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.

Output Format

{
  "score": 680,
  "pd": 0.035,
  "risk_grade": "B",
  "top_risk_factors": [{"factor": "high_utilization", "impact": -45}, {"factor": "short_history", "impact": -30}],
  "metadata": {"model": "logistic_regression", "auc": 0.78, "vintage": "2024-Q3"}
}

Examples

Sample I/O

Input: Borrower: income=$60K, DTI=35%, 5yr credit history, 0 delinquencies, 60% utilization Expected: Score ~680, PD ~3.5%, Grade B (some risk from high utilization)

Edge Cases

Input	Expected	Why
No credit history (thin file)	High uncertainty, default to conservative	Insufficient data for scoring
All features identical	Same score regardless of outcome	Model can't differentiate — need more features
Major economy shift	PSI > 0.25, model needs recalibration	Population has shifted from training distribution

Gotchas

• Reject inference: Training data only includes approved applicants. Rejected applicants' outcomes are unknown, creating selection bias. Use reject inference techniques.
• Fair lending: Models must not discriminate by protected attributes (race, gender, age). Even proxy variables (zip code ≈ race) can create disparate impact. Test with fairness metrics.
• Through-the-door vs on-the-books: TTD samples include all applicants; OTB only approved ones. Model purpose determines which sample to use.
• Vintage analysis: Default rates vary by economic conditions. A 2019-trained model may not predict well in a recession. Track model performance by vintage.
• Regulatory requirements: Financial regulators (Basel, OCC, FDIC) have specific requirements for model validation, documentation, and fair lending testing.

References

• For WOE binning methodology, see references/woe-binning.md
• For reject inference techniques, see references/reject-inference.md