"algo-risk-credit"

Credit Scoring Model

Overview

Credit scoring models predict the probability of default (PD) from borrower characteristics using logistic regression or gradient boosting. Output: a score (300-850 range) or PD (0-1). Used for loan approval, pricing, and portfolio risk management.

When to Use

Trigger conditions:

• Building a scorecard for loan/credit approval decisions
• Predicting default probability for risk-based pricing
• Evaluating existing credit models for discriminatory power

When NOT to use:

• For corporate bankruptcy prediction (use Altman Z-Score)
• For market risk measurement (use VaR)

Algorithm

IRON LAW: A Credit Model Must Discriminate AND Be Calibrated
Discrimination (AUC): correctly ranking good vs bad borrowers.
Calibration: predicted PD matches actual default rates.
A model with AUC=0.85 but predicted PD 2x actual default rate will
cause systematic over/under-pricing. Need BOTH properties.

Phase 1: Input Validation

Collect: borrower features (income, debt ratio, credit history length, delinquency count, utilization), outcome variable (default within 12-24 months). Handle: missing values, class imbalance (typically 2-5% default rate). Gate: Sufficient defaults (300+ events), features available at decision time.

Phase 2: Core Algorithm

1. Feature engineering: WOE (Weight of Evidence) binning for logistic regression, or direct encoding for GBDT
2. Train model: logistic regression (interpretable, regulatory-preferred) or GBDT (higher accuracy)
3. Calibrate: Platt scaling on holdout, ensure predicted PD matches actual default rate by decile
4. Convert to score: Score = offset + factor × log(odds), scaled to 300-850 range

Phase 3: Verification

Evaluate: AUC (>0.70 acceptable, >0.80 good), KS statistic, Gini coefficient. Population stability index (PSI) for monitoring drift. Gate: AUC > 0.70, calibration acceptable, no discriminatory bias in protected attributes.

Phase 4: Output

Return score, PD, and key risk drivers.