A Good Sketch is Better than A Long Speech: Evaluate Delinquency Risk through Real-Time Video Analysis
58 Pages Posted: 13 Dec 2023 Last revised: 13 Nov 2024
Date Written: December 10, 2023
Abstract
This paper 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
JEL Classification: D14, G14, G23, G51
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