A logistic regression model for consumer default risk

Eliana Costa e Silva, Isabel Cristina Lopes, Aldina Correia and Susana Faria

Journal of Applied Statistics, 2020, vol. 47, issue 13-15, 2879-2894

Abstract: In this study, a logistic regression model is applied to credit scoring data from a given Portuguese financial institution to evaluate the default risk of consumer loans. It was found that the risk of default increases with the loan spread, loan term and age of the customer, but decreases if the customer owns more credit cards. Clients receiving the salary in the same banking institution of the loan have less chances of default than clients receiving their salary in another institution. We also found that clients in the lowest income tax echelon have more propensity to default. The model predicted default correctly in 89.79% of the cases.

Date: 2020
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DOI: 10.1080/02664763.2020.1759030

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