Credit scoring is where traditional statistics meets modern machine learning. The fundamental task—predicting probability of default—has been approached with logistic regression for decades. Modern ML techniques can improve predictive power, but the statistical foundations remain essential for model interpretability and regulatory compliance.
The Logistic Regression Foundation
Logistic regression remains the workhorse of credit scoring for good reasons: interpretable coefficients, well-understood behavior, and regulatory acceptance. The log-odds formulation provides intuitive scorecards where each variable contributes additively to the credit score.
Feature Engineering from Statistics
The most impactful improvements often come from feature engineering rather than model complexity:
- Weight of Evidence (WOE) transformation for optimal binning
- Information Value (IV) for feature selection
- Interaction terms identified through statistical testing
- Time-based features capturing behavioral trends
Gradient Boosting for Credit
Gradient boosting models (XGBoost, LightGBM) can capture non-linear relationships and interactions automatically. However, they require careful handling in credit contexts:
- Monotonicity constraints to ensure logical relationships
- SHAP values for local interpretability
- Out-of-time validation to detect concept drift
- Ensemble with logistic regression for stability
Model Validation
Statistical rigor is essential in model validation. Beyond AUC-ROC, we emphasize KS statistic, Gini coefficient, population stability index (PSI), and characteristic stability analysis. These metrics ensure the model performs consistently across different populations and time periods.
A slightly less accurate model that is stable and interpretable is often better than a complex model with marginally higher accuracy but unknown failure modes.