  \\\n",
       "34076     8  CASH_OUT  88553.310000  C319673957    67106.460000   \n",
       "34779     8  CASH_OUT 106659.840000  C638668928    80084.580000   \n",
       "\n",
       "       newBalanceOrig    nameDest  oldBalanceDest  newBalanceDest  isFraud  \\\n",
       "34076    88553.310000  C932634391    67106.460000        0.000000        0   \n",
       "34779   106659.840000  C224342296    80084.580000    92322.890000        0   \n",
       "\n",
       "       isFlaggedFraud     week  amt_bal_ratio  \n",
       "34076               0 1.000000       1.319594  \n",
       "34779               0 1.000000       1.331840  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore the 2 Cash Out Cases\n",
    "df.loc[(df['amt_bal_ratio']>1)&(df['newBalanceOrig']!=0)&(df['type']=='CASH_OUT')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.331840</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       step      type        amount    nameOrig  oldBalanceOrig  \\\n",
       "34076     8  CASH_OUT  88553.310000  C319673957    67106.460000   \n",
       "34779     8  CASH_OUT 106659.840000  C638668928    80084.580000   \n",
       "\n",
       "       newBalanceOrig    nameDest  oldBalanceDest  newBalanceDest  isFraud  \\\n",
       "34076    88553.310000  C932634391    67106.460000        0.000000        0   \n",
       "34779   106659.840000  C224342296    80084.580000    92322.890000        0   \n",
       "\n",
       "       isFlaggedFraud     week  amt_bal_ratio  \n",
       "34076               0 1.000000       1.319594  \n",
       "34779               0 1.000000       1.331840  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['nameOrig'].isin(['C319673957','C638668928'])].sort_values(by=['step'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>step</th>\n",
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       "      <th>amount</th>\n",
       "      <th>nameOrig</th>\n",
       "      <th>oldBalanceOrig</th>\n",
       "      <th>newBalanceOrig</th>\n",
       "      <th>nameDest</th>\n",
       "      <th>oldBalanceDest</th>\n",
       "      <th>newBalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>isFlaggedFraud</th>\n",
       "      <th>week</th>\n",
       "      <th>amt_bal_ratio</th>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [step, type, amount, nameOrig, oldBalanceOrig, newBalanceOrig, nameDest, oldBalanceDest, newBalanceDest, isFraud, isFlaggedFraud, week, amt_bal_ratio]\n",
       "Index: []"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['nameDest'].isin(['C319673957','C638668928'])].sort_values(by=['step'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Findings 16: New Balance Orig is not equal to zero and Amount > Old Balance Origin and type==CASH OUT data does not tally with Balance movements. There is not corresponding CASH IN or Name in Destination for the corresponding Names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count   464072.000000\n",
       "mean         0.000002\n",
       "std          0.001131\n",
       "min         -0.010000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          0.000000\n",
       "max          0.010000\n",
       "Name: diffBalanceOrig, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['diffBalanceOrig']=df['oldBalanceOrig']+df['amount']-df['newBalanceOrig']\n",
    "df.loc[(df['amt_bal_ratio']>1)&(df['newBalanceOrig']!=0)&(df['type']=='CASH_IN'),'diffBalanceOrig'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count   1399284.000000\n",
       "mean          5.038225\n",
       "std        1319.894982\n",
       "min          -0.010000\n",
       "25%           0.000000\n",
       "50%           0.000000\n",
       "75%           0.000000\n",
       "max      770537.370000\n",
       "Name: diffBalanceOrig, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[(df['type']=='CASH_IN'),'diffBalanceOrig'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>nameOrig</th>\n",
       "      <th>oldBalanceOrig</th>\n",
       "      <th>newBalanceOrig</th>\n",
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       "  <tbody>\n",
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       "      <td>456</td>\n",
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       "      <td>110226.340000</td>\n",
       "      <td>C1475192960</td>\n",
       "      <td>1601450.630000</td>\n",
       "      <td>1601450.630000</td>\n",
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       "    <tr>\n",
       "      <td>656</td>\n",
       "      <td>1</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>770537.370000</td>\n",
       "      <td>C2015999862</td>\n",
       "      <td>8499043.130000</td>\n",
       "      <td>8499043.130000</td>\n",
       "      <td>C1883840933</td>\n",
       "      <td>8499043.130000</td>\n",
       "      <td>16874643.090000</td>\n",
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       "    <tr>\n",
       "      <td>1292</td>\n",
       "      <td>1</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>61505.910000</td>\n",
       "      <td>C1790387225</td>\n",
       "      <td>4910403.440000</td>\n",
       "      <td>4910403.440000</td>\n",
       "      <td>C11003494</td>\n",
       "      <td>4910403.440000</td>\n",
       "      <td>10588885.270000</td>\n",
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       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.012526</td>\n",
       "      <td>61505.910000</td>\n",
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       "    <tr>\n",
       "      <td>1293</td>\n",
       "      <td>1</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>404167.600000</td>\n",
       "      <td>C984412970</td>\n",
       "      <td>4910403.440000</td>\n",
       "      <td>4910403.440000</td>\n",
       "      <td>C11003494</td>\n",
       "      <td>4910403.440000</td>\n",
       "      <td>10588885.270000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.082308</td>\n",
       "      <td>404167.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2129</td>\n",
       "      <td>1</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>42012.450000</td>\n",
       "      <td>C1137667747</td>\n",
       "      <td>350501.240000</td>\n",
       "      <td>350501.240000</td>\n",
       "      <td>C667346055</td>\n",
       "      <td>350501.240000</td>\n",
       "      <td>9291619.620000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.119864</td>\n",
       "      <td>42012.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2246</td>\n",
       "      <td>1</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>187256.620000</td>\n",
       "      <td>C1106468732</td>\n",
       "      <td>521018.570000</td>\n",
       "      <td>521018.570000</td>\n",
       "      <td>C662736689</td>\n",
       "      <td>521018.570000</td>\n",
       "      <td>4891090.560000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.359405</td>\n",
       "      <td>187256.620000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2678</td>\n",
       "      <td>1</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>208664.250000</td>\n",
       "      <td>C1216611732</td>\n",
       "      <td>4800759.180000</td>\n",
       "      <td>4800759.180000</td>\n",
       "      <td>C1870252780</td>\n",
       "      <td>4800759.180000</td>\n",
       "      <td>7416184.790000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.043465</td>\n",
       "      <td>208664.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2694</td>\n",
       "      <td>1</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>301755.980000</td>\n",
       "      <td>C1544031987</td>\n",
       "      <td>6509377.510000</td>\n",
       "      <td>6509377.510000</td>\n",
       "      <td>C1870252780</td>\n",
       "      <td>6509377.510000</td>\n",
       "      <td>7416184.790000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.046357</td>\n",
       "      <td>301755.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2751</td>\n",
       "      <td>2</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>305762.250000</td>\n",
       "      <td>C690051320</td>\n",
       "      <td>1957713.690000</td>\n",
       "      <td>1957713.690000</td>\n",
       "      <td>C1778801068</td>\n",
       "      <td>1957713.690000</td>\n",
       "      <td>8813776.610000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.156183</td>\n",
       "      <td>305762.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2923</td>\n",
       "      <td>2</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>275712.540000</td>\n",
       "      <td>C1378612814</td>\n",
       "      <td>6269058.270000</td>\n",
       "      <td>6269058.270000</td>\n",
       "      <td>C1122118227</td>\n",
       "      <td>6269058.270000</td>\n",
       "      <td>11073556.180000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.043980</td>\n",
       "      <td>275712.540000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2950</td>\n",
       "      <td>2</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>31876.770000</td>\n",
       "      <td>C1517105378</td>\n",
       "      <td>9506081.110000</td>\n",
       "      <td>9506081.110000</td>\n",
       "      <td>C1122118227</td>\n",
       "      <td>9506081.110000</td>\n",
       "      <td>11073556.180000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003353</td>\n",
       "      <td>31876.770000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3672</td>\n",
       "      <td>2</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>188118.260000</td>\n",
       "      <td>C1963590892</td>\n",
       "      <td>11126712.230000</td>\n",
       "      <td>11126712.230000</td>\n",
       "      <td>C769342184</td>\n",
       "      <td>11126712.230000</td>\n",
       "      <td>12458650.930000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.016907</td>\n",
       "      <td>188118.260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3745</td>\n",
       "      <td>3</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>148620.650000</td>\n",
       "      <td>C1974701110</td>\n",
       "      <td>1989555.830000</td>\n",
       "      <td>1989555.830000</td>\n",
       "      <td>C1446521801</td>\n",
       "      <td>1989555.830000</td>\n",
       "      <td>6540346.280000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.074700</td>\n",
       "      <td>148620.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3955</td>\n",
       "      <td>3</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>440757.720000</td>\n",
