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Machine Learning for Enhanced Credit Risk Assessment: An Empirical Approach

Author

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  • Nicolas Suhadolnik

    (Institute of Mathematics and Computer Science, University of Sao Paulo, Sao Carlos 13566-590, Brazil
    Regional Bank for Development of the South Region, Curitiba 80030-900, Brazil)

  • Jo Ueyama

    (Institute of Mathematics and Computer Science, University of Sao Paulo, Sao Carlos 13566-590, Brazil)

  • Sergio Da Silva

    (Graduate Program in Economics, Federal University of Santa Catarina, Florianopolis 88049-970, Brazil)

Abstract

Financial institutions and regulators increasingly rely on large-scale data analysis, particularly machine learning, for credit decisions. This paper assesses ten machine learning algorithms using a dataset of over 2.5 million observations from a financial institution. We also summarize key statistical and machine learning models in credit scoring and review current research findings. Our results indicate that ensemble models, particularly XGBoost, outperform traditional algorithms such as logistic regression in credit classification. Researchers and experts in the subject of credit risk can use this work as a practical reference as it covers crucial phases of data processing, exploratory data analysis, modeling, and evaluation metrics.

Suggested Citation

  • Nicolas Suhadolnik & Jo Ueyama & Sergio Da Silva, 2023. "Machine Learning for Enhanced Credit Risk Assessment: An Empirical Approach," JRFM, MDPI, vol. 16(12), pages 1-21, November.
    • Handle: RePEc:gam:jjrfmx:v:16:y:2023:i:12:p:496-:d:1288822
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    References listed on IDEAS

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    1. Hussein A. Abdou & John Pointon, 2011. "Credit Scoring, Statistical Techniques And Evaluation Criteria: A Review Of The Literature," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(2-3), pages 59-88, April.
    2. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    3. Tobias Berg & Valentin Burg & Ana Gombović & Manju Puri, 2020. "On the Rise of FinTechs: Credit Scoring Using Digital Footprints," The Review of Financial Studies, Society for Financial Studies, vol. 33(7), pages 2845-2897.
    4. 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.
    5. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    6. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
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