A good sketch is better than a long speech: evaluate delinquency risk through real-time video analysis

Xiangyu Chang, Lili Dai, Lingbing Feng, Jianlei Han, Jing Shi and Bohui Zhang

Review of Finance, 2025, vol. 29, issue 2, 467-500

Abstract: This article proposes an innovative method to assess borrowers’ creditworthiness in consumer credit markets by conducting machine-learning-based analyses on real-time video information that records borrowers’ behavior during the loan application process. We find that the extent of borrowers’ micro-facial expressions of happiness is negatively associated with loan delinquency likelihood, while the degree of fear expressions is positively associated with delinquency risk. These results are consistent with two economic channels relating to the adequacy and uncertainty of borrowers’ future income, drawn from the extant psychology and economics literature. Our study provides important practical implications for fintech lenders and policymakers.

Keywords: loan delinquency risk; real-time data; video analysis; machine learning; fintech (search for similar items in EconPapers)
JEL-codes: D14 G14 G23 G51 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:oup:revfin:v:29:y:2025:i:2:p:467-500.

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