       "      <td>C903218049</td>\n",
       "      <td>6533762.110000</td>\n",
       "      <td>6533762.110000</td>\n",
       "      <td>C1674899618</td>\n",
       "      <td>6533762.110000</td>\n",
       "      <td>7074754.630000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.067458</td>\n",
       "      <td>440757.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4026</td>\n",
       "      <td>3</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>79516.070000</td>\n",
       "      <td>C2084634607</td>\n",
       "      <td>4697384.290000</td>\n",
       "      <td>4697384.290000</td>\n",
       "      <td>C1255024717</td>\n",
       "      <td>4697384.290000</td>\n",
       "      <td>6349066.600000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.016928</td>\n",
       "      <td>79516.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4322</td>\n",
       "      <td>4</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>221970.490000</td>\n",
       "      <td>C358636931</td>\n",
       "      <td>2983939.000000</td>\n",
       "      <td>2983939.000000</td>\n",
       "      <td>C728526866</td>\n",
       "      <td>2983939.000000</td>\n",
       "      <td>6671931.330000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.074388</td>\n",
       "      <td>221970.490000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4474</td>\n",
       "      <td>4</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>81300.200000</td>\n",
       "      <td>C870627780</td>\n",
       "      <td>2143395.300000</td>\n",
       "      <td>2143395.300000</td>\n",
       "      <td>C214774813</td>\n",
       "      <td>2143395.300000</td>\n",
       "      <td>13381242.460000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.037931</td>\n",
       "      <td>81300.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4480</td>\n",
       "      <td>4</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>38606.170000</td>\n",
       "      <td>C1174599796</td>\n",
       "      <td>2543476.080000</td>\n",
       "      <td>2543476.080000</td>\n",
       "      <td>C214774813</td>\n",
       "      <td>2543476.080000</td>\n",
       "      <td>13381242.460000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.015179</td>\n",
       "      <td>38606.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5680</td>\n",
       "      <td>6</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>120910.430000</td>\n",
       "      <td>C604405894</td>\n",
       "      <td>2833408.480000</td>\n",
       "      <td>2833408.480000</td>\n",
       "      <td>C1636786811</td>\n",
       "      <td>2833408.480000</td>\n",
       "      <td>6703099.060000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.042673</td>\n",
       "      <td>120910.430000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5807</td>\n",
       "      <td>6</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>192005.110000</td>\n",
       "      <td>C806566417</td>\n",
       "      <td>7553719.760000</td>\n",
       "      <td>7553719.760000</td>\n",
       "      <td>C1880332929</td>\n",
       "      <td>7553719.760000</td>\n",
       "      <td>11423976.980000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.025419</td>\n",
       "      <td>192005.110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16572</td>\n",
       "      <td>8</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>179369.980000</td>\n",
       "      <td>C1688871501</td>\n",
       "      <td>4752059.840000</td>\n",
       "      <td>4752059.840000</td>\n",
       "      <td>C68113386</td>\n",
       "      <td>4752059.840000</td>\n",
       "      <td>12099108.350000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.037746</td>\n",
       "      <td>179369.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19057</td>\n",
       "      <td>8</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>175007.100000</td>\n",
       "      <td>C1656993994</td>\n",
       "      <td>12587653.430000</td>\n",
       "      <td>12587653.430000</td>\n",
       "      <td>C2042633527</td>\n",
       "      <td>12587653.430000</td>\n",
       "      <td>22590474.830000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013903</td>\n",
       "      <td>175007.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21080</td>\n",
       "      <td>8</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>285894.570000</td>\n",
       "      <td>C138466543</td>\n",
       "      <td>8023577.180000</td>\n",
       "      <td>8023577.180000</td>\n",
       "      <td>C1751073738</td>\n",
       "      <td>8023577.180000</td>\n",
       "      <td>8875838.690000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.035632</td>\n",
       "      <td>285894.570000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22109</td>\n",
       "      <td>8</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>158077.380000</td>\n",
       "      <td>C115341006</td>\n",
       "      <td>2156386.800000</td>\n",
       "      <td>2156386.800000</td>\n",
       "      <td>C1948558238</td>\n",
       "      <td>2156386.800000</td>\n",
       "      <td>11604362.040000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.073307</td>\n",
       "      <td>158077.380000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24388</td>\n",
       "      <td>8</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>221425.160000</td>\n",
       "      <td>C1455087188</td>\n",
       "      <td>3886456.770000</td>\n",
       "      <td>3886456.770000</td>\n",
       "      <td>C897209100</td>\n",
       "      <td>3886456.770000</td>\n",
       "      <td>5188807.880000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.056974</td>\n",
       "      <td>221425.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53669</td>\n",
       "      <td>9</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>469893.880000</td>\n",
       "      <td>C971451724</td>\n",
       "      <td>704858.480000</td>\n",
       "      <td>704858.480000</td>\n",
       "      <td>C373776370</td>\n",
       "      <td>704858.480000</td>\n",
       "      <td>1310506.280000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.666650</td>\n",
       "      <td>469893.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67883</td>\n",
       "      <td>9</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>116403.400000</td>\n",
       "      <td>C1947259890</td>\n",
       "      <td>17655792.460000</td>\n",
       "      <td>17655792.460000</td>\n",
       "      <td>C1743362143</td>\n",
       "      <td>17655792.460000</td>\n",
       "      <td>24088579.790000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.006593</td>\n",
       "      <td>116403.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>230377</td>\n",
       "      <td>14</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>179492.840000</td>\n",
       "      <td>C1072800963</td>\n",
       "      <td>3471370.990000</td>\n",
       "      <td>3471370.990000</td>\n",
       "      <td>C644727351</td>\n",
       "      <td>3471370.990000</td>\n",
       "      <td>3521295.230000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.051707</td>\n",
       "      <td>179492.840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>699857</td>\n",
       "      <td>37</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>84211.100000</td>\n",
       "      <td>C523212228</td>\n",
       "      <td>2904239.420000</td>\n",
       "      <td>2904239.420000</td>\n",
       "      <td>C411362965</td>\n",
       "      <td>2904239.420000</td>\n",
       "      <td>7542898.250000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.028996</td>\n",
       "      <td>84211.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>749851</td>\n",
       "      <td>38</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>171173.160000</td>\n",
       "      <td>C917390417</td>\n",
       "      <td>1224105.950000</td>\n",
       "      <td>1224105.950000</td>\n",
       "      <td>C263358702</td>\n",
       "      <td>1224105.950000</td>\n",
       "      <td>10926387.080000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.139835</td>\n",
       "      <td>171173.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1292718</td>\n",
       "      <td>136</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>650332.480000</td>\n",
       "      <td>C535447547</td>\n",
       "      <td>1810252.060000</td>\n",
       "      <td>1810252.060000</td>\n",
       "      <td>C295939724</td>\n",
       "      <td>1810252.060000</td>\n",
       "      <td>7000184.090000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.359250</td>\n",
       "      <td>650332.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1723221</td>\n",
       "      <td>160</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>90562.510000</td>\n",
       "      <td>C40949584</td>\n",
       "      <td>3910752.160000</td>\n",
       "      <td>3910752.160000</td>\n",
       "      <td>C1395252683</td>\n",
       "      <td>3910752.160000</td>\n",
       "      <td>8314241.640000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.023157</td>\n",
       "      <td>90562.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1817355</td>\n",
       "      <td>163</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>56778.210000</td>\n",
       "      <td>C230086251</td>\n",
       "      <td>2348169.530000</td>\n",
       "      <td>2348169.530000</td>\n",
       "      <td>C909042673</td>\n",
       "      <td>2348169.530000</td>\n",
       "      <td>29156189.540000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.024180</td>\n",
       "      <td>56778.210000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         step     type        amount     nameOrig  oldBalanceOrig  \\\n",
       "456         1  CASH_IN 110226.340000  C1475192960  1601450.630000   \n",
       "656         1  CASH_IN 770537.370000  C2015999862  8499043.130000   \n",
       "1292        1  CASH_IN  61505.910000  C1790387225  4910403.440000   \n",
       "1293        1  CASH_IN 404167.600000   C984412970  4910403.440000   \n",
       "2129        1  CASH_IN  42012.450000  C1137667747   350501.240000   \n",
       "2246        1  CASH_IN 187256.620000  C1106468732   521018.570000   \n",
       "2678        1  CASH_IN 208664.250000  C1216611732  4800759.180000   \n",
       "2694        1  CASH_IN 301755.980000  C1544031987  6509377.510000   \n",
       "2751        2  CASH_IN 305762.250000   C690051320  1957713.690000   \n",
