# textttfindr: Transparent and Fair Credit Risk Decisions through Semi-Structured Regressions

arXiv: https://arxiv.org/abs/2608.24582 · HTML: https://mlsanity.com/paper/2608.24582/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 52 references that are in the map.
- First posted on arXiv 25 Aug 2026.

## Builds on (most cited first, 28 of 28)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Computational Optimal Transport](https://mlsanity.com/paper/1803.00567/) (2018)
- [Fair prediction with disparate impact: A study of bias in recidivism prediction instruments](https://mlsanity.com/paper/1610.07524/) (2016)
- [A Reductions Approach to Fair Classification](https://mlsanity.com/paper/1803.02453/) (2018)
- [Neural Additive Models: Interpretable Machine Learning with Neural Nets](https://mlsanity.com/paper/2004.13912/) (2020)
- [Distilling a Neural Network Into a Soft Decision Tree](https://mlsanity.com/paper/1711.09784/) (2017)
- [Statistical Aspects of Wasserstein Distances](https://mlsanity.com/paper/1806.05500/) (2018)
- [Wasserstein Fair Classification](https://mlsanity.com/paper/1907.12059/) (2019)
- [Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey](https://mlsanity.com/paper/2207.07068/) (2022)
- [Fair Mixup: Fairness via Interpolation](https://mlsanity.com/paper/2103.06503/) (2021)
- [NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning](https://mlsanity.com/paper/2106.01613/) (2021)
- [GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactions](https://mlsanity.com/paper/2003.07132/) (2020)
- [Robust Optimization for Fairness with Noisy Protected Groups](https://mlsanity.com/paper/2002.09343/) (2020)
- [Bias Mitigation Post-processing for Individual and Group Fairness](https://mlsanity.com/paper/1812.06135/) (2018)
- [Fairness in Credit Scoring: Assessment, Implementation and Profit Implications](https://mlsanity.com/paper/2103.01907/) (2021)
- [A Distributionally Robust Approach to Fair Classification](https://mlsanity.com/paper/2007.09530/) (2020)
- [Semi-Structured Distributional Regression -- Extending Structured Additive Models by Arbitrary Deep Neural Networks and Data Modalities](https://mlsanity.com/paper/2002.05777/) (2020)
- [The value of text for small business default prediction: A deep learning approach](https://mlsanity.com/paper/2003.08964/) (2020)
- [Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Mean](https://mlsanity.com/paper/2301.11862/) (2023)
- [The Fairness of Credit Scoring Models](https://mlsanity.com/paper/2205.10200/) (2022)
- [Considerations When Learning Additive Explanations for Black-Box Models](https://mlsanity.com/paper/1801.08640/) (2018)
- [A transformer-based model for default prediction in mid-cap corporate markets](https://mlsanity.com/paper/2111.09902/) (2021)
- [Structural Neural Additive Models: Enhanced Interpretable Machine Learning](https://mlsanity.com/paper/2302.09275/) (2023)
- [The Deep Promotion Time Cure Model](https://mlsanity.com/paper/2305.11575/) (2023)
- [Fair Models in Credit: Intersectional Discrimination and the Amplification of Inequity](https://mlsanity.com/paper/2308.02680/) (2023)
- [Foundation Models for Credit Risk Prediction: A Game Changer?](https://mlsanity.com/paper/2605.18147/) (2026)
- [Decision-centric fairness: Evaluation and optimization for resource allocation problems](https://mlsanity.com/paper/2504.20642/) (2025)
- [Semi-structured multi-state delinquency model for mortgage default](https://mlsanity.com/paper/2603.26309/) (2026)

## Built on by (most cited first, 1 of 1)

- [PaMIR: Open Benchmark of Public Credit-Default Datasets](https://mlsanity.com/paper/2610.03259/) (2026)

## Abstract (arXiv)

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 textttfindr, 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 textttfindr in a simulation study and on eight public credit datasets using score-level accuracy-fairness frontiers. The results show that textttfindr 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.