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Time to Assess Bias in Machine Learning Models for Credit Decisions

Author

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  • Liming Brotcke

    (Model Validation Group, Ally Financial, Charlotte, NC 28202, USA)

Abstract

Focus on fair lending has become more intensified recently as bank and non-bank lenders apply artificial-intelligence (AI)-based credit determination approaches. The data analytics technique behind AI and machine learning (ML) has proven to be powerful in many application areas. However, ML can be less transparent and explainable than traditional regression models, which may raise unique questions about its compliance with fair lending laws. ML may also reduce potential for discrimination, by reducing discretionary and judgmental decisions. As financial institutions continue to explore ML applications in loan underwriting and pricing, the fair lending assessments typically led by compliance and legal functions will likely continue to evolve. In this paper, the author discusses unique considerations around ML in the existing fair lending risk assessment practice for underwriting and pricing models and proposes consideration of additional evaluations to be added in the present practice.

Suggested Citation

  • Liming Brotcke, 2022. "Time to Assess Bias in Machine Learning Models for Credit Decisions," JRFM, MDPI, vol. 15(4), pages 1-10, April.
    • Handle: RePEc:gam:jjrfmx:v:15:y:2022:i:4:p:165-:d:787449
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    References listed on IDEAS

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    1. Robert B. Avery & Kenneth P. Brevoort & Glenn Canner, 2012. "Does Credit Scoring Produce a Disparate Impact?," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 40, pages 65-114, December.
    2. John R. Walter, 1995. "The fair lending laws and their enforcement," Economic Quarterly, Federal Reserve Bank of Richmond, issue Fall, pages 61-77.
    3. Imai, Kosuke & Khanna, Kabir, 2016. "Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration Records," Political Analysis, Cambridge University Press, vol. 24(2), pages 263-272, April.
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    Cited by:

    1. Douglas Kiarelly Godoy de Araujo, 2023. "gingado: a machine learning library focused on economics and finance," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Data science in central banking: applications and tools, volume 59, Bank for International Settlements.
    2. Ardita TODRI & Petraq PAPAJORGJI, 2024. "Artificial Intelligence Waves In Financial Services Industry: An Evolution Factorial Analysis," Regional Science Inquiry, Hellenic Association of Regional Scientists, vol. 0(2), pages 63-75, June.
    3. Alex Chernoff & Gabriela Galassi, 2023. "Digitalization: Labour Markets," Discussion Papers 2023-16, Bank of Canada.
    4. Brygała Magdalena & Korol Tomasz, 2024. "Personal bankruptcy prediction using machine learning techniques," Economics and Business Review, Sciendo, vol. 10(2), pages 118-142.

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