       "2923        2  CASH_IN 275712.540000  C1378612814  6269058.270000   \n",
       "2950        2  CASH_IN  31876.770000  C1517105378  9506081.110000   \n",
       "3672        2  CASH_IN 188118.260000  C1963590892 11126712.230000   \n",
       "3745        3  CASH_IN 148620.650000  C1974701110  1989555.830000   \n",
       "3955        3  CASH_IN 440757.720000   C903218049  6533762.110000   \n",
       "4026        3  CASH_IN  79516.070000  C2084634607  4697384.290000   \n",
       "4322        4  CASH_IN 221970.490000   C358636931  2983939.000000   \n",
       "4474        4  CASH_IN  81300.200000   C870627780  2143395.300000   \n",
       "4480        4  CASH_IN  38606.170000  C1174599796  2543476.080000   \n",
       "5680        6  CASH_IN 120910.430000   C604405894  2833408.480000   \n",
       "5807        6  CASH_IN 192005.110000   C806566417  7553719.760000   \n",
       "16572       8  CASH_IN 179369.980000  C1688871501  4752059.840000   \n",
       "19057       8  CASH_IN 175007.100000  C1656993994 12587653.430000   \n",
       "21080       8  CASH_IN 285894.570000   C138466543  8023577.180000   \n",
       "22109       8  CASH_IN 158077.380000   C115341006  2156386.800000   \n",
       "24388       8  CASH_IN 221425.160000  C1455087188  3886456.770000   \n",
       "53669       9  CASH_IN 469893.880000   C971451724   704858.480000   \n",
       "67883       9  CASH_IN 116403.400000  C1947259890 17655792.460000   \n",
       "230377     14  CASH_IN 179492.840000  C1072800963  3471370.990000   \n",
       "699857     37  CASH_IN  84211.100000   C523212228  2904239.420000   \n",
       "749851     38  CASH_IN 171173.160000   C917390417  1224105.950000   \n",
       "1292718   136  CASH_IN 650332.480000   C535447547  1810252.060000   \n",
       "1723221   160  CASH_IN  90562.510000    C40949584  3910752.160000   \n",
       "1817355   163  CASH_IN  56778.210000   C230086251  2348169.530000   \n",
       "\n",
       "         newBalanceOrig     nameDest  oldBalanceDest  newBalanceDest  isFraud  \\\n",
       "456      1601450.630000  C1816757085  1601450.630000 10681238.790000        0   \n",
       "656      8499043.130000  C1883840933  8499043.130000 16874643.090000        0   \n",
       "1292     4910403.440000    C11003494  4910403.440000 10588885.270000        0   \n",
       "1293     4910403.440000    C11003494  4910403.440000 10588885.270000        0   \n",
       "2129      350501.240000   C667346055   350501.240000  9291619.620000        0   \n",
       "2246      521018.570000   C662736689   521018.570000  4891090.560000        0   \n",
       "2678     4800759.180000  C1870252780  4800759.180000  7416184.790000        0   \n",
       "2694     6509377.510000  C1870252780  6509377.510000  7416184.790000        0   \n",
       "2751     1957713.690000  C1778801068  1957713.690000  8813776.610000        0   \n",
       "2923     6269058.270000  C1122118227  6269058.270000 11073556.180000        0   \n",
       "2950     9506081.110000  C1122118227  9506081.110000 11073556.180000        0   \n",
       "3672    11126712.230000   C769342184 11126712.230000 12458650.930000        0   \n",
       "3745     1989555.830000  C1446521801  1989555.830000  6540346.280000        0   \n",
       "3955     6533762.110000  C1674899618  6533762.110000  7074754.630000        0   \n",
       "4026     4697384.290000  C1255024717  4697384.290000  6349066.600000        0   \n",
       "4322     2983939.000000   C728526866  2983939.000000  6671931.330000        0   \n",
       "4474     2143395.300000   C214774813  2143395.300000 13381242.460000        0   \n",
       "4480     2543476.080000   C214774813  2543476.080000 13381242.460000        0   \n",
       "5680     2833408.480000  C1636786811  2833408.480000  6703099.060000        0   \n",
       "5807     7553719.760000  C1880332929  7553719.760000 11423976.980000        0   \n",
       "16572    4752059.840000    C68113386  4752059.840000 12099108.350000        0   \n",
       "19057   12587653.430000  C2042633527 12587653.430000 22590474.830000        0   \n",
       "21080    8023577.180000  C1751073738  8023577.180000  8875838.690000        0   \n",
       "22109    2156386.800000  C1948558238  2156386.800000 11604362.040000        0   \n",
       "24388    3886456.770000   C897209100  3886456.770000  5188807.880000        0   \n",
       "53669     704858.480000   C373776370   704858.480000  1310506.280000        0   \n",
       "67883   17655792.460000  C1743362143 17655792.460000 24088579.790000        0   \n",
       "230377   3471370.990000   C644727351  3471370.990000  3521295.230000        0   \n",
       "699857   2904239.420000   C411362965  2904239.420000  7542898.250000        0   \n",
       "749851   1224105.950000   C263358702  1224105.950000 10926387.080000        0   \n",
       "1292718  1810252.060000   C295939724  1810252.060000  7000184.090000        0   \n",
       "1723221  3910752.160000  C1395252683  3910752.160000  8314241.640000        0   \n",
       "1817355  2348169.530000   C909042673  2348169.530000 29156189.540000        0   \n",
       "\n",
       "         isFlaggedFraud     week  amt_bal_ratio  diffBalanceOrig  \n",
       "456                   0 1.000000       0.068829    110226.340000  \n",
       "656                   0 1.000000       0.090662    770537.370000  \n",
       "1292                  0 1.000000       0.012526     61505.910000  \n",
       "1293                  0 1.000000       0.082308    404167.600000  \n",
       "2129                  0 1.000000       0.119864     42012.450000  \n",
       "2246                  0 1.000000       0.359405    187256.620000  \n",
       "2678                  0 1.000000       0.043465    208664.250000  \n",
       "2694                  0 1.000000       0.046357    301755.980000  \n",
       "2751                  0 1.000000       0.156183    305762.250000  \n",
       "2923                  0 1.000000       0.043980    275712.540000  \n",
       "2950                  0 1.000000       0.003353     31876.770000  \n",
       "3672                  0 1.000000       0.016907    188118.260000  \n",
       "3745                  0 1.000000       0.074700    148620.650000  \n",
       "3955                  0 1.000000       0.067458    440757.720000  \n",
       "4026                  0 1.000000       0.016928     79516.070000  \n",
       "4322                  0 1.000000       0.074388    221970.490000  \n",
       "4474                  0 1.000000       0.037931     81300.200000  \n",
       "4480                  0 1.000000       0.015179     38606.170000  \n",
       "5680                  0 1.000000       0.042673    120910.430000  \n",
       "5807                  0 1.000000       0.025419    192005.110000  \n",
       "16572                 0 1.000000       0.037746    179369.980000  \n",
       "19057                 0 1.000000       0.013903    175007.100000  \n",
       "21080                 0 1.000000       0.035632    285894.570000  \n",
       "22109                 0 1.000000       0.073307    158077.380000  \n",
       "24388                 0 1.000000       0.056974    221425.160000  \n",
       "53669                 0 1.000000       0.666650    469893.880000  \n",
       "67883                 0 1.000000       0.006593    116403.400000  \n",
       "230377                0 1.000000       0.051707    179492.840000  \n",
       "699857                0 1.000000       0.028996     84211.100000  \n",
       "749851                0 1.000000       0.139835    171173.160000  \n",
       "1292718               0 1.000000       0.359250    650332.480000  \n",
       "1723221               0 1.000000       0.023157     90562.510000  \n",
       "1817355               0 1.000000       0.024180     56778.210000  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[(df['type']=='CASH_IN')&(df['diffBalanceOrig']>=1),:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Findings 17: For CASH IN Transactions majority cases NEW Balance Origin - OLD Balance Origin == Amount. For the cases its doesnt hold true, NEW Balance Origin == OLD Balance Origin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nameDest</th>\n",
       "      <th>Trans_Num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>4244</td>\n",
       "      <td>C2020337583</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6699</td>\n",
       "      <td>C650699445</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5937</td>\n",
       "      <td>C475338087</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6065</td>\n",
       "      <td>C505532836</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>759</td>\n",
       "      <td>C1185292292</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         nameDest  Trans_Num\n",
       "4244  C2020337583          2\n",
       "6699   C650699445          2\n",
       "5937   C475338087          2\n",
       "6065   C505532836          2\n",
       "759   C1185292292          2"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check Fraud nameDest\n",
    "df_fraudcust_cnt=df.loc[df['isFraud']==1,['nameDest','isFraud']].groupby('nameDest',as_index=False).sum()\n",
    "df_fraudcust_cnt=df_fraudcust_cnt.rename(columns={'isFraud':'Trans_Num'})\n",
    "df_fraudcust_cnt=df_fraudcust_cnt.sort_values(by=['Trans_Num'],ascending=False)\n",
    "df_fraudcust_cnt.head()\n",
    "#df_fraudcust_cnt[df_fraudcust_cnt['Trans_Num']>1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <th>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>nameOrig</th>\n",
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       "      <th>newBalanceOrig</th>\n",
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       "      <th>amt_bal_ratio</th>\n",
       "      <th>diffBalanceOrig</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>49826</td>\n",
       "      <td>9</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>2539898.070000</td>\n",
       "      <td>C728718059</td>\n",
       "      <td>2539898.070000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C1013511446</td>\n",
       "      <td>968.000000</td>\n",
       "      <td>1842864.220000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5079796.140000</td>\n",
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       "    <tr>\n",
       "      <td>67782</td>\n",
       "      <td>9</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>594209.730000</td>\n",
       "      <td>C1888557649</td>\n",
       "      <td>3151936.520000</td>\n",
       "      <td>3746146.250000</td>\n",
       "      <td>C1013511446</td>\n",
