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

Yidi Liu (), Xin Li () and Zhiqiang (Eric) Zheng ()
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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

Information Systems Research, 2024, vol. 35, issue 2, 489-504

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.

Keywords: AI; natural disasters; lending; delinquency; credit scoring; fintech (search for similar items in EconPapers)
Date: 2024
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