Credit Scoring Model 信用評分模型
Released已發布Build credit scoring models to predict default probability from borrower characteristics. Use this skill when the user needs to assess creditworthiness, build a credit scorecard, or evaluate lending risk — even if they say 'predict default risk', 'credit scoring', or 'loan approval model'.
演算法技能：Credit Scoring Model 分析與應用。
Overview概述
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使用時機
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 演算法
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
- Feature engineering: WOE (Weight of Evidence) binning for logistic regression, or direct encoding for GBDT
- Train model: logistic regression (interpretable, regulatory-preferred) or GBDT (higher accuracy)
- Calibrate: Platt scaling on holdout, ensure predicted PD matches actual default rate by decile
- 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輸出格式
{
"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範例
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注意事項
- 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參考資料
- For WOE binning methodology, see
references/woe-binning.md - For reject inference techniques, see
references/reject-inference.md