       "      <td>2540866.070000</td>\n",
       "      <td>1842864.220000</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.188522</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <td>83389</td>\n",
       "      <td>10</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>103792.120000</td>\n",
       "      <td>C792601931</td>\n",
       "      <td>42696.840000</td>\n",
       "      <td>146488.970000</td>\n",
       "      <td>C1013511446</td>\n",
       "      <td>1946656.340000</td>\n",
       "      <td>1842864.220000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.430909</td>\n",
       "      <td>-0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146724</td>\n",
       "      <td>12</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>398361.070000</td>\n",
       "      <td>C1402841545</td>\n",
       "      <td>8287.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C1013511446</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2006826.390000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>48.070601</td>\n",
       "      <td>406648.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>147414</td>\n",
       "      <td>12</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>1608465.320000</td>\n",
       "      <td>C657294376</td>\n",
       "      <td>10678.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C1013511446</td>\n",
       "      <td>398361.070000</td>\n",
       "      <td>2006826.390000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>150.633576</td>\n",
       "      <td>1619143.320000</td>\n",
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       "    <tr>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>4473849</td>\n",
       "      <td>324</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>529863.480000</td>\n",
       "      <td>C924297599</td>\n",
       "      <td>4813001.110000</td>\n",
       "      <td>5342864.590000</td>\n",
       "      <td>C967226405</td>\n",
       "      <td>2903277.850000</td>\n",
       "      <td>2373414.370000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.110090</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4621225</td>\n",
       "      <td>329</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>116391.330000</td>\n",
       "      <td>C1882477120</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C967226405</td>\n",
       "      <td>2373414.370000</td>\n",
       "      <td>2489805.700000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>116391.330000</td>\n",
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       "    <tr>\n",
       "      <td>5496752</td>\n",
       "      <td>380</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>34672.080000</td>\n",
       "      <td>C142316438</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C967226405</td>\n",
       "      <td>2489805.700000</td>\n",
       "      <td>2524477.780000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>34672.080000</td>\n",
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       "    <tr>\n",
       "      <td>5889515</td>\n",
       "      <td>403</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>140653.400000</td>\n",
       "      <td>C1943053207</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C967226405</td>\n",
       "      <td>2524477.780000</td>\n",
       "      <td>2665131.180000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>140653.400000</td>\n",
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       "    <tr>\n",
       "      <td>6346369</td>\n",
       "      <td>694</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>855809.290000</td>\n",
       "      <td>C624708368</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>nan</td>\n",
       "      <td>855809.290000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>839 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         step      type         amount     nameOrig  oldBalanceOrig  \\\n",
       "49826       9  CASH_OUT 2539898.070000   C728718059  2539898.070000   \n",
       "67782       9   CASH_IN  594209.730000  C1888557649  3151936.520000   \n",
       "83389      10   CASH_IN  103792.120000   C792601931    42696.840000   \n",
       "146724     12  CASH_OUT  398361.070000  C1402841545     8287.000000   \n",
       "147414     12  TRANSFER 1608465.320000   C657294376    10678.000000   \n",
       "...       ...       ...            ...          ...             ...   \n",
       "4473849   324   CASH_IN  529863.480000   C924297599  4813001.110000   \n",
       "4621225   329  CASH_OUT  116391.330000  C1882477120        0.000000   \n",
       "5496752   380  TRANSFER   34672.080000   C142316438        0.000000   \n",
       "5889515   403  TRANSFER  140653.400000  C1943053207        0.000000   \n",
       "6346369   694  TRANSFER  855809.290000   C624708368        0.000000   \n",
       "\n",
       "         newBalanceOrig     nameDest  oldBalanceDest  newBalanceDest  isFraud  \\\n",
       "49826          0.000000  C1013511446      968.000000  1842864.220000        1   \n",
       "67782    3746146.250000  C1013511446  2540866.070000  1842864.220000        0   \n",
       "83389     146488.970000  C1013511446  1946656.340000  1842864.220000        0   \n",
       "146724         0.000000  C1013511446        0.000000  2006826.390000        0   \n",
       "147414         0.000000  C1013511446   398361.070000  2006826.390000        0   \n",
       "...                 ...          ...             ...             ...      ...   \n",
       "4473849  5342864.590000   C967226405  2903277.850000  2373414.370000        0   \n",
       "4621225        0.000000   C967226405  2373414.370000  2489805.700000        0   \n",
       "5496752        0.000000   C967226405  2489805.700000  2524477.780000        0   \n",
       "5889515        0.000000   C967226405  2524477.780000  2665131.180000        0   \n",
       "6346369        0.000000   C967226405  2665131.180000  3520940.460000        0   \n",
       "\n",
       "         isFlaggedFraud     week  amt_bal_ratio  diffBalanceOrig  \n",
       "49826                 0 1.000000       1.000000   5079796.140000  \n",
       "67782                 0 1.000000       0.188522         0.000000  \n",
       "83389                 0 1.000000       2.430909        -0.010000  \n",
       "146724                0 1.000000      48.070601    406648.070000  \n",
       "147414                0 1.000000     150.633576   1619143.320000  \n",
       "...                 ...      ...            ...              ...  \n",
       "4473849               0 2.000000       0.110090         0.000000  \n",
       "4621225               0 2.000000            nan    116391.330000  \n",
       "5496752               0 3.000000            nan     34672.080000  \n",
       "5889515               0 3.000000            nan    140653.400000  \n",
       "6346369               0 4.000000            nan    855809.290000  \n",
       "\n",
       "[839 rows x 14 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "repeat_fraud_destcust = df_fraudcust_cnt[df_fraudcust_cnt['Trans_Num']>1]['nameDest'].tolist()\n",
    "df[df['nameDest'].isin(repeat_fraud_destcust)].sort_values(by=['nameDest','step'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
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       "</table>\n",
       "<p>88 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         step      type         amount     nameOrig  oldBalanceOrig  \\\n",
       "49826       9  CASH_OUT 2539898.070000   C728718059  2539898.070000   \n",
       "1030590    74  CASH_OUT  455074.130000   C695129655   455074.130000   \n",
       "788647     39  TRANSFER  152042.160000  C1118399210   152042.160000   \n",
       "1030368    59  CASH_OUT 2050727.810000   C992917413  2050727.810000   \n",
       "2122590   183  CASH_OUT  224378.100000  C1234321469   224378.100000   \n",
       "...       ...       ...            ...          ...             ...   \n",
       "1006043    46  CASH_OUT   71372.000000   C313736303    71372.000000   \n",
       "4058811   300  CASH_OUT  176059.160000  C1203474575   176059.160000   \n",
       "5801852   401  CASH_OUT 5838803.230000   C388640141  5838803.230000   \n",
       "1832574   163  CASH_OUT  762135.720000  C1088995906   762135.720000   \n",
       "3363957   254  CASH_OUT  653949.740000  C1985193615   653949.740000   \n",
       "\n",
       "         newBalanceOrig     nameDest  oldBalanceDest  newBalanceDest  isFraud  \\\n",
       "49826          0.000000  C1013511446      968.000000  1842864.220000        1   \n",
       "1030590        0.000000  C1013511446  9237170.740000  9692244.860000        1   \n",
       "788647         0.000000   C104038589        0.000000        0.000000        1   \n",
       "1030368        0.000000   C104038589        0.000000  2050727.810000        1   \n",
       "2122590        0.000000   C105660024   483419.250000   707797.360000        1   \n",
       "...                 ...          ...             ...             ...      ...   \n",
       "1006043        0.000000   C935310781   613464.040000   684836.040000        1   \n",
       "4058811        0.000000   C964377943   105783.210000   281842.360000        1   \n",
       "5801852        0.000000   C964377943   471758.210000  6310561.440000        1   \n",
       "1832574        0.000000   C967226405    47768.780000   809904.500000        1   \n",
       "3363957        0.000000   C967226405  1404475.930000  2058425.670000        1   \n",
       "\n",
       "         isFlaggedFraud     week  amt_bal_ratio  diffBalanceOrig  \n",
       "49826                 0 1.000000       1.000000   5079796.140000  \n",
       "1030590               0 1.000000       1.000000    910148.260000  \n",
       "788647                0 1.000000       1.000000    304084.320000  \n",
       "1030368               0 1.000000       1.000000   4101455.620000  \n",
       "2122590               0 1.000000       1.000000    448756.200000  \n",
       "...                 ...      ...            ...              ...  \n",
       "1006043               0 1.000000       1.000000    142744.000000  \n",
       "4058811               0 2.000000       1.000000    352118.320000  \n",
       "5801852               0 3.000000       1.000000  11677606.460000  \n",
       "1832574               0 1.000000       1.000000   1524271.440000  \n",
       "3363957               0 2.000000       1.000000   1307899.480000  \n",
       "\n",
