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 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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References

  1. Abdou, H. A., & Pointon, J. (2011). Credit scoring, statistical techniques and evaluation criteria: a review of the literature. Intelligent Systems in Accounting, Finance and Management, 18, 59–88. https://doi.org/10.1002/isaf.325
  2. Addo, P. M., Guegan, D., & Hassani, B. (2018). Credit Risk Analysis Using Machine and Deep Learning Models. Risks, 6(2):38. https://doi.org/10.3390/risks6020038
  3. Aguilera, A., Escabias, M., & Valderrama, M. (2006). Using principal components for estimating logistic regression with high-dimensional multicollinear data. Computational Statistics & Data Analysis, 50, 1905-1924. https://doi.org/10.1016/j.csda.2005.03.011
  4. Altman, E. I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance, 23, 589-609. https://doi.org/10.2307/2978933
  5. Angelini, E., di Tollo, G., & Roli, A. (2008). A neural network approach for credit risk evaluation. Quarterly Review of Economics and Finance, 48, 733–755. https://doi.org/10.1016/j.qref.2007.04.001
  6. Archer, K., & Kimes, R. (2008). Empirical characterization of random forest variable importance measures. Computational Statistics & Data Analysis, 52, 2249-2260. https://doi.org/10.1016/j.csda.2007.08.015
  7. Ayala, H., & Coelho, L. (2016). Cascaded evolutionary algorithm for nonlinear system identification based on correlation functions and radial basis functions neural networks. Mechanical Systems and Signal Processing, 68, 378–393. https://doi.org/10.1016/j.ymssp.2015.05.022
  8. Baesens, B., Setiono, R., Mues, C., & Vanthienen, J. (2003). Using Neural Network Rule Extraction andDecision Tables for Credit-Risk Evaluation. Management Science, 49, 312-329. https://doi.org/10.1287/mnsc.49.3.312.12739
  9. Barnett, V. & Lewis, T. (1994). Outliers in Statistical Data (3rd ed.). Chichester, UK: Wiley
  10. Baser, F., Koc, O., & Selcuk-Kestel, A. (2023). Credit risk evaluation using clustering based fuzzy classification method. Expert Systems with Applications, 223. https://doi.org/10.1016/j.eswa.2023.119882
  11. Batista, A. (2012). Credit Scoring – Uma ferramenta de gestão financeira. Porto, Portugal: Vida Económica.
  12. Beliakov, G., Kelarev, A., & Yearwood, J. (2011). Robust artificial neural networks and outlier detection. Technical report.
  13. Breiman, L. (1996). Bagging Predictors. Machine Learning, 24, 123-140. https://doi.org/10.1007/BF00058655
  14. Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32. https://doi.org/10.1023/A:1010933404324
  15. Brereton, R., & Lloyd (2016). Re-evaluating the role of the Mahalanobis distance measure. Journal of Chemometrics, 30, 134-143. https://doi.org/10.1002/cem.2779
  16. Bryll, R., Gutierrez-Osuna, R., & Quek, F. (2003). Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets. Pattern Recognition, 36, 1291-1302. https://doi.org/10.1016/S0031-3203(02)00121-8
  17. Chen, X., Wang, D., Liu, Z., & Wu, Y. (2018). A Fast Direct Position Determination for Multiple Sources Based on Radial Basis Function Neural Network. 10th International Conference on Communication Software and Networks (ICCSN), 381-385.
  18. Craney, T., & Surles, J. (2002). Model-Dependent Variance Inflation Factor Cutoff Values. Quality Engineering, 14, 391-403. https://doi.org/10.1081/QEN-120001878
  19. Crone, S., & Finlay, F. (2012). Instance sampling in credit scoring: An empirical study of sample size and balancing. International Journal of Forecasting, 28, 224-238. https://doi.org/10.1016/j.ijforecast.2011.07.006
  20. Dawoud, I., Awwad, F., Tageldin, E., & Abonazel, M. (2022). New Robust Estimators for Handling Multicollinearity and Outliers in the Poisson Model: Methods, Simulation and Applications. Axioms, 11. https://doi.org/10.3390/axioms11110612
  21. Dietterich, T. (2000). An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization. Machine Learning, 40, 139-157. https://doi.org/10.1023/A:1007607513941
  22. Dumitrescu, E., Hué, S., Hurlin, C., & Tokpavi, S. 2022. Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects. European Journal of Operational Research, 297(3), 1178-1192. https://doi.org/10.1016/j.ejor.2021.06.053
  23. Fabbri, D., & Menichini, A. (2010). Trade credit, collateral liquidation and borrowing constraints. Journal of Financial Economics, 96, 413-432. https://doi.org/10.1016/j.jfineco.2010.02.010
  24. Filzmoser, P. (2004). A multivariate outlier detection method. Proceedings of the Seventh International Conference on Computer Data Analysis and Modeling, 1, 18-22.
  25. Finlay, S. (2011). Multiple classifier architectures and their application to credit risk assessment. European Journal of Operational Research, 210, 368-378. https://doi.org/10.1016/j.ejor.2010.09.029
  26. Fletcher, P., Venkatasubramanian, S., & Joshi, S. (2008). 2008 IEEE Conference on Computer Vision and Pattern Recognition.
