findr：基于半结构化回归的透明公平信用风险决策

中文摘要 AI 辅助

信用风险模型日益需要兼具预测准确性、透明解释性与可审计的公平性约束。逻辑回归因系数易于解释仍具吸引力，但可能遗漏非线性结构；灵活模型可提升预测性能，但其解释常为事后生成，可能无法描述决策规则本身。本文提出findr（flexible, interpretable deep regression，即灵活可解释深度回归的缩写），这是一种用于二元信用风险建模的半结构化框架，将logit分解为可解释的结构化分量与正交神经残差。正交化将基于系数的效应与残差非线性变化分离，同时训练过程中通过Wasserstein惩罚项比较分数分布，缓解群体差异。该框架还包含诊断工具，可测量结构化分量对logit变化、决策一致性及局部方向一致性的贡献。本文在模拟研究与8个公开信用数据集上，利用分数级准确性-公平性前沿评估findr。结果显示，当信号近似线性时，findr表现接近逻辑回归；当存在非线性结构时，其可获得大部分神经模型的预测增益。诊断工具可识别基于系数的解释何时与完整拟合模型接近，以及何时必须同时检查残差变化。这些发现表明，半结构化建模是在信用风险决策中明确权衡性能、公平性与可解释性的实用方法。

英文摘要

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 residual nonlinear variation, while an in-processing Wasserstein penalty mitigates group disparities by comparing score distributions during training. The framework also includes diagnostics that measure the structured component's contribution to logit variation, decision agreement, and local directional consistency. We evaluate $\texttt{findr}$ in a simulation study and on eight public credit datasets using score-level accuracy-fairness frontiers. The results show that $\texttt{findr}$ behaves close to logistic regression when the signal is approximately linear, while recovering much of the predictive gain of neural models when nonlinear structure is relevant. The diagnostics identify when coefficient-based explanations remain close to the full fitted model and when residual variation must also be examined. These findings support semi-structured modelling as a practical way to make performance, fairness, and interpretability trade-offs explicit in credit risk decisions.

发表机构

• University of Edinburgh（爱丁堡大学）
• Humboldt-Universität zu Berlin（柏林洪堡大学）

机构由 AI 辅助整理，请以论文原文为准。