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Credit Risk Analysis Using Machine and Deep Learning Models

Author

Listed:
  • Peter Martey Addo

    (Direction du Numérique, AFD—Agence Française de Développement, Paris 75012, France
    Laboratory of Excellence for Financial Regulation (LabEx ReFi), Paris 75011, France)

  • Dominique Guegan

    (Laboratory of Excellence for Financial Regulation (LabEx ReFi), Paris 75011, France
    IPAG Business School, University Paris 1 Pantheon Sorbonne, Ca’Foscari Unversity of Venezia, Venezia 30123, Italy
    Université Paris 1 Panthéon-Sorbonne, CES, 106 bd de l’Hôpital, Paris 75013, France)

  • Bertrand Hassani

    (Laboratory of Excellence for Financial Regulation (LabEx ReFi), Paris 75011, France
    Université Paris 1 Panthéon-Sorbonne, CES, 106 bd de l’Hôpital, Paris 75013, France
    Capgemini Consulting, Courbevoie 92400, France
    University College London Computer Science, 66-72 Gower Street, London WC1E 6EA, UK)

Abstract

Due to the advanced technology associated with Big Data, data availability and computing power, most banks or lending institutions are renewing their business models. Credit risk predictions, monitoring, model reliability and effective loan processing are key to decision-making and transparency. In this work, we build binary classifiers based on machine and deep learning models on real data in predicting loan default probability. The top 10 important features from these models are selected and then used in the modeling process to test the stability of binary classifiers by comparing their performance on separate data. We observe that the tree-based models are more stable than the models based on multilayer artificial neural networks. This opens several questions relative to the intensive use of deep learning systems in enterprises.

Suggested Citation

  • Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Risks, MDPI, vol. 6(2), pages 1-20, April.
    • Handle: RePEc:gam:jrisks:v:6:y:2018:i:2:p:38-:d:141267
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