  27. Grubbs, F. (1969). Procedures for Detecting Outlying Observations in Samples. Technometrics ,11(1), 1-21. https://doi.org/10.1080/00401706.1969.10490657
  28. Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The elements of statistical learning: data mining,inference, and prediction (2nd ed.). New York, USA: Springer
  29. Huang, X., Liu, X., & Ren, Y. (2018). Enterprise credit risk evaluation based on neural network algorithm. Cognitive Systems Research, 52, 317–324. https://doi.org/10.1016/j.cogsys.2018.07.023
  30. Huang, Z., Chen, H., Hsu, C. J., Chen, W. H., & Wu, S. (2004). Credit rating analysis with support vector machines and neural networks: A market comparative study. Decision Support Systems, 37,543–558. https://doi.org/10.1016/S0167-9236(03)00086-1
  31. Jones, S., Johnstone, D., & Wilson, R. (2015). An empirical evaluation of the performance of binary classifiers in the prediction of credit ratings changes. Journal of Banking and Finance, 56, 72–85. https://doi.org/10.1016/j.jbankfin.2015.02.006
  32. Khashman, A. (2010). Neural networks for credit risk evaluation: Investigation of different neural models and learning schemes. Expert Systems with Applications, 37, 6233–6239. https://doi.org/10.1016/j.eswa.2010.02.101
  33. Khemakhem, S., & Boujelbènea, Y. (2015). Credit risk prediction: A comparative study between discriminant analysis and the neural network approach. Accounting and Management Information Systems, 14(1), 60–78.
  34. Kvamme, H., Sellereite, N., Aas, K., & Sjursen, S. (2018). Predicting mortgage default using convolutional neural networks. Expert Systems with Applications, 102, 207–217. https://doi.org/10.1016/j.eswa.2018.02.029
  35. Lai, K., Yu, L., Wang, S., & Zhou, L. (2006). Credit risk analysis using a reliability-based neural network ensemble model. Artificial Neural Networks – ICANN 2006, 682–690. https://doi.org/10.1007/11840930_71
  36. Lee, T. S., Chiu, C. C., Lu, C. J., & Chen, I. F. (2002). Credit scoring using the hybrid neural discriminant technique. Expert Systems with Applications, 23(3), 245–254. https://doi.org/10.1016/S0957-4174(02)00044-1
  37. Lessmann, S., Baesens, B., Seow, H., & Thomas, L. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research. European Journal of Operational Research, 247,124-136. https://doi.org/10.1016/j.ejor.2015.05.030
  38. Leys, C., Klein, O., Dominicy, Y., & Ley, C (2018). Detecting multivariate outliers: Use a robust variant of the Mahalanobis distance. Journal of Experimental Social Psychology, 74, 150-156. https://doi.org/10.1016/j.jesp.2017.09.011
  39. MathWorks. Detect outliers in multivariate datasets. (2019). https://www.mathworks.com/matlabcentral/fileexchange/65817-detect-outliers-in-multivaraite-datasets Accessed 24 September 2019.
  40. Ong, C. S., Huang, J. J., & Tzeng, G. H. (2005). Building credit scoring models using genetic programming. Expert Systems with Applications, 29, 41-47. https://doi.org/10.1016/j.eswa.2005.01.003
  41. Pacelli, V., & Azzollini, M. (2011). An Artificial Neural Network Approach for Credit Risk Management. Journal of Intelligent LearningSystems and Applications, 3, 103–112.
  42. Paleologo, G., Elisseeff, A., & Antonini, G. (2010). Subagging for credit scoring models. European Journal of Operational Research, 201, 490-499. https://doi.org/10.1016/j.ejor.2009.03.008
  43. Press, S., & Wilson, S. (1978). Choosing Between Logistic Regression and Discriminant Analysis. Journal of the American Statistical Association, 73, 699-705. https://doi.org/10.1080/01621459.1978.10480080
  44. Šušteršič, M., Mramor, D., & Zupan, J. (2009). Consumer credit scoring models with limited data. Expert Systems with Applications, 36, 4736-4744. https://doi.org/10.1016/j.eswa.2008.06.016
  45. Swets, J., Dawes, R., & Monahan, J. (2000). Better decisions through science. Scientific American, 283(4), 82–87. https://www.jstor.org/stable/26058901
  46. Tang, Y., Ji, J., Gao, S., Dai, H., Yu, Y., & Todo, Y. (2018). A Pruning Neural Network Model in Credit Classification Analysis. Computational Intelligence and Neuroscience, 2018, 1-22. https://doi.org/10.1155/2018/9390410
  47. Thompson, C., Kim, R., Aloe, A., & Becker, B. (2017). Extracting the Variance Inflation Factor and Other Multicollinearity Diagnostics from Typical Regression Results. Basic and Applied Social Psychology, 39(2), 81-90. https://doi.org/10.1080/01973533.2016.1277529
  48. Vellido, A., Lisboa, P. J. G. & Vaughan, J. (1999). Neural networks in business: A survey of applications (1992-1998). Expert Systems with Applications, 17, 51-70. https://doi.org/10.1016/S0957-4174(99)00016-0
  49. West, D. (2000). Neural network credit scoring models. Computers and Operations Research, 27, 1131–1152. https://doi.org/10.1016/S0305-0548(99)00149-5
  50. Wójcicka, A. (2017). Neural Networks in Credit Risk Classification of Companies in the Construction Sector. Econometric Research in Finance, 2(2), 63–77. https://doi.org/10.33119/ERFIN.2017.2.2.1
  51. Zhao, Z., Xu, S., Kang, B. H., Kabir, M. M., Liu, Y., & Wasinger, R. (2015). Investigation and improvement of multi-layer perception neural networks for credit scoring. Expert Systems with Applications, 42, 3508-3516. https://doi.org/10.1016/j.eswa.2014.12.006