Machine Learning in Credit Risk Modeling

This lecture explores the challenges, lessons, and opportunities of using machine learning in credit risk modeling. It compares machine learning models with traditional statistical models, highlighting the advantages of capturing non-linear relationships. The lecture delves into the performance of different machine learning algorithms, such as artificial neural networks, random forest, and boosting. It discusses how machine learning models can provide better insights but may also present challenges in terms of interpretability and sensitivity to outliers. The analysis shows that expanding the dataset with loan behavioral variables significantly enhances predictive power across all modeling methods.