2 citations · 2 across the 6 of their papers we have counts for
6 papers
: 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…
Tabular Foundation Models for Credit Risk Prediction: A Dedicated Review and Empirical Comparison
Bart Baesens, Andreas Goethals, Stefan Lessmann +10
Predictive models are central to credit risk management, where the accuracy of default-probability and loss estimates affects the profitability of lending and the stability of the…
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…
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…
Incorporating data drift to perform survival analysis on credit risk
Jianwei Peng, Stefan Lessmann
Survival analysis has become a standard approach for modelling time to default by time-varying covariates in credit risk. Unlike most existing methods that implicitly assume a stat…
Fighting Sampling Bias: A Framework for Training and Evaluating Credit Scoring Models
Nikita Kozodoi, Stefan Lessmann, Morteza Alamgir +2
Scoring models support decision-making in financial institutions. Their estimation and evaluation are based on the data of previously accepted applicants with known repayment behav…