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Evaluating Classical and Artificial Intelligence Methods for Credit Risk Analysis

Author

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  • Bruno Reis

    (Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal)

  • António Quintino

    (CEG-IST, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal)

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.

Suggested Citation

  • Bruno Reis & António Quintino, 2023. "Evaluating Classical and Artificial Intelligence Methods for Credit Risk Analysis," Journal of Economic Analysis, Anser Press, vol. 2(3), pages 94-112, May.
    • Handle: RePEc:bba:j00001:v:2:y:2023:i:3:p:94-112:d:47
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    References listed on IDEAS

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    1. Lessmann, Stefan & Baesens, Bart & Seow, Hsin-Vonn & Thomas, Lyn C., 2015. "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, Elsevier, vol. 247(1), pages 124-136.
    2. Aleksandra Wójcicka, 2017. "Neural Networks in Credit Risk Classification of Companies in the Construction Sector," Econometric Research in Finance, SGH Warsaw School of Economics, Collegium of Economic Analysis, vol. 2(2), pages 63-77, December.
    3. Finlay, Steven, 2011. "Multiple classifier architectures and their application to credit risk assessment," European Journal of Operational Research, Elsevier, vol. 210(2), pages 368-378, April.
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