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