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Smart Natural Disaster Relief: Assisting Victims with Artificial Intelligence in Lending

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  • Yidi Liu

    (School of Management and Economics and Shenzhen Finance Institute, Chinese University of Hong Kong, Shenzhen, Shenzhen 518172, China)

  • Xin Li

    (Department of Information Systems, College of Business, City University of Hong Kong, Hong Kong)

  • Zhiqiang (Eric) Zheng

    (Department of Information Systems and Operations Management, Jindal School of Management, University of Texas at Dallas, Richardson, Texas 75080)

Abstract

Natural disasters wreak economic havoc and cause financial distress for victims. Commercial loans provided by lending firms play a key role in helping victims recover from disasters. This research note studies whether lenders’ use of artificial intelligence (AI) in the lending process can, through reducing delinquency, benefit borrowers who experience natural disasters. Collaborating with a leading credit-scoring company, we track borrowers’ loan applications and lenders’ use of customized AI solutions in assessing loan risks. We find that borrowers who apply to AI-empowered lenders fare better in reducing delinquency rates after experiencing natural disasters. Notably, such a disaster mitigation effect is more pronounced for borrowers with lower credit scores. We explore the possible mechanisms at play and discuss the implications of our findings.

Suggested Citation

  • Yidi Liu & Xin Li & Zhiqiang (Eric) Zheng, 2024. "Smart Natural Disaster Relief: Assisting Victims with Artificial Intelligence in Lending," Information Systems Research, INFORMS, vol. 35(2), pages 489-504, June.
    • Handle: RePEc:inm:orisre:v:35:y:2024:i:2:p:489-504
    DOI: 10.1287/isre.2023.1230
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    References listed on IDEAS

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    1. Ahmed Abbasi & Robin Dillon & H. Raghav Rao & Olivia R. Liu Sheng, 2024. "Preparedness and Response in the Century of Disasters: Overview of Information Systems Research Frontiers," Information Systems Research, INFORMS, vol. 35(2), pages 460-468, June.

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