Open Access Journal Article

Evaluating Classical and Artificial Intelligence Methods for Credit Risk Analysis

by Bruno Reis a orcid  and  António Quintino b,* orcid
a
Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
b
CEG-IST, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
*
Author to whom correspondence should be addressed.
JEA  2023, 35; 2(3), 35; https://doi.org/10.58567/jea02030006
Received: 19 March 2023 / Accepted: 9 May 2023 / Published Online: 31 May 2023
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Abstract

Credit scoring remains one of the most important subjects in financial risk management. Although the methods in this field have grown in sophistication, further improvements are necessary. These advances could translate in major gains for financial institutions and other companies that extend credit by diminishing the potential for losses in this process. This research seeks to compare statistical and artificial intelligence (AI) predictors in a credit risk analysis setting, namely the discriminant analysis, the logistic regression (LR), the artificial neural networks (ANNs), and the random forests. In order to perform this comparison, these methods are used to predict the default risk for a sample of companies that engage in trade credit. Pre-processing procedures are established, namely in the form of a proper sampling technique to assure the balance of the sample. Additionally, multicollinearity in the dataset is assessed via an analysis of the variance inflation factors (VIFs), and the presence of multivariate outliers is investigated with an algorithm based on robust Mahalanobis distances (MDs). After seeking the most beneficial architectures and/or settings for each predictor category, the final models are then compared in terms of several relevant key performance indicators (KPIs). The benchmarking analysis revealed that the artificial intelligence methods outperformed the statistical approaches.

Keywords: Credit scoring; artificial intelligence; discriminant analysis; logistic regression; artificial neural networks; random forest;
Copyright: © 2023 by Reis and Quintino. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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ACS Style
Reis, B.; Quintino, A. Evaluating Classical and Artificial Intelligence Methods for Credit Risk Analysis. Journal of Economic Analysis, 2023, 2, 35. https://doi.org/10.58567/jea02030006
AMA Style
Reis B, Quintino A. Evaluating Classical and Artificial Intelligence Methods for Credit Risk Analysis. Journal of Economic Analysis; 2023, 2(3):35. https://doi.org/10.58567/jea02030006
Chicago/Turabian Style
Reis, Bruno; Quintino, António 2023. "Evaluating Classical and Artificial Intelligence Methods for Credit Risk Analysis" Journal of Economic Analysis 2, no.3:35. https://doi.org/10.58567/jea02030006
APA style
Reis, B., & Quintino, A. (2023). Evaluating Classical and Artificial Intelligence Methods for Credit Risk Analysis. Journal of Economic Analysis, 2(3), 35. https://doi.org/10.58567/jea02030006

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