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$\texttt{findr}$: Transparent and Fair Credit Risk Decisions through Semi-Structured Regressions

arXiv · AI, language, vision and robotics · article · Aug 25, 2026 · UTC

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 a

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