       "[88 rows x 14 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['nameDest'].isin(repeat_fraud_destcust) & df['isFraud']==1].sort_values(by=['nameDest','step'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>step</th>\n",
       "      <th>type</th>\n",
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       "      <td>49826</td>\n",
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       "      <td>83389</td>\n",
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       "    <tr>\n",
       "      <td>147414</td>\n",
       "      <td>12</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>1608465.320000</td>\n",
       "      <td>C657294376</td>\n",
       "      <td>10678.000000</td>\n",
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       "    <tr>\n",
       "      <td>201153</td>\n",
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       "      <td>C783323484</td>\n",
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       "    <tr>\n",
       "      <td>230719</td>\n",
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       "    <tr>\n",
       "      <td>299948</td>\n",
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       "      <td>342032</td>\n",
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       "      <td>C1145539504</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>198969.470000</td>\n",
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       "    <tr>\n",
       "      <td>399494</td>\n",
       "      <td>18</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>653487.610000</td>\n",
       "      <td>C1547327732</td>\n",
       "      <td>1329013.790000</td>\n",
       "      <td>675526.180000</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <td>553854</td>\n",
       "      <td>22</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>1482744.680000</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>1482744.680000</td>\n",
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       "    <tr>\n",
       "      <td>583665</td>\n",
       "      <td>33</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>138155.180000</td>\n",
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       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>138155.180000</td>\n",
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       "    <tr>\n",
       "      <td>596281</td>\n",
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       "      <td>362779.210000</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>362779.210000</td>\n",
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       "    <tr>\n",
       "      <td>607493</td>\n",
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       "      <td>640413</td>\n",
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       "      <td>8328219.330000</td>\n",
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       "    <tr>\n",
       "      <td>658480</td>\n",
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       "      <td>1030590</td>\n",
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       "      <td>455074.130000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C1013511446</td>\n",
       "      <td>9237170.740000</td>\n",
       "      <td>9692244.860000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>910148.260000</td>\n",
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       "    <tr>\n",
       "      <td>1093395</td>\n",
       "      <td>130</td>\n",
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       "      <td>530141.560000</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>530141.560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1412569</td>\n",
       "      <td>139</td>\n",
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       "      <td>21961622.690000</td>\n",
       "      <td>22129487.380000</td>\n",
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       "      <td>10054521.730000</td>\n",
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       "      <td>0.007644</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1471037</td>\n",
       "      <td>141</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>311718.690000</td>\n",
       "      <td>C1154087140</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C1013511446</td>\n",
       "      <td>10054521.730000</td>\n",
       "      <td>10366240.420000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>311718.690000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1568249</td>\n",
       "      <td>155</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>195698.180000</td>\n",
       "      <td>C1791352632</td>\n",
       "      <td>8542.030000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C1013511446</td>\n",
       "      <td>10366240.420000</td>\n",
       "      <td>10561938.600000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>22.910032</td>\n",
       "      <td>204240.210000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1908414</td>\n",
       "      <td>166</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>308789.580000</td>\n",
       "      <td>C1854610643</td>\n",
       "      <td>3580318.370000</td>\n",
       "      <td>3889107.950000</td>\n",
       "      <td>C1013511446</td>\n",
       "      <td>10561938.600000</td>\n",
       "      <td>10253149.020000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.086246</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2778922</td>\n",
       "      <td>214</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>365923.470000</td>\n",
       "      <td>C737433241</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C1013511446</td>\n",
       "      <td>10253149.020000</td>\n",
       "      <td>10619072.490000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>365923.470000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2852444</td>\n",
       "      <td>227</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>2531127.630000</td>\n",
       "      <td>C1008129833</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C1013511446</td>\n",
       "      <td>10619072.490000</td>\n",
       "      <td>13150200.120000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>2531127.630000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3073119</td>\n",
       "      <td>235</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>41950.600000</td>\n",
       "      <td>C2055047714</td>\n",
       "      <td>27750075.050000</td>\n",
       "      <td>27792025.650000</td>\n",
       "      <td>C1013511446</td>\n",
       "      <td>13150200.120000</td>\n",
       "      <td>13108249.510000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.001512</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3205519</td>\n",
       "      <td>249</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>693889.520000</td>\n",
       "      <td>C1993947775</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C1013511446</td>\n",
       "      <td>13108249.510000</td>\n",
       "      <td>13802139.040000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>693889.520000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3252142</td>\n",
       "      <td>251</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>100947.300000</td>\n",
       "      <td>C2038458872</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C1013511446</td>\n",
       "      <td>13802139.040000</td>\n",
       "      <td>13903086.330000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>100947.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4115562</td>\n",
       "      <td>302</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>374092.820000</td>\n",
       "      <td>C361292479</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C1013511446</td>\n",
       "      <td>13903086.330000</td>\n",
       "      <td>14277179.160000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>374092.820000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5448455</td>\n",
       "      <td>379</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>64845.450000</td>\n",
       "      <td>C968628383</td>\n",
       "      <td>654.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C1013511446</td>\n",
       "      <td>14277179.160000</td>\n",
       "      <td>14366884.410000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>99.152064</td>\n",
       "      <td>65499.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5473371</td>\n",
       "      <td>379</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>24859.810000</td>\n",
       "      <td>C2057847261</td>\n",
       "      <td>48951.000000</td>\n",
       "      <td>24091.190000</td>\n",
       "      <td>C1013511446</td>\n",
       "      <td>14342024.600000</td>\n",
       "      <td>14366884.410000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.507851</td>\n",
       "      <td>49719.620000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5839979</td>\n",
       "      <td>402</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>1404263.060000</td>\n",
       "      <td>C605208523</td>\n",
       "      <td>3080.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C1013511446</td>\n",
       "      <td>14366884.410000</td>\n",
       "      <td>15771147.470000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>455.929565</td>\n",
       "      <td>1407343.060000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         step      type         amount     nameOrig  oldBalanceOrig  \\\n",
       "49826       9  CASH_OUT 2539898.070000   C728718059  2539898.070000   \n",
       "67782       9   CASH_IN  594209.730000  C1888557649  3151936.520000   \n",
       "83389      10   CASH_IN  103792.120000   C792601931    42696.840000   \n",
       "146724     12  CASH_OUT  398361.070000  C1402841545     8287.000000   \n",
       "147414     12  TRANSFER 1608465.320000   C657294376    10678.000000   \n",
       "201153     13  CASH_OUT  328650.740000    C47864924   151676.000000   \n",
       "209103     13  TRANSFER 2657484.810000   C783323484    41409.000000   \n",
       "230719     14   CASH_IN  362466.680000  C1031467251    52474.000000   \n",
       "299948     15   CASH_IN  236827.640000   C712201876 10927914.720000   \n",
       "342032     16  CASH_OUT  198969.470000  C1145539504        0.000000   \n",
       "399494     18  TRANSFER  653487.610000  C1547327732  1329013.790000   \n",
       "413683     18  TRANSFER 2223888.630000   C229094752        0.000000   \n",
       "506656     20   CASH_IN   50720.680000   C559538806   103362.000000   \n",
       "553849     22   CASH_IN  239700.250000  C1526133535    48463.000000   \n",
