Comprehensive Guide to Explainable Predictive Models for Credit Unions: Risk, Default, Churn, and Fraud

1. Credit Risk Assessment (Loan Approval)

• Recommended Model: Logistic Regression or Decision Tree (Scorecard Model)
• Explainability: High
• Interpretability: SHAP/LIME, coefficients or decision-tree rules

FeatureTypical Weight / Importance (approx.)Credit Score~30-35%Debt-to-Income Ratio20-25%Payment History (past defaults)20-30%Income Level15-20%Employment Stability10%Existing Debt Obligations10%Account Tenure5-10%

Statistical Model: Logistic regression is optimal due to simplicity, regulatory transparency, and straightforward odds interpretation.

2. Loan Default Prediction (Existing Loans)

• Recommended Model: Random Forest, XGBoost
• Explainability: Medium-High (Requires interpretability tools)
• Interpretability: SHAP, LIME, Feature Importance Analysis

FeatureTypical Weight / ImportanceRecent Payment Delinquencies25-30%Credit Score Changes (negative)20-25%Credit Utilization15-20%Income Stability / Declines10-15%Historical Default Patterns10%Loan-to-Value Ratio10%Account Age and Loan Tenure5-10%

Statistical Model: XGBoost/Random Forest for accuracy; Logistic Regression for simplicity. Interpretability with SHAP is critical.

3. Member Churn Analysis

• Recommended Model: Random Forest, Decision Trees
• Explainability: Medium-High (with interpretability methods)
• Interpretability: SHAP, LIME, Feature Importance, Decision Path Rules

FeatureTypical Weight / ImportanceRecent Transaction Activity Decline25-30%Number of Financial Products Held15-20%Account Tenure15-20%Online Banking Engagement15%Direct Deposit Presence10%Member Age or Life Stage10%

Statistical Model: Random Forest/XGBoost provides better accuracy due to capturing nonlinear interactions. Decision trees are simpler if interpretability is primary.

3. Fraud Detection

• Recommended Model: Random Forest or XGBoost
• Explainability: Medium (with SHAP/LIME)
• Interpretability: SHAP, LIME, Anomaly Scores, Business Rule Triggers

FeatureTypical Weight / ImportanceTransaction Amount relative to historical average20-30%Transaction Location (unusual location)20-25%Frequency/velocity of transactions15-20%Merchant Category (high-risk merchant)10-15%Device/IP location mismatch10%Transaction Timing (unusual times)10%

Statistical Model: Random Forest/XGBoost due to robustness with fraud complexity and anomalies, combined with SHAP/LIME to ensure explainability.

Recommended Statistical Model by Use Case:

Use CaseStatistical ModelInterpretability MethodCredit Risk AssessmentLogistic Regression, Decision TreesCoefficients, Scorecards, SHAPLoan Default PredictionXGBoost, Random ForestSHAP, LIME, Feature ImportanceMember Churn AnalysisRandom Forest, Decision TreesSHAP, Decision rules, LIMEFraud DetectionRandom Forest, XGBoostSHAP, LIME, Anomaly Analysis

Implementation Insights:

• Logistic Regression or simple scorecards remain widely preferred for loan-related predictions due to regulatory compliance (explainability).
• Advanced models (Random Forest, XGBoost) provide higher accuracy, especially valuable in fraud detection, loan default, and churn analysis, but require interpretability tools like SHAP or LIME for regulatory compliance and internal auditability.
• Regular retraining and validation ensure stability, compliance, and robustness against changing patterns or fraud tactics.

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