The profitability of online loans: A competing risks analysis on default and prepayment

Zhiyong Li, Aimin Li, Anthony Bellotti and Xiao Yao

European Journal of Operational Research, 2023, vol. 306, issue 2, 968-985

Abstract: Traditional credit scoring models help lenders to make informed decisions in identifying those borrowers most likely to default. We analyse over one million online loans and find that the rates for both default and prepayment are relatively high compared to traditional bank loans. A preliminary nonparametric life-table estimate shows that loans with different terms exhibit varying patterns of hazards. We use a proportional hazard model with competing risks to predict the time to default and prepayment, and parameterise those covariates affecting the time to both events. Two dimensions of predictive performance, the discriminant power and the probability calibration, are then examined. To further support the primacy of profit-driven decisions, we propose a framework based on competing risks survival analysis to estimate the profitability of loans and the return of loan portfolios. Profitability forecasts incorporating both the time to default and prepayment are compared to the profitability of real assets, and finally a penalty is suggested to compensate for those losses incurred by prepayment.

Keywords: OR in banking; Competing risks; Credit scoring; Profitability; Survival analysis (search for similar items in EconPapers)
JEL-codes: C34 D8 G17 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:306:y:2023:i:2:p:968-985

DOI: 10.1016/j.ejor.2022.08.013

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