       "553854     22  TRANSFER 1482744.680000   C399937466        0.000000   \n",
       "583665     33  CASH_OUT  138155.180000  C1825249710        0.000000   \n",
       "596281     33  CASH_OUT  362779.210000  C1915461880        0.000000   \n",
       "607493     34  CASH_OUT  109870.970000   C349439645   293405.000000   \n",
       "640413     35   CASH_IN  200241.730000   C721340951  8328219.330000   \n",
       "658480     35  CASH_OUT  164270.050000  C2041096570    13202.000000   \n",
       "1030590    74  CASH_OUT  455074.130000   C695129655   455074.130000   \n",
       "1093395   130  CASH_OUT  530141.560000   C188801067        0.000000   \n",
       "1412569   139   CASH_IN  167864.690000   C617389671 21961622.690000   \n",
       "1471037   141  CASH_OUT  311718.690000  C1154087140        0.000000   \n",
       "1568249   155  CASH_OUT  195698.180000  C1791352632     8542.030000   \n",
       "1908414   166   CASH_IN  308789.580000  C1854610643  3580318.370000   \n",
       "2778922   214  TRANSFER  365923.470000   C737433241        0.000000   \n",
       "2852444   227  TRANSFER 2531127.630000  C1008129833        0.000000   \n",
       "3073119   235   CASH_IN   41950.600000  C2055047714 27750075.050000   \n",
       "3205519   249  TRANSFER  693889.520000  C1993947775        0.000000   \n",
       "3252142   251  CASH_OUT  100947.300000  C2038458872        0.000000   \n",
       "4115562   302  CASH_OUT  374092.820000   C361292479        0.000000   \n",
       "5448455   379  CASH_OUT   64845.450000   C968628383      654.000000   \n",
       "5473371   379  CASH_OUT   24859.810000  C2057847261    48951.000000   \n",
       "5839979   402  TRANSFER 1404263.060000   C605208523     3080.000000   \n",
       "\n",
       "         newBalanceOrig     nameDest  oldBalanceDest  newBalanceDest  isFraud  \\\n",
       "49826          0.000000  C1013511446      968.000000  1842864.220000        1   \n",
       "67782    3746146.250000  C1013511446  2540866.070000  1842864.220000        0   \n",
       "83389     146488.970000  C1013511446  1946656.340000  1842864.220000        0   \n",
       "146724         0.000000  C1013511446        0.000000  2006826.390000        0   \n",
       "147414         0.000000  C1013511446   398361.070000  2006826.390000        0   \n",
       "201153         0.000000  C1013511446  2006826.390000  2335477.130000        0   \n",
       "209103         0.000000  C1013511446  2335477.130000  4992961.940000        0   \n",
       "230719    414940.680000  C1013511446  4992961.940000  4630495.260000        0   \n",
       "299948  11164742.360000  C1013511446  4630495.260000  4393667.620000        0   \n",
       "342032         0.000000  C1013511446  4393667.620000  4592637.080000        0   \n",
       "399494    675526.180000  C1013511446  4592637.080000  7470013.320000        0   \n",
       "413683         0.000000  C1013511446  5246124.690000  7470013.320000        0   \n",
       "506656    154082.680000  C1013511446  7470013.320000  7419292.630000        0   \n",
       "553849    288163.250000  C1013511446  7419292.630000  8662337.060000        0   \n",
       "553854         0.000000  C1013511446  7179592.380000  8662337.060000        0   \n",
       "583665         0.000000  C1013511446  8662337.060000  8800492.240000        0   \n",
       "596281         0.000000  C1013511446  8800492.240000  9163271.450000        0   \n",
       "607493    183534.030000  C1013511446  9163271.450000  9072900.690000        0   \n",
       "640413   8528461.070000  C1013511446  9273142.420000  9072900.690000        0   \n",
       "658480         0.000000  C1013511446  9072900.690000  9237170.740000        0   \n",
       "1030590        0.000000  C1013511446  9237170.740000  9692244.860000        1   \n",
       "1093395        0.000000  C1013511446  9692244.860000 10222386.420000        0   \n",
       "1412569 22129487.380000  C1013511446 10222386.420000 10054521.730000        0   \n",
       "1471037        0.000000  C1013511446 10054521.730000 10366240.420000        0   \n",
       "1568249        0.000000  C1013511446 10366240.420000 10561938.600000        0   \n",
       "1908414  3889107.950000  C1013511446 10561938.600000 10253149.020000        0   \n",
       "2778922        0.000000  C1013511446 10253149.020000 10619072.490000        0   \n",
       "2852444        0.000000  C1013511446 10619072.490000 13150200.120000        0   \n",
       "3073119 27792025.650000  C1013511446 13150200.120000 13108249.510000        0   \n",
       "3205519        0.000000  C1013511446 13108249.510000 13802139.040000        0   \n",
       "3252142        0.000000  C1013511446 13802139.040000 13903086.330000        0   \n",
       "4115562        0.000000  C1013511446 13903086.330000 14277179.160000        0   \n",
       "5448455        0.000000  C1013511446 14277179.160000 14366884.410000        0   \n",
       "5473371    24091.190000  C1013511446 14342024.600000 14366884.410000        0   \n",
       "5839979        0.000000  C1013511446 14366884.410000 15771147.470000        0   \n",
       "\n",
       "         isFlaggedFraud     week  amt_bal_ratio  diffBalanceOrig  \n",
       "49826                 0 1.000000       1.000000   5079796.140000  \n",
       "67782                 0 1.000000       0.188522         0.000000  \n",
       "83389                 0 1.000000       2.430909        -0.010000  \n",
       "146724                0 1.000000      48.070601    406648.070000  \n",
       "147414                0 1.000000     150.633576   1619143.320000  \n",
       "201153                0 1.000000       2.166795    480326.740000  \n",
       "209103                0 1.000000      64.176503   2698893.810000  \n",
       "230719                0 1.000000       6.907548         0.000000  \n",
       "299948                0 1.000000       0.021672         0.000000  \n",
       "342032                0 1.000000            nan    198969.470000  \n",
       "399494                0 1.000000       0.491709   1306975.220000  \n",
       "413683                0 1.000000            nan   2223888.630000  \n",
       "506656                0 1.000000       0.490709         0.000000  \n",
       "553849                0 1.000000       4.946046         0.000000  \n",
       "553854                0 1.000000            nan   1482744.680000  \n",
       "583665                0 1.000000            nan    138155.180000  \n",
       "596281                0 1.000000            nan    362779.210000  \n",
       "607493                0 1.000000       0.374469    219741.940000  \n",
       "640413                0 1.000000       0.024044        -0.010000  \n",
       "658480                0 1.000000      12.442815    177472.050000  \n",
       "1030590               0 1.000000       1.000000    910148.260000  \n",
       "1093395               0 1.000000            nan    530141.560000  \n",
       "1412569               0 1.000000       0.007644         0.000000  \n",
       "1471037               0 1.000000            nan    311718.690000  \n",
       "1568249               0 1.000000      22.910032    204240.210000  \n",
       "1908414               0 1.000000       0.086246         0.000000  \n",
       "2778922               0 2.000000            nan    365923.470000  \n",
       "2852444               0 2.000000            nan   2531127.630000  \n",
       "3073119               0 2.000000       0.001512         0.000000  \n",
       "3205519               0 2.000000            nan    693889.520000  \n",
       "3252142               0 2.000000            nan    100947.300000  \n",
       "4115562               0 2.000000            nan    374092.820000  \n",
       "5448455               0 3.000000      99.152064     65499.450000  \n",
       "5473371               0 3.000000       0.507851     49719.620000  \n",
       "5839979               0 3.000000     455.929565   1407343.060000  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['nameDest']=='C1013511446') | (df['nameOrig']=='C1013511446')].sort_values(by='step')\n",
    "# Why C1013511446 is receiving money post Fraud? In step 12 and 13 balance movements are not similar to each other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " # Transactions which are Fraud\n",
      "8213\n",
      "\n",
      " % Transactions which are Fraud and New Balance Origination==0\n",
      "0.9805186898818946\n",
      "\n",
      " # Transactions which are not Fraud\n",
      "6354407\n",
      "\n",
      " % Transactions which are not Fraud and New Balance Origination==0\n",
      "0.566774051457516\n",
      "\n",
      " # Transactions which are not Fraud and Cash Out or Transfer\n",
      "2762196\n",
      "\n",
      " % Transactions which are not Fraud and Cash Out or Transfer and New Balance Origination==0\n",
      "0.9009509100729999\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n # Transactions which are Fraud\")\n",
    "print(len(df[df['isFraud']==1]))\n",
    "print(\"\\n % Transactions which are Fraud and New Balance Origination==0\")\n",
    "print(len(df[(df['isFraud']==1) & (df['newBalanceOrig']==0)])/len(df[df['isFraud']==1]))\n",
    "print(\"\\n # Transactions which are not Fraud\")\n",
    "print(len(df[df['isFraud']==0]))\n",
    "print(\"\\n % Transactions which are not Fraud and New Balance Origination==0\")\n",
    "print(len(df[(df['isFraud']==0) & (df['newBalanceOrig']==0)])/len(df[df['isFraud']==0]))\n",
    "print(\"\\n # Transactions which are not Fraud and Cash Out or Transfer\")\n",
    "print(len(df[(df['isFraud']==0) & (df['type'].isin(['CASH_OUT','TRANSFER']))]))\n",
    "print(\"\\n % Transactions which are not Fraud and Cash Out or Transfer and New Balance Origination==0\")\n",
    "print(len(df[(df['isFraud']==0) & (df['newBalanceOrig']==0) & (df['type'].isin(['CASH_OUT','TRANSFER']))])\n",
    "      /len(df[(df['isFraud']==0) & (df['type'].isin(['CASH_OUT','TRANSFER']))]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Findings 18: New Balance Origination==0 is not a distinguisher between Fraud and Non Fraud transactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>nameOrig</th>\n",
       "      <th>oldBalanceOrig</th>\n",
       "      <th>newBalanceOrig</th>\n",
       "      <th>nameDest</th>\n",
       "      <th>oldBalanceDest</th>\n",
       "      <th>newBalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>isFlaggedFraud</th>\n",
       "      <th>week</th>\n",
       "      <th>amt_bal_ratio</th>\n",
       "      <th>diffBalanceOrig</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2736447</td>\n",
