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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…
stat.ML2026
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…
stat.ML2024★ 2 cited
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…