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.
Highlights
Consumer spending is one of the main drivers of macroeconomic conditions and systemic risk [15]
Based on real-world data, we developed models based on machine learning techniques to predict default in a credit line and compare the performance of these models with logit, usually applied to this
According to Lantz [17], the C5.0 algorithm has become the industry standard for Decision Trees, generating good results for most types of problems when compared to other advanced machine learning models
Summary
Consumer spending is one of the main drivers of macroeconomic conditions and systemic risk [15]. Pl awiak et al, and Twala [20, 25, 30] established that credit risk assessment is an important issue in financial risk management, because banks should make important decisions about whether or not make a loan to a counterparty. In this context, Assef et al [1] suggest that one of the main problems in finance involves the prediction of bankruptcy or default.
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