       "      <td>212</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C1510987794</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C1696624817</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3247298</td>\n",
       "      <td>250</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C521393327</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C480398193</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3760289</td>\n",
       "      <td>279</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C539112012</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C1106468520</td>\n",
       "      <td>538547.630000</td>\n",
       "      <td>538547.630000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5563714</td>\n",
       "      <td>387</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C1294472700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C1325541393</td>\n",
       "      <td>7970766.570000</td>\n",
       "      <td>7970766.570000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5996408</td>\n",
       "      <td>425</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C832555372</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C1462759334</td>\n",
       "      <td>76759.900000</td>\n",
       "      <td>76759.900000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5996410</td>\n",
       "      <td>425</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C69493310</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C719711728</td>\n",
       "      <td>2921531.340000</td>\n",
       "      <td>2921531.340000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6168500</td>\n",
       "      <td>554</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C10965156</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C1493336195</td>\n",
       "      <td>230289.660000</td>\n",
       "      <td>230289.660000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6205440</td>\n",
       "      <td>586</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C1303719003</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C900608348</td>\n",
       "      <td>1328472.860000</td>\n",
       "      <td>1328472.860000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6266414</td>\n",
       "      <td>617</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C1971175979</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C1352345416</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6281483</td>\n",
       "      <td>646</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C2060908932</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C1587892888</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6281485</td>\n",
       "      <td>646</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C1997645312</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C601248796</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6296015</td>\n",
       "      <td>671</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C1960007029</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C459118517</td>\n",
       "      <td>27938.720000</td>\n",
       "      <td>27938.720000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6351226</td>\n",
       "      <td>702</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C1461113533</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C1382150537</td>\n",
       "      <td>107777.020000</td>\n",
       "      <td>107777.020000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6362461</td>\n",
       "      <td>730</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C729003789</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C1388096959</td>\n",
       "      <td>1008609.530000</td>\n",
       "      <td>1008609.530000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6362463</td>\n",
       "      <td>730</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C2088151490</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C1156763710</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6362585</td>\n",
       "      <td>741</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C312737633</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>C1400061387</td>\n",
       "      <td>267522.870000</td>\n",
       "      <td>267522.870000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         step      type   amount     nameOrig  oldBalanceOrig  newBalanceOrig  \\\n",
       "2736447   212  CASH_OUT 0.000000  C1510987794        0.000000        0.000000   \n",
       "3247298   250  CASH_OUT 0.000000   C521393327        0.000000        0.000000   \n",
       "3760289   279  CASH_OUT 0.000000   C539112012        0.000000        0.000000   \n",
       "5563714   387  CASH_OUT 0.000000  C1294472700        0.000000        0.000000   \n",
       "5996408   425  CASH_OUT 0.000000   C832555372        0.000000        0.000000   \n",
       "5996410   425  CASH_OUT 0.000000    C69493310        0.000000        0.000000   \n",
       "6168500   554  CASH_OUT 0.000000    C10965156        0.000000        0.000000   \n",
       "6205440   586  CASH_OUT 0.000000  C1303719003        0.000000        0.000000   \n",
       "6266414   617  CASH_OUT 0.000000  C1971175979        0.000000        0.000000   \n",
       "6281483   646  CASH_OUT 0.000000  C2060908932        0.000000        0.000000   \n",
       "6281485   646  CASH_OUT 0.000000  C1997645312        0.000000        0.000000   \n",
       "6296015   671  CASH_OUT 0.000000  C1960007029        0.000000        0.000000   \n",
       "6351226   702  CASH_OUT 0.000000  C1461113533        0.000000        0.000000   \n",
       "6362461   730  CASH_OUT 0.000000   C729003789        0.000000        0.000000   \n",
       "6362463   730  CASH_OUT 0.000000  C2088151490        0.000000        0.000000   \n",
       "6362585   741  CASH_OUT 0.000000   C312737633        0.000000        0.000000   \n",
       "\n",
       "            nameDest  oldBalanceDest  newBalanceDest  isFraud  isFlaggedFraud  \\\n",
       "2736447  C1696624817        0.000000        0.000000        1               0   \n",
       "3247298   C480398193        0.000000        0.000000        1               0   \n",
       "3760289  C1106468520   538547.630000   538547.630000        1               0   \n",
       "5563714  C1325541393  7970766.570000  7970766.570000        1               0   \n",
       "5996408  C1462759334    76759.900000    76759.900000        1               0   \n",
       "5996410   C719711728  2921531.340000  2921531.340000        1               0   \n",
       "6168500  C1493336195   230289.660000   230289.660000        1               0   \n",
       "6205440   C900608348  1328472.860000  1328472.860000        1               0   \n",
       "6266414  C1352345416        0.000000        0.000000        1               0   \n",
       "6281483  C1587892888        0.000000        0.000000        1               0   \n",
       "6281485   C601248796        0.000000        0.000000        1               0   \n",
       "6296015   C459118517    27938.720000    27938.720000        1               0   \n",
       "6351226  C1382150537   107777.020000   107777.020000        1               0   \n",
       "6362461  C1388096959  1008609.530000  1008609.530000        1               0   \n",
       "6362463  C1156763710        0.000000        0.000000        1               0   \n",
       "6362585  C1400061387   267522.870000   267522.870000        1               0   \n",
       "\n",
       "            week  amt_bal_ratio  diffBalanceOrig  \n",
       "2736447 2.000000            nan         0.000000  \n",
       "3247298 2.000000            nan         0.000000  \n",
       "3760289 2.000000            nan         0.000000  \n",
       "5563714 3.000000            nan         0.000000  \n",
       "5996408 3.000000            nan         0.000000  \n",
       "5996410 3.000000            nan         0.000000  \n",
       "6168500 3.000000            nan         0.000000  \n",
       "6205440 4.000000            nan         0.000000  \n",
       "6266414 4.000000            nan         0.000000  \n",
       "6281483 4.000000            nan         0.000000  \n",
       "6281485 4.000000            nan         0.000000  \n",
       "6296015 4.000000            nan         0.000000  \n",
       "6351226 4.000000            nan         0.000000  \n",
       "6362461 4.000000            nan         0.000000  \n",
       "6362463 4.000000            nan         0.000000  \n",
       "6362585 4.000000            nan         0.000000  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['amount']==0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Findings 19: All transactions with Amount==0 are Fraud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " For only Transaction Types CASH_OUT & TRANSFER\n",
      "\n",
      " % Fraud Transactions where Amount>0 and Origin Balances are 0\n",
      "0.00304989630352568\n",
      "\n",
      " % Non Fraud Transactions where Amount>0 and Origin Balances are 0\n",
      "0.4737321319703598\n",
      "\n",
      " % Fraud Transactions where Amount>0 and Destination Balances are 0\n",
      "0.4965231182139807\n",
      "\n",
      " % Non Fraud Transactions where Amount>0 and Destination Balances are 0\n",
      "0.0006176245277308345\n"
     ]
    }
   ],
   "source": [
    "# Cases amount is positive but balances are zero\n",
    "print(\"\\n For only Transaction Types CASH_OUT & TRANSFER\")\n",
    "print(\"\\n % Fraud Transactions where Amount>0 and Origin Balances are 0\")\n",
    "print(len(df[(df['isFraud']==1)&(df['amount']!=0)&(df['oldBalanceOrig']==0)&(df['newBalanceOrig']==0)])\n",
    "      /len(df[(df['isFraud']==1)&(df['amount']!=0)]))\n",
    "print(\"\\n % Non Fraud Transactions where Amount>0 and Origin Balances are 0\")\n",
    "print(len(df[(df['isFraud']==0)&(df['amount']!=0)&(df['oldBalanceOrig']==0)\n",
    "             &(df['newBalanceOrig']==0)&(df['type'].isin(['CASH_OUT','TRANSFER']))])\n",
    "             /len(df[(df['isFraud']==0)&(df['amount']!=0)&(df['type'].isin(['CASH_OUT','TRANSFER']))]))\n",
    "print(\"\\n % Fraud Transactions where Amount>0 and Destination Balances are 0\")\n",
    "print(len(df[(df['isFraud']==1)&(df['amount']!=0)&(df['oldBalanceDest']==0)&(df['newBalanceDest']==0)])\n",
    "     /len(df[(df['isFraud']==1)&(df['amount']!=0)]))\n",
    "print(\"\\n % Non Fraud Transactions where Amount>0 and Destination Balances are 0\")\n",
    "print(len(df[(df['isFraud']==0)&(df['amount']!=0)&(df['oldBalanceDest']==0)\n",
