$\texttt{findr}$: Transparent and Fair Credit Risk Decisions through Semi-Structured Regressions
Credit risk models increasingly need to combine predictive accuracy with transparent explanations and auditable fairness constraints. Logistic regression remains attractive because its coefficients are easy to interpret, but it can miss nonlinear structure. Flexible models can improve prediction, but their explanations are often post-hoc and may not describe the decision rule itself. We introduce $\texttt{findr}$, short for flexible, interpretable deep regression, a semi-structured framework for binary credit risk modelling that decomposes the logit into an interpretable structured component and an orthogonal neural residual. The orthogonalisation separates coefficient-based effects from ...
| Authors | Victor Medina-Olivares, Stefan Lessmann, Jonathan Crook |
|---|---|
| Primary category | stat.ML |
| Published | 25 August 2026 |
| Last updated at source | 25 August 2026 |
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