Completed as part of the 365 Data Science Credit Risk Modeling in Python Udemy course. Developed an end-to-end credit risk modeling pipeline for consumer lending, covering data preprocessing, feature engineering, Probability of Default , Loss Given Default , Exposure at Default , scorecard development, model validation, population stability

End-to-end Basel II compliant credit risk modeling system with PD/LGD/EAD models, SHAP explainability, PyTorch neural network and PSI monitoring

Conducted fraud analytics on claims data with Basel II framing. Used PRIDIT + PCA (RIDITs) to surface suspicious claims without labels, demonstrating unsupervised detection methods for operational risk.