A logistic regression model for consumer default risk

Eliana Costa e Silva · Aldina Correia · Susana Faria

57 citations0/0 files storedJournal of Applied Statistics

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Financial Distress and Bankruptcy Prediction Statistical Methods and Applications Credit Risk and Financial Regulations

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.

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References

2000A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers 2007Recent developments in consumer credit risk assessment 2015Key Determinants of Non-performing Loans: New Evidence from a Global Sample 2001Default probabilities in a corporate bank portfolio: A logistic model approach 1999Predicting a corporate credit analyst's risk estimate by logistic and linear models

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