    "             &(df['newBalanceDest']==0)&(df['type'].isin(['CASH_OUT','TRANSFER']))])\n",
    "             /len(df[(df['isFraud']==0)&(df['amount']!=0)&(df['type'].isin(['CASH_OUT','TRANSFER']))]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Findings 20: Distinct difference in pattern of ZERO Origination and Destination Balance for Fraud and Non Fraud Transactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Old Balance Orig Distribution for Fraud Transactions\n",
      "count       8213.000000\n",
      "mean     1649667.605712\n",
      "std      3547719.439109\n",
      "min            0.000000\n",
      "25%       125822.440000\n",
      "50%       438983.450000\n",
      "75%      1517771.480000\n",
      "max     59585040.370000\n",
      "Name: oldBalanceOrig, dtype: float64\n",
      "\n",
      "Old Balance Distribution Orig for Non Fraud Transactions\n",
      "count    2762196.000000\n",
      "mean       42879.685563\n",
      "std       135200.889180\n",
      "min            0.000000\n",
      "25%            0.000000\n",
      "50%          283.000000\n",
      "75%        30790.000000\n",
      "max     43818855.300000\n",
      "Name: oldBalanceOrig, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nOld Balance Orig Distribution for Fraud Transactions\")\n",
    "print(df[df['isFraud']==1]['oldBalanceOrig'].describe())\n",
    "print(\"\\nOld Balance Distribution Orig for Non Fraud Transactions\")\n",
    "print(df[(df['isFraud']==0)&(df['type'].isin(['CASH_OUT','TRANSFER']))]['oldBalanceOrig'].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Old Balance Orig Distribution for Fraud Transactions for Old Balance Orig>0\n",
      "count       8172.000000\n",
      "mean     1657944.205300\n",
      "std      3554679.214471\n",
      "min           63.800000\n",
      "25%       127564.330000\n",
      "50%       443788.325000\n",
      "75%      1532242.692500\n",
      "max     59585040.370000\n",
      "Name: oldBalanceOrig, dtype: float64\n",
      "\n",
      "Old Balance Orig Distribution for Non Fraud Transactions for Old Balance Orig>0\n",
      "count    1453655.000000\n",
      "mean       81478.821277\n",
      "std       177732.361829\n",
      "min            0.050000\n",
      "25%        10311.000000\n",
      "50%        28690.000000\n",
      "75%        85871.500000\n",
      "max     43818855.300000\n",
      "Name: oldBalanceOrig, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nOld Balance Orig Distribution for Fraud Transactions for Old Balance Orig>0\")\n",
    "print(df[(df['isFraud']==1) & (df['oldBalanceOrig']>0)]['oldBalanceOrig'].describe())\n",
    "print(\"\\nOld Balance Orig Distribution for Non Fraud Transactions for Old Balance Orig>0\")\n",
    "print(df[(df['isFraud']==0)&(df['type'].isin(['CASH_OUT','TRANSFER'])) & (df['oldBalanceOrig']>0)]['oldBalanceOrig'].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Findings 21: Old Balance Orig tend to be higher for Fraud than Non Fraud transactions. Customer with higher balances are usually targeted for Fraud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>nameOrig</th>\n",
       "      <th>oldBalanceOrig</th>\n",
       "      <th>newBalanceOrig</th>\n",
       "      <th>nameDest</th>\n",
       "      <th>oldBalanceDest</th>\n",
       "      <th>newBalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>isFlaggedFraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1030443</td>\n",
       "      <td>65</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>1282971.57</td>\n",
       "      <td>C1175896731</td>\n",
       "      <td>1282971.57</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C1714931087</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1107905</td>\n",
       "      <td>130</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>251983.50</td>\n",
       "      <td>C737757614</td>\n",
       "      <td>20522.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C1714931087</td>\n",
       "      <td>0.00</td>\n",
       "      <td>251983.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1168398</td>\n",
       "      <td>132</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>29084.28</td>\n",
       "      <td>C1023330867</td>\n",
       "      <td>51999.00</td>\n",
       "      <td>22914.72</td>\n",
       "      <td>C1422447255</td>\n",
       "      <td>0.00</td>\n",
       "      <td>29084.28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1168833</td>\n",
       "      <td>132</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>202763.68</td>\n",
       "      <td>C669553166</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C1714931087</td>\n",
       "      <td>251983.50</td>\n",
       "      <td>454747.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1446676</td>\n",
       "      <td>140</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>103156.75</td>\n",
       "      <td>C398595800</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C1714931087</td>\n",
       "      <td>140102.47</td>\n",
       "      <td>243259.21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2186261</td>\n",
       "      <td>185</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>214555.85</td>\n",
       "      <td>C423543548</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C1066927674</td>\n",
       "      <td>4575179.83</td>\n",
       "      <td>4789735.69</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6039814</td>\n",
       "      <td>486</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>214793.32</td>\n",
       "      <td>C2140495649</td>\n",
       "      <td>214793.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C423543548</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6149940</td>\n",
       "      <td>546</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>18091.05</td>\n",
       "      <td>C1714931087</td>\n",
       "      <td>197227.00</td>\n",
       "      <td>179135.95</td>\n",
       "      <td>C1339132632</td>\n",
       "      <td>66177.84</td>\n",
       "      <td>84268.89</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6362556</td>\n",
       "      <td>738</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>814689.88</td>\n",
       "      <td>C2029041842</td>\n",
       "      <td>814689.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C1023330867</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         step      type      amount     nameOrig  oldBalanceOrig  \\\n",
       "1030443    65  TRANSFER  1282971.57  C1175896731      1282971.57   \n",
       "1107905   130  CASH_OUT   251983.50   C737757614        20522.00   \n",
       "1168398   132  CASH_OUT    29084.28  C1023330867        51999.00   \n",
       "1168833   132  CASH_OUT   202763.68   C669553166            0.00   \n",
       "1446676   140  CASH_OUT   103156.75   C398595800            0.00   \n",
       "2186261   185  CASH_OUT   214555.85   C423543548            0.00   \n",
       "6039814   486  TRANSFER   214793.32  C2140495649       214793.32   \n",
       "6149940   546  CASH_OUT    18091.05  C1714931087       197227.00   \n",
       "6362556   738  TRANSFER   814689.88  C2029041842       814689.88   \n",
       "\n",
       "         newBalanceOrig     nameDest  oldBalanceDest  newBalanceDest  isFraud  \\\n",
       "1030443            0.00  C1714931087            0.00            0.00        1   \n",
       "1107905            0.00  C1714931087            0.00       251983.50        0   \n",
       "1168398        22914.72  C1422447255            0.00        29084.28        0   \n",
       "1168833            0.00  C1714931087       251983.50       454747.18        0   \n",
       "1446676            0.00  C1714931087       140102.47       243259.21        0   \n",
       "2186261            0.00  C1066927674      4575179.83      4789735.69        0   \n",
       "6039814            0.00   C423543548            0.00            0.00        1   \n",
       "6149940       179135.95  C1339132632        66177.84        84268.89        0   \n",
       "6362556            0.00  C1023330867            0.00            0.00        1   \n",
       "\n",
       "         isFlaggedFraud  \n",
       "1030443               0  \n",
       "1107905               0  \n",
       "1168398               0  \n",
       "1168833               0  \n",
       "1446676               0  \n",
       "2186261               0  \n",
       "6039814               0  \n",
       "6149940               0  \n",
       "6362556               0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Dest_Fraud_List=df.loc[(df['isFraud']==1) & (df['type']=='TRANSFER'),'nameDest'].drop_duplicates().tolist()\n",
    "CO_NoFraud_List=df.loc[(df['isFraud']==0) & (df['type']=='CASH_OUT') & (df['nameOrig'].isin(Dest_Fraud_List))\n",
    "                       ,'nameOrig'].drop_duplicates().tolist()\n",
    "df.loc[(df['type'].isin(['CASH_OUT','TRANSFER']))&\n",
    "       ((df['nameDest'].isin(CO_NoFraud_List))|(df['nameOrig'].isin(CO_NoFraud_List)))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Findings 22: Only in one case, C1714931087 [step 65 and 546] is doing a CASH_OUT  which is not tagged as Fraud after receiving a Fraud TRANSFER"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Actions from Data Exploration Findings:\n",
    "\n",
    "#### 1: Take first 3 weeks as Train and Week 4 as OOT Validation sample [Findings: 1]\n",
    "#### 2: No missing value imputation required [Findings: 2]\n",
    "#### 3: Rely of features created from single transactions and me vs others. Create a flag for nameOrig in consequetive 2 weeks[Findings: 3,4]\n",
    "#### 4: Model data on only CASH_OUT and TRANSFER transactions/Create variable with extra weightage to CASH_OUT and TRANSFER transaction types [Findings: 5]\n",
    "#### 5: isFlaggedFraud feature can be dropped [6 - 11]\n",
    "#### 6: Create a flag for Payments to Merchants if modelling data on all transaction types[12 - 14]\n",
    "#### 7: Assign a standard value for Amount/oldBalanceOrig for CASH IN type transactions [15-17]\n",
    "#### 8: Assign Flag for Amount==0 [19]\n",
    "#### 9: Assign Flag for CASH_OUT & TRANSFER type transactions when Amount>0 and newBalanceOrig==oldBalanceOrig==0 \n",
    "#### 10: Assign Flag for CASH_OUT & TRANSFER type transactions when Amount>0 and newBalanceDest==oldBalanceDest==0 \n",
    "#### 11: Make buckets for Old Balance Orig for CASH_OUT & TRANSFER type transactions [21]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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