Credit risk topic
Compare statistical and machine-learning approaches for risk modelling.
Learn how default, prepayment, and account closure compete in credit PD modeling. Compare cause-specific hazards, cumulative incidence, Kaplan–Meier, and Fine–Gray with a worked example, monitoring guidance, and runnable synthetic-data Python.
A practical workflow for rare-default modelling: define the target, compare class weighting and resampling without leakage, evaluate precision and recall at useful thresholds, and recalibrate PDs on representative data.
Choose model complexity based on use, sample size, data quality, and governance capacity.
Re-map predictions to observed event rates on separate, representative data.
Use event time when accounts have different follow-up or default risk changes over time.
Assess model outputs and impacts across groups while preserving a clear purpose and review trail.