mlsanityData to 14 Oct 2026

Algorithmic decision making methods for fair credit scoring

arXiv 2209.07912 ↗First posted 16 Sep 2022cs.LG cs.AI cs.CYcited by 5

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Abstract (arXiv)

The effectiveness of machine learning in evaluating the creditworthiness of loan applicants has been demonstrated for a long time. However, there is concern that the use of automated decision-making processes may result in unequal treatment of groups or individuals, potentially leading to discriminatory outcomes. This paper seeks to address this issue by evaluating the effectiveness of 12 leading bias mitigation methods across 5 different fairness metrics, as well as assessing their accuracy and potential profitability for financial institutions. Through our analysis, we have identified the challenges associated with achieving fairness while maintaining accuracy and profitabiliy, and have highlighted both the most successful and least successful mitigation methods. Ultimately, our research serves to bridge the gap between experimental machine learning and its practical applications in the finance industry.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 24 of its 56 references that are in the map. First posted on arXiv 16 Sep 2022.

Builds on

24 of 56 references.

References · 24

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,007
  2. 2014Certifying and removing disparate impactcited by 715
  3. 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
  4. 2018A Reductions Approach to Fair Classificationcited by 587
  5. 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 586
  6. 2017On Fairness and Calibrationcited by 406
  7. 2017Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairnesscited by 384
  8. 2018A comparative study of fairness-enhancing interventions in machine learningcited by 208
  9. 2018Delayed Impact of Fair Machine Learningcited by 184
  10. 2022The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrinkcited by 184
  11. 2019Fair Regression: Quantitative Definitions and Reduction-based Algorithmscited by 168
  12. 2021A survey on datasets for fairness-aware machine learningcited by 157
12 more
  1. 2018Classification with Fairness Constraints: A Meta-Algorithm with Provable Guaranteescited by 146
  2. 2021A Clarification of the Nuances in the Fairness Metrics Landscapecited by 98
  3. 2018A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indicescited by 97
  4. 2021Fairness in Credit Scoring: Assessment, Implementation and Profit Implicationscited by 65
  5. 2019Causal Modeling for Fairness in Dynamical Systemscited by 47
  6. 2019Stable and Fair Classificationcited by 25
  7. 2022The Fairness of Credit Scoring Modelscited by 15
  8. 2020Fairness by Explicability and Adversarial SHAP Learningcited by 9
  9. 2019Dynamic fairness - Breaking vicious cycles in automatic decision makingcited by 5
  10. 2021Improving Fairness of AI Systems with Lossless De-biasingcited by 4
  11. 2021Unpacking the Black Box: Regulating Algorithmic Decisionscited by 3
  12. 2019Algorithmic and Economic Perspectives on Fairnesscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 1; 2026: 3.

5 papers.

  1. 2022Consistent Range Approximation for Fair Predictive Modelingcited by 7
  2. 2026Foundation Models for Credit Risk Prediction: A Game Changer?cited by 3
  3. 2026FSL-BDP: Federated Survival Learning with Bayesian Differential Privacy for Credit Risk Modelingcited by 2
  4. 2026Calibrated Credit Intelligence: Shift-Robust and Fair Risk Scoring with Bayesian Uncertainty and Gradient Boostingcited by 2
  5. 2025PDx -- Adaptive Credit Risk Forecasting Model in Digital Lending using Machine Learning Operationscited by 1