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Modeling credit risk in credit unions using survival analysis

Modeling credit risk in credit unions using survival analysis PurposeThe purpose of this paper is to investigate proprietary data from customers of a Southern Louisiana credit union. It analyzes the factors that contribute to an accelerated failure time (AFT) using information from customers’ credit applications as well as information provided in their credit report.Design/methodology/approachThis paper investigates the factors that affect credit risk using survival analysis by employing two primary models – the AFT model and the Cox proportional hazard (PH) model. While several studies employ the Cox PH model, few use the AFT model. However, this paper concludes that the AFT model has superior predictive qualities.FindingsThis paper finds that the factors specific to borrowers and local factors play an important role in the duration of a loan.Practical implicationsThis paper offers an easily interpretable model for determining the duration of a potential borrower. The marketing department of credit unions can then use this information to predict when a customer will default, thus allowing the credit union to intervene in a timely manner to prevent defaults. Further, the credit union can use this information to seek out customers who are less likely to default.Originality/valueThis study is different from the previous research due to its focus on credit unions, which have distinct characteristics. Compared to similar lending institutions, the charter of the credit union does not allow management to sell off loans to other investors. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Bank Marketing Emerald Publishing

Modeling credit risk in credit unions using survival analysis

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
0265-2323
DOI
10.1108/IJBM-05-2017-0091
Publisher site
See Article on Publisher Site

Abstract

PurposeThe purpose of this paper is to investigate proprietary data from customers of a Southern Louisiana credit union. It analyzes the factors that contribute to an accelerated failure time (AFT) using information from customers’ credit applications as well as information provided in their credit report.Design/methodology/approachThis paper investigates the factors that affect credit risk using survival analysis by employing two primary models – the AFT model and the Cox proportional hazard (PH) model. While several studies employ the Cox PH model, few use the AFT model. However, this paper concludes that the AFT model has superior predictive qualities.FindingsThis paper finds that the factors specific to borrowers and local factors play an important role in the duration of a loan.Practical implicationsThis paper offers an easily interpretable model for determining the duration of a potential borrower. The marketing department of credit unions can then use this information to predict when a customer will default, thus allowing the credit union to intervene in a timely manner to prevent defaults. Further, the credit union can use this information to seek out customers who are less likely to default.Originality/valueThis study is different from the previous research due to its focus on credit unions, which have distinct characteristics. Compared to similar lending institutions, the charter of the credit union does not allow management to sell off loans to other investors.

Journal

International Journal of Bank MarketingEmerald Publishing

Published: May 8, 2018

References

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