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The Cohort Shapley value to measure fairness in financing small and medium enterprises in the UK

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  • Lu, Xuefei
  • Calabrese, Raffaella

Abstract

Banks are relying on machine learning techniques to support their decisions in financing small and medium enterprises (SMEs). As regulators require that credit decisions are transparent, there is a need to develop methods to measure fairness. We propose a weighted average of the Cohort Shapley value, which removes impossible feature combinations, and a relative fairness score for assessing the fairness level within sub-populations. Based on our knowledge, this is the first paper that investigates the fairness of UK financial institutions in providing funding to SMEs. Our findings reveal discrimination against start-up, micro, women-led companies, and owners of Asian ethnic backgrounds.

Suggested Citation

  • Lu, Xuefei & Calabrese, Raffaella, 2023. "The Cohort Shapley value to measure fairness in financing small and medium enterprises in the UK," Finance Research Letters, Elsevier, vol. 58(PC).
    • Handle: RePEc:eee:finlet:v:58:y:2023:i:pc:s1544612323009145
    DOI: 10.1016/j.frl.2023.104542
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    References listed on IDEAS

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    Cited by:

    1. Shiqi Fang & Zexun Chen & Jake Ansell, 2024. "Peer-induced Fairness: A Causal Approach for Algorithmic Fairness Auditing," Papers 2408.02558, arXiv.org, revised Sep 2024.

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