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Machine Learning Models and Data-Balancing Techniques for Credit Scoring: What Is the Best Combination?

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  • Ahmed Almustfa Hussin Adam Khatir

    (Department of Economics and Management, University of Trento, Via Inama 5, 38122 Trento, Italy)

  • Marco Bee

    (Department of Economics and Management, University of Trento, Via Inama 5, 38122 Trento, Italy)

Abstract

Forecasting the creditworthiness of customers is a central issue of banking activity. This task requires the analysis of large datasets with many variables, for which machine learning algorithms and feature selection techniques are a crucial tool. Moreover, the percentages of “good” and “bad” customers are typically imbalanced such that over- and undersampling techniques should be employed. In the literature, most investigations tackle these three issues individually. Since there is little evidence about their joint performance, in this paper, we try to fill this gap. We use five machine learning classifiers, and each of them is combined with different feature selection techniques and various data-balancing approaches. According to the empirical analysis of a retail credit bank dataset, we find that the best combination is given by random forests, random forest recursive feature elimination and random oversampling.

Suggested Citation

  • Ahmed Almustfa Hussin Adam Khatir & Marco Bee, 2022. "Machine Learning Models and Data-Balancing Techniques for Credit Scoring: What Is the Best Combination?," Risks, MDPI, vol. 10(9), pages 1-22, August.
    • Handle: RePEc:gam:jrisks:v:10:y:2022:i:9:p:169-:d:895806
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    References listed on IDEAS

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    1. Reichert, Alan K & Cho, Chien-Ching & Wagner, George M, 1983. "An Examination of the Conceptual Issues Involved in Developing Credit-scoring Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(2), pages 101-114, April.
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    3. B Baesens & T Van Gestel & S Viaene & M Stepanova & J Suykens & J Vanthienen, 2003. "Benchmarking state-of-the-art classification algorithms for credit scoring," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(6), pages 627-635, June.
    4. K B Schebesch & R Stecking, 2005. "Support vector machines for classifying and describing credit applicants: detecting typical and critical regions," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(9), pages 1082-1088, September.
    5. Desai, Vijay S. & Crook, Jonathan N. & Overstreet, George A., 1996. "A comparison of neural networks and linear scoring models in the credit union environment," European Journal of Operational Research, Elsevier, vol. 95(1), pages 24-37, November.
    6. D. J. Hand & W. E. Henley, 1997. "Statistical Classification Methods in Consumer Credit Scoring: a Review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 160(3), pages 523-541, September.
    7. Trivedi, Shrawan Kumar, 2020. "A study on credit scoring modeling with different feature selection and machine learning approaches," Technology in Society, Elsevier, vol. 63(C).
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    Cited by:

    1. Flavio Bazzana & Marco Bee & Ahmed Almustfa Hussin Adam Khatir, 2024. "Machine learning techniques for default prediction: an application to small Italian companies," Risk Management, Palgrave Macmillan, vol. 26(1), pages 1-23, February.
    2. Abdussalam Aljadani & Bshair Alharthi & Mohammed A. Farsi & Hossam Magdy Balaha & Mahmoud Badawy & Mostafa A. Elhosseini, 2023. "Mathematical Modeling and Analysis of Credit Scoring Using the LIME Explainer: A Comprehensive Approach," Mathematics, MDPI, vol. 11(19), pages 1-28, September.

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