Survival Analysis and ROC Analysis in Analyzing Credit Risks: Assessing Default Risks Over Time

Survival Analysis and ROC Analysis in Analyzing Credit Risks: Assessing Default Risks Over Time

Nan Hu, Haojie Cheng
Copyright: © 2018 |Pages: 24
DOI: 10.4018/978-1-5225-3932-2.ch007
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Abstract

As the aim of large banks has been changing to select customers of highest benefits, it is important for banks to know not only if but also when a customer will default. Survival analyses have been used to estimate over time risk of default or early payoff, two major risks for banks. The major benefit of this method is that it can easily handle censoring and competing risks. An ROC curve, as a statistical tool, was applied to evaluate credit scoring systems. Traditional ROC analyses allow banks to evaluate if a credit-scoring system can correctly classify customers based on their cross-sectional default status, but will fail when assessing a credit-scoring system at a series of future time points, especially when there are censorings or competing risks. The time-dependent ROC analysis was introduced by Hu and Zhou to evaluate credit-scoring systems in a time-varying fashion and it allows us to assess credit scoring systems for predicting default by any time within study periods.
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Background

The primary goals of risk analysis for the credit industry are to monitor the credit risks and to decide whether it should grant credit to an applicant. Traditionally, it is done by estimating the probability that the applicant will eventually default (Stepanova & Thomas 2002). More recently, however, the aim of banks and credit companies has been changing to select customers of highest benefits. This changes implies that it is important not only if but also when a customer will default in a future time (Banasik et al. 1999). It is possible that if the time to default is long, the acquired interest from the customer will compensate or even exceed the losses resulting from default. On the other hand, an early pay-off by the customer can also impact the profitability of financial businesses. Depending on when the actual repayment occurs, the lender will lose a proportion of interest in the loan. It has been shown that survival analysis can be applied to estimate time to default or early repayment (Narian 1992, Banasik et al. 1999).

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