Credit Risk Modeling in Python

The Korshub Take

Evaluating credit applications accurately is crucial for financial institutions managing risk and profitability. This intermediate Python course walks through preparing credit application datasets, training machine learning models, and layering business rules to calculate expected monetary value and manage credit default risk.

Who It's For

Data scientists, financial analysts, and risk managers interested in leveraging Python and machine learning for credit application scoring.

Key Takeaways

• Credit Data Preparation: Preprocess and clean financial application data for risk modeling tasks.
• Machine Learning Scoring: Build machine learning models to assess probability of default and manage credit risk.
• Business Rules & Expected Value: Combine model predictions with business constraints to optimize overall loan portfolio value.