Create decision_support.py

ai-powered-risk-management-system/decision_support.py

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+import matplotlib.pyplot as plt
+
+def risk_mitigation_strategies(fraud_score, liquidity_score, stability_score):
+    """
+    Implement risk mitigation strategies based on risk scores.
+
+    Args:
+        fraud_score (float): Fraud detection score
+        liquidity_score (float): Liquidity risk assessment score
+        stability_score (float): Network stability evaluation score
+
+    Returns:
+        None
+    """
+    if fraud_score > 0.5:
+        print('Implement additional security measures, such as:')
+        print('  * Enhancing user authentication and authorization')
+        print('  * Implementing more robust fraud detection algorithms')
+        print('  * Increasing transaction monitoring and reporting')
+    elif liquidity_score < 0.5:
+        print('Increase liquidity provisions, such as:')
+        print('  * Increasing the reserve ratio')
+        print('  * Implementing more efficient liquidity management algorithms')
+        print('  * Enhancing market making and liquidity provision incentives')
+    elif stability_score < 0.5:
+        print('Optimize network performance, such as:')
+        print('  * Upgrading node hardware and infrastructure')
+        print('  * Implementing more efficient consensus algorithms')
+        print('  * Enhancing network monitoring and maintenance')
+
+def alert_notifications(fraud_score, liquidity_score, stability_score):
+    """
+    Implement alert notifications based on risk scores.
+
+    Args:
+        fraud_score (float): Fraud detection score
+        liquidity_score (float): Liquidity risk assessment score
+        stability_score (float): Network stability evaluation score
+
+    Returns:
+        None
+    """
+    if fraud_score > 0.8:
+        print('**Fraud Alert!**')
+        print('  * Immediate action required to prevent potential fraud')
+    elif liquidity_score < 0.2:
+        print('**Liquidity Alert!**')
+        print('  * Immediate action required to maintain liquidity')
+    elif stability_score < 0.2:
+        print('**Stability Alert!**')
+        print('  * Immediate action required to maintain network stability')
+
+def visualize_risk_scores(fraud_score, liquidity_score, stability_score):
+    """
+    Visualize risk scores using a heatmap.
+
+    Args:
+        fraud_score (float): Fraud detection score
+        liquidity_score (float): Liquidity risk assessment score
+        stability_score (float): Network stability evaluation score
+
+    Returns:
+        None
+    """
+    risk_scores = pd.DataFrame({'Fraud Score': [fraud_score], 'Liquidity Score': [liquidity_score], 'Stability Score': [stability_score]})
+    sns.heatmap(risk_scores, annot=True, cmap='coolwarm', square=True)
+    plt.show()
+
+def visualize_market_trends(market_data):
+    """
+    Visualize market trends using a line chart.
+
+    Args:
+        market_data (pd.DataFrame): Market data
+
+    Returns:
+        None
+    """
+    plt.plot(market_data['date'], market_data['price'])
+    plt.xlabel('Date')
+    plt.ylabel('Price')
+    plt.title('Market Trends')
+    plt.show()
+
+def visualize_user_behavior(user_behavior_data):
+    """
+    Visualize user behavior using a bar chart.
+
+    Args:
+        user_behavior_data (pd.DataFrame): User behavior data
+
+    Returns:
+        None
+    """
+    plt.bar(user_behavior_data['user_id'], user_behavior_data['transaction_count'])
+    plt.xlabel('User ID')
+    plt.ylabel('Transaction Count')
+    plt.title('User Behavior')
+    plt.show()