# The Fairness of Credit Scoring Models

arXiv: https://arxiv.org/abs/2205.10200 · HTML: https://mlsanity.com/paper/2205.10200/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 77 references that are in the map.
- First posted on arXiv 20 May 2022.

## Builds on (most cited first, 11 of 11)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [Fair prediction with disparate impact: A study of bias in recidivism prediction instruments](https://mlsanity.com/paper/1610.07524/) (2016)
- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [Fairness in Machine Learning: A Survey](https://mlsanity.com/paper/2010.04053/) (2020)
- [Fairness in Criminal Justice Risk Assessments: The State of the Art](https://mlsanity.com/paper/1703.09207/) (2017)
- [Recursive Partitioning for Heterogeneous Causal Effects](https://mlsanity.com/paper/1504.01132/) (2015)
- [Path-Specific Counterfactual Fairness](https://mlsanity.com/paper/1802.08139/) (2018)
- [Fairness in Credit Scoring: Assessment, Implementation and Profit Implications](https://mlsanity.com/paper/2103.01907/) (2021)
- [FAIR: Fair Adversarial Instance Re-weighting](https://mlsanity.com/paper/2011.07495/) (2020)

## Built on by (most cited first, 15 of 15)

- [Algorithmic decision making methods for fair credit scoring](https://mlsanity.com/paper/2209.07912/) (2022)
- [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)
- [Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring](https://mlsanity.com/paper/2212.05866/) (2022)
- [A Distributionally Robust Optimisation Approach to Fair Credit Scoring](https://mlsanity.com/paper/2402.01811/) (2024)
- [textttfindr: Transparent and Fair Credit Risk Decisions through Semi-Structured Regressions](https://mlsanity.com/paper/2608.24582/) (2026)
- [Walk a Mile in Their Shoes: a New Fairness Criterion for Machine Learning](https://mlsanity.com/paper/2210.06680/) (2022)
- [Advancing Fairness in Natural Language Processing: From Traditional Methods to Explainability](https://mlsanity.com/paper/2410.12511/) (2024)
- [Assistive AI for Augmenting Human Decision-making](https://mlsanity.com/paper/2410.14353/) (2024)
- [An Experimental Study on Fairness-aware Machine Learning for Credit Scoring Problems](https://mlsanity.com/paper/2412.20298/) (2024)
- [The influence of missing data mechanisms and simple missing data handling techniques on fairness](https://mlsanity.com/paper/2503.07313/) (2025)
- [Shapley meets Rawls: an integrated framework for measuring and explaining unfairness](https://mlsanity.com/paper/2603.26476/) (2026)
- [Demystifying the Optimal Fair Classifier in Multi-Class Classification](https://mlsanity.com/paper/2606.00656/) (2026)
- [A Rank Graduation metric for Algorithmic fairness](https://mlsanity.com/paper/2609.39025/) (2026)

## Abstract (arXiv)

In credit markets, screening algorithms aim to discriminate between good-type and bad-type borrowers. However, when doing so, they can also discriminate between individuals sharing a protected attribute (e.g. gender, age, racial origin) and the rest of the population. This can be unintentional and originate from the training dataset or from the model itself. We show how to formally test the algorithmic fairness of scoring models and how to identify the variables responsible for any lack of fairness. We then use these variables to optimize the fairness-performance trade-off. Our framework provides guidance on how algorithmic fairness can be monitored by lenders, controlled by their regulators, improved for the benefit of protected groups, while still maintaining a high level of forecasting accuracy.