3 papers
stat.ML2026
: Transparent and Fair Credit Risk Decisions through Semi-Structured Regressions
Victor Medina-Olivares, Stefan Lessmann, Jonathan Crook
Credit risk models increasingly need to combine predictive accuracy with transparent explanations and auditable fairness constraints. Logistic regression remains attractive because…
q-fin.RM2026
Transfer Learning for Loan Recovery Prediction under Distribution Shifts with Heterogeneous Feature Spaces
Christopher Gerling, Hanqiu Peng, Ying Chen +1
Accurate forecasting of recovery rates (RR) is central to credit risk management and regulatory capital determination. In many loan portfolios, however, RR modeling is constrained…
stat.AP2026
Semi-structured multi-state delinquency model for mortgage default
Victor Medina-Olivares, Wangzhen Xia, Stefan Lessmann +1
We propose a semi-structured discrete-time multi-state model to analyse mortgage delinquency transitions. This model combines an easy-to-understand structured additive predictor, w…