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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. 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.
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    5. 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.
    6. Thomas, Lyn C., 2000. "A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers," International Journal of Forecasting, Elsevier, vol. 16(2), pages 149-172.
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    Cited by:

    1. 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.
    2. 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.
    3. Faraz Ahmed & Kehkashan Nizam & Zubair Sajid & Sunain Qamar & Ahsan, 2024. "Striking a Balance: Evaluating Credit Risk with Traditional and Machine Learning Models," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 13(3), pages 30-35.
    4. Luis J. Mena & Vicente García & Vanessa G. Félix & Rodolfo Ostos & Rafael Martínez-Peláez & Alberto Ochoa-Brust & Pablo Velarde-Alvarado, 2024. "Enhancing financial risk prediction with symbolic classifiers: addressing class imbalance and the accuracy–interpretability trade–off," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-11, December.

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