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Hidden Bias? Examining Gender Discrimination in Credit Scoring with AI Models versus Traditional Methods

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  • Stefania Stancu

Abstract

This study aims to investigate the impact of using Artificial Intelligence and Machine Learning on gender bias in credit scoring models by comparing advanced estimation techniques (Random Forest, Support Vector Machine, Artificial Neural Networks) with traditional methods (logistic regression). As AI-based credit scoring systems become widespread, concerns about transparency, fairness, and potential discrimination arise, especially regarding sensitive attributes like gender. Using data from the National Bank of Romania's Credit Risk Register, this study spans a seven-year period, offering an empirical analysis of potential biases in mortgage lending. Findings indicate that, while ML models provide enhanced predictive power, they vary in fairness. Random Forest emerges as the most accurate and least discriminatory model, underscoring the need for careful model selection to ensure equitable credit decisions.

Suggested Citation

  • Stefania Stancu, 2025. "Hidden Bias? Examining Gender Discrimination in Credit Scoring with AI Models versus Traditional Methods," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 17(2), pages 123-138, December.
    • Handle: RePEc:rfb:journl:v:17:y:2025:i:2:p:123-138
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    References listed on IDEAS

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    1. Christophe Hurlin & Christophe Pérignon & Sébastien Saurin, 2026. "The Fairness of Credit Scoring Models," Management Science, INFORMS, vol. 72(1), pages 406-425, January.
    2. Will Dobbie & Andres Liberman & Daniel Paravisini & Vikram Pathania, 2021. "Measuring Bias in Consumer Lending [Loan Prospecting and the Loss of Soft Information]," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 88(6), pages 2799-2832.
    3. Alberto F. Alesina & Francesca Lotti & Paolo Emilio Mistrulli, 2013. "Do Women Pay More For Credit? Evidence From Italy," Journal of the European Economic Association, European Economic Association, vol. 11, pages 45-66, January.
    4. Kim, Hong Sik & Sohn, So Young, 2010. "Support vector machines for default prediction of SMEs based on technology credit," European Journal of Operational Research, Elsevier, vol. 201(3), pages 838-846, March.
    5. Robert Bartlett & Adair Morse & Richard Stanton & Nancy Wallace, 2019. "Consumer-Lending Discrimination in the FinTech Era," NBER Working Papers 25943, National Bureau of Economic Research, Inc.
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