Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/246646 
Year of Publication: 
2020
Citation: 
[Journal:] Future Business Journal [ISSN:] 2314-7210 [Volume:] 6 [Issue:] 1 [Publisher:] Springer [Place:] Heidelberg [Year:] 2020 [Pages:] 1-14
Publisher: 
Springer, Heidelberg
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.
Subjects: 
Consumer lending
Credit risk
Default prediction
Machine learning
Performance analysis
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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