Machine learning predictivity applied to consumer creditworthiness

Maisa Cardoso Aniceto (), Flavio Barboza () and Herbert Kimura
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Maisa Cardoso Aniceto: University of Brasília
Flavio Barboza: Federal University of Uberlandia

Future Business Journal, 2020, vol. 6, issue 1, 1-14

Abstract: Abstract Credit risk evaluation has a relevant role to financial institutions, since lending may result in real and immediate losses. In particular, default prediction is one of the most challenging activities for managing credit risk. This study analyzes the adequacy of borrower’s classification models using a Brazilian bank’s loan database, and exploring machine learning techniques. We develop Support Vector Machine, Decision Trees, Bagging, AdaBoost and Random Forest models, and compare their predictive accuracy with a benchmark based on a Logistic Regression model. Comparisons are analyzed based on usual classification performance metrics. Our results show that Random Forest and Adaboost perform better when compared to other models. Moreover, Support Vector Machine models show poor performance using both linear and nonlinear kernels. Our findings suggest that there are value creating opportunities for banks to improve default prediction models by exploring machine learning techniques.

Keywords: Machine learning; Credit risk; Consumer lending; Default prediction; Performance analysis (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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DOI: 10.1186/s43093-020-00041-w

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