This case study aims to understand the driving factors (or driver variables) behind loan default, i.e. the variables which are strong indicators of default.

Developed a credit risk framework for GE’s lending team using CRISP-DM and k-NN modeling. Demonstrated how predictive analytics can reduce default losses and improve risk management through scalable, automated workflows.