Credit Risk Assessment Model

Developed a comprehensive credit risk model incorporating traditional credit bureau data with alternative data sources including payment history, social media activity, and behavioral patterns. Used gradient boosting algorithms to predict default probability with 89% accuracy. Enabled the bank to expand lending to underserved segments while maintaining acceptable risk levels.