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
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