# Fairness in Credit Scoring: Assessment, Implementation and Profit Implications

arXiv: https://arxiv.org/abs/2103.01907 · HTML: https://mlsanity.com/paper/2103.01907/

## Key facts

- Cited by 65 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 49 references that are in the map.
- First posted on arXiv 2 Mar 2021.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [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)
- [Mitigating Unwanted Biases with Adversarial Learning](https://mlsanity.com/paper/1801.07593/) (2018)
- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [On Fairness and Calibration](https://mlsanity.com/paper/1709.02012/) (2017)
- [Algorithmic decision making and the cost of fairness](https://mlsanity.com/paper/1701.08230/) (2017)
- [Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment](https://mlsanity.com/paper/1610.08452/) (2016)
- [Fairness in Criminal Justice Risk Assessments: The State of the Art](https://mlsanity.com/paper/1703.09207/) (2017)
- [The Variational Fair Autoencoder](https://mlsanity.com/paper/1511.00830/) (2015)
- [A comparative study of fairness-enhancing interventions in machine learning](https://mlsanity.com/paper/1802.04422/) (2018)
- [Delayed Impact of Fair Machine Learning](https://mlsanity.com/paper/1803.04383/) (2018)
- [Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees](https://mlsanity.com/paper/1806.06055/) (2018)
- [Satisfying Real-world Goals with Dataset Constraints](https://mlsanity.com/paper/1606.07558/) (2016)

## Built on by (most cited first, 30 of 65)

- [Enforcing Group Fairness in Algorithmic Decision Making: Utility Maximization Under Sufficiency](https://mlsanity.com/paper/2206.02237/) (2022)
- [Algorithmic Fairness in Business Analytics: Directions for Research and Practice](https://mlsanity.com/paper/2207.10991/) (2022)
- [The Fairness of Credit Scoring Models](https://mlsanity.com/paper/2205.10200/) (2022)
- [A Short Survey on Importance Weighting for Machine Learning](https://mlsanity.com/paper/2403.10175/) (2024)
- [A Fused Large Language Model for Predicting Startup Success](https://mlsanity.com/paper/2409.03668/) (2024)
- [Fair Off-Policy Learning from Observational Data](https://mlsanity.com/paper/2303.08516/) (2023)
- [Explainability in Practice: A Survey of Explainable NLP Across Various Domains](https://mlsanity.com/paper/2502.00837/) (2025)
- [fairmodels: A Flexible Tool For Bias Detection, Visualization, And Mitigation](https://mlsanity.com/paper/2104.00507/) (2021)
- [Survey on Fairness Notions and Related Tensions](https://mlsanity.com/paper/2209.13012/) (2022)
- [Equalizing Credit Opportunity in Algorithms: Aligning Algorithmic Fairness Research with U.S. Fair Lending Regulation](https://mlsanity.com/paper/2210.02516/) (2022)
- [FinTrust: A Comprehensive Benchmark of Trustworthiness Evaluation in Finance Domain](https://mlsanity.com/paper/2510.15232/) (2025)
- [Bias and unfairness in machine learning models: a systematic literature review](https://mlsanity.com/paper/2202.08176/) (2022)
- [Understanding Unfairness in Fraud Detection through Model and Data Bias Interactions](https://mlsanity.com/paper/2207.06273/) (2022)
- [Consistent Range Approximation for Fair Predictive Modeling](https://mlsanity.com/paper/2212.10839/) (2022)
- [Algorithmic decision making methods for fair credit scoring](https://mlsanity.com/paper/2209.07912/) (2022)
- [Algorithmic Censoring in Dynamic Learning Systems](https://mlsanity.com/paper/2305.09035/) (2023)
- [Navigating Fairness Measures and Trade-Offs](https://mlsanity.com/paper/2307.08484/) (2023)
- [Overcoming Fairness Trade-offs via Pre-processing: A Causal Perspective](https://mlsanity.com/paper/2501.14710/) (2025)
- [Retiring DeltaDP: New Distribution-Level Metrics for Demographic Parity](https://mlsanity.com/paper/2301.13443/) (2023)
- [A Catalog of Fairness-Aware Practices in Machine Learning Engineering](https://mlsanity.com/paper/2408.16683/) (2024)
- [Uplift modeling with continuous treatments: A predict-then-optimize approach](https://mlsanity.com/paper/2412.09232/) (2024)
- [Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings](https://mlsanity.com/paper/2505.22356/) (2025)
- [On the Vulnerability of Fairness Constrained Learning to Malicious Noise](https://mlsanity.com/paper/2307.11892/) (2023)
- [Fair Models in Credit: Intersectional Discrimination and the Amplification of Inequity](https://mlsanity.com/paper/2308.02680/) (2023)
- [Standardized Interpretable Fairness Measures for Continuous Risk Scores](https://mlsanity.com/paper/2308.11375/) (2023)
- [Counter-Empirical Attacking based on Adversarial Reinforcement Learning for Time-Relevant Scoring System](https://mlsanity.com/paper/2311.05144/) (2023)
- [A Comprehensive Survey and Classification of Evaluation Criteria for Trustworthy Artificial Intelligence](https://mlsanity.com/paper/2410.17281/) (2024)
- [Engineering Trustworthy AI: A Developer Guide for Empirical Risk Minimization](https://mlsanity.com/paper/2410.19361/) (2024)
- [Enabling Group Fairness in Graph Unlearning via Bi-level Debiasing](https://mlsanity.com/paper/2505.09702/) (2025)
- [Foundation Models for Credit Risk Prediction: A Game Changer?](https://mlsanity.com/paper/2605.18147/) (2026)

## Abstract (arXiv)

The rise of algorithmic decision-making has spawned much research on fair machine learning (ML). Financial institutions use ML for building risk scorecards that support a range of credit-related decisions. Yet, the literature on fair ML in credit scoring is scarce. The paper makes three contributions. First, we revisit statistical fairness criteria and examine their adequacy for credit scoring. Second, we catalog algorithmic options for incorporating fairness goals in the ML model development pipeline. Last, we empirically compare different fairness processors in a profit-oriented credit scoring context using real-world data. The empirical results substantiate the evaluation of fairness measures, identify suitable options to implement fair credit scoring, and clarify the profit-fairness trade-off in lending decisions. We find that multiple fairness criteria can be approximately satisfied at once and recommend separation as a proper criterion for measuring the fairness of a scorecard. We also find fair in-processors to deliver a good balance between profit and fairness and show that algorithmic discrimination can be reduced to a reasonable level at a relatively low cost. The codes corresponding to the paper are available on GitHub.