Credit Risk Modeling in R

The Korshub Take

Estimating probability of default is a fundamental component of banking risk management. This intermediate course demonstrates how to build and evaluate credit risk models in R using two widely adopted algorithms in banking: logistic regression and decision trees, tested on authentic credit application datasets.

Who It's For

R users, financial data analysts, and risk modeling professionals looking to evaluate loan default probabilities with machine learning.

Key Takeaways

• Logistic Regression Models: Implement logistic regression to estimate debtor probability of default.
• Decision Tree Classification: Build decision tree classifiers tailored for credit scoring context.
• Bank Model Evaluation: Measure and evaluate risk model performance against standard financial institution metrics.