A logistic regression model for consumer default risk

Eliana Costa E Silva; Isabel Cristina Lopes; Aldina Correia; Susana Faria

doi:10.1080/02664763.2020.1759030

A logistic regression model for consumer default risk

J Appl Stat. 2020 May 5;47(13-15):2879-2894. doi: 10.1080/02664763.2020.1759030. eCollection 2020.

Authors

Eliana Costa E Silva¹, Isabel Cristina Lopes², Aldina Correia¹, Susana Faria³

Affiliations

¹ CIICESI, ESTG, Politécnico do Porto, Felgueiras, Portugal.
² LEMA, CEOS.PP/ISCAP/P. Porto, Porto, Portugal.
³ Department of Mathematics, Centre of Molecular and Environmental Biology, University of Minho, Guimarães, Portugal.

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.

Keywords: 62-J-12; 62-P-05; 91-G-40; Generalized linear models logistic regression; applications to actuarial sciences and financial mathematics; credit scoring; default risk.

Grants and funding

This work has received funding from FEDER funds through P2020 program and from national funds through FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) under the project UID/GES/04728/2020.