Deep Learning Survival Analysis for Consumer Credit Risk Modelling

Great Through the course, I gained insights into how neural networks and time-to-event modelling enhance the assessment of borrower risk beyond static credit scoring approaches. The lessons emphasized the use of hazard functions, Kaplan-Meier estimations, and Cox proportional hazards models—adapted into deep learning frameworks—to estimate credit default timelines.

A particularly valuable component was learning to handle imbalanced data and censored information, common in consumer lending datasets. The hands-on exercises with Python and TensorFlow provided practical exposure to building survival models for predicting loan default and prepayment risks.