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Human-AI collaboration to mitigate decision noise in financial underwriting: A study on FinTech innovation in a lending firm

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  • Sachan, Swati
  • Almaghrabi, Fatima
  • Yang, Jian-Bo
  • Xu, Dong-Ling

Abstract

Financial institutions have recognized the value of collaborating human expertise and AI to create high-performance augmented decision-support systems. Stakeholders at lending firms have increasingly acknowledged that plugging data into AI algorithms and eliminating the role of human underwriters by automation, with the expectation of immediate returns on investment from business process automation, is a flawed strategy. This research emphasizes the necessity of auditing the consistency of decisions (or professional judgment) made by human underwriters and monitoring the ability of data to capture the lending policies of a firm to lay a strong foundation for a legitimate system before investing millions in AI projects. The judgments made by experts in the past re-emerge in the future as outcomes or labels in the data used to train and evaluate algorithms. This paper presents Evidential Reasoning-eXplainer, a methodology to estimate probability mass as an extent of support for a given decision on a loan application by jointly assessing multiple independent and conflicting pieces of evidence. It quantifies variability in past decisions by comparing the subjective judgments of underwriters during manual financial underwriting with outcomes estimated from data. The consistency analysis improves decision quality by bridging the gap between past inconsistent decisions and desired ultimate-true decisions. A case study on a specialist lending firm demonstrates the strategic work plan adapted to configure underwriters and developers to capture the correct data and audit the quality of decisions.

Suggested Citation

  • Sachan, Swati & Almaghrabi, Fatima & Yang, Jian-Bo & Xu, Dong-Ling, 2024. "Human-AI collaboration to mitigate decision noise in financial underwriting: A study on FinTech innovation in a lending firm," International Review of Financial Analysis, Elsevier, vol. 93(C).
    • Handle: RePEc:eee:finana:v:93:y:2024:i:c:s1057521924000814
    DOI: 10.1016/j.irfa.2024.103149
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    References listed on IDEAS

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    1. Andreas Fuster & Matthew Plosser & Philipp Schnabl & James Vickery, 2019. "The Role of Technology in Mortgage Lending," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 1854-1899.
    2. Zoe M. Boundy-Singer & Corey M. Ziemba & Robbe L. T. Goris, 2023. "Confidence reflects a noisy decision reliability estimate," Nature Human Behaviour, Nature, vol. 7(1), pages 142-154, January.
    3. Kowalewski, Oskar & Pisany, Paweł, 2022. "Banks' consumer lending reaction to fintech and bigtech credit emergence in the context of soft versus hard credit information processing," International Review of Financial Analysis, Elsevier, vol. 81(C).
    4. Akter, Shahriar & Hossain, Md Afnan & Sajib, Shahriar & Sultana, Saida & Rahman, Mahfuzur & Vrontis, Demetris & McCarthy, Grace, 2023. "A framework for AI-powered service innovation capability: Review and agenda for future research," Technovation, Elsevier, vol. 125(C).
    5. Chee Kian Leong, 2016. "Credit Risk Scoring with Bayesian Network Models," Computational Economics, Springer;Society for Computational Economics, vol. 47(3), pages 423-446, March.
    6. Wu, Chunchi & Wang, Xu-Ming, 2000. "A Neural Network Approach for Analyzing Small Business Lending Decisions," Review of Quantitative Finance and Accounting, Springer, vol. 15(3), pages 259-276, November.
    7. Lei Chen & Ling Diao & Jun Sang, 2018. "Weighted Evidence Combination Rule Based on Evidence Distance and Uncertainty Measure: An Application in Fault Diagnosis," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-10, January.
    8. Dominik Dellermann & Philipp Ebel & Matthias Söllner & Jan Marco Leimeister, 2019. "Hybrid Intelligence," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(5), pages 637-643, October.
    9. Almansour, Mohammed, 2023. "Artificial intelligence and resource optimization: A study of Fintech start-ups," Resources Policy, Elsevier, vol. 80(C).
    Full references (including those not matched with items on IDEAS)

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