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Machine Learning for Credit Risk Prediction: A Systematic Literature Review

Author

Listed:
  • Jomark Pablo Noriega

    (Departamento Académico de Ciencia de la Computacion, Universidad Nacional Mayor de San Marcos, Decana de América, Lima 15081, Peru
    Financiera QAPAQ, Lima 150120, Peru
    These authors contributed equally to this work.)

  • Luis Antonio Rivera

    (Departamento Académico de Ciencia de la Computacion, Universidad Nacional Mayor de San Marcos, Decana de América, Lima 15081, Peru
    Centro de Ciências Exatas e Tecnologia, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Campos dos Goytacazes 28013-602, Brazil
    These authors contributed equally to this work.)

  • José Alfredo Herrera

    (Departamento Académico de Ciencia de la Computacion, Universidad Nacional Mayor de San Marcos, Decana de América, Lima 15081, Peru
    Programme in Biotechnology, Engineering and Chemical Technology, Universidad Pablo de Olavide, 41013 Sevilla, Spain
    These authors contributed equally to this work.)

Abstract
In this systematic review of the literature on using Machine Learning (ML) for credit risk prediction, we raise the need for financial institutions to use Artificial Intelligence (AI) and ML to assess credit risk, analyzing large volumes of information. We posed research questions about algorithms, metrics, results, datasets, variables, and related limitations in predicting credit risk. In addition, we searched renowned databases responding to them and identified 52 relevant studies within the credit industry of microfinance. Challenges and approaches in credit risk prediction using ML models were identified; we had difficulties with the implemented models such as the black box model, the need for explanatory artificial intelligence, the importance of selecting relevant features, addressing multicollinearity, and the problem of the imbalance in the input data. By answering the inquiries, we identified that the Boosted Category is the most researched family of ML models; the most commonly used metrics for evaluation are Area Under Curve (AUC), Accuracy (ACC), Recall, precision measure F1 (F1), and Precision. Research mainly uses public datasets to compare models, and private ones to generate new knowledge when applied to the real world. The most significant limitation identified is the representativeness of reality, and the variables primarily used in the microcredit industry are data related to the Demographic, Operation, and Payment behavior. This study aims to guide developers of credit risk management tools and software towards the existing ability of ML methods, metrics, and techniques used to forecast it, thereby minimizing possible losses due to default and guiding risk appetite.

Suggested Citation

  • Jomark Pablo Noriega & Luis Antonio Rivera & José Alfredo Herrera, 2023. "Machine Learning for Credit Risk Prediction: A Systematic Literature Review," Data, MDPI, vol. 8(11), pages 1-17, November.
    • Handle: RePEc:gam:jdataj:v:8:y:2023:i:11:p:169-:d:1275568
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    References listed on IDEAS

    as
    1. Dumitrescu, Elena & Hué, Sullivan & Hurlin, Christophe & Tokpavi, Sessi, 2022. "Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1178-1192.
    2. Gianfranco Lombardo & Mattia Pellegrino & George Adosoglou & Stefano Cagnoni & Panos M. Pardalos & Agostino Poggi, 2022. "Machine Learning for Bankruptcy Prediction in the American Stock Market: Dataset and Benchmarks," Future Internet, MDPI, vol. 14(8), pages 1-23, August.
    Full references (including those not matched with items on IDEAS)

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