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Credit Risk Analysis Using Machine and Deep Learning Models

Author

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  • Dominique Guegan

    (UP1 - Université Paris 1 Panthéon-Sorbonne, CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, Labex ReFi - UP1 - Université Paris 1 Panthéon-Sorbonne, IPAG Business School, University of Ca’ Foscari [Venice, Italy])

  • Peter Martey Addo

    (AFD - Agence française de développement, Labex ReFi - UP1 - Université Paris 1 Panthéon-Sorbonne)

  • Bertrand Hassani

    (Labex ReFi - UP1 - Université Paris 1 Panthéon-Sorbonne, CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, Capgemini Consulting [Paris], UCL-CS - Department of Computer science [University College of London] - UCL - University College of London [London])

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

  • Dominique Guegan & Peter Martey Addo & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01835164, HAL.
    • Handle: RePEc:hal:cesptp:halshs-01835164
    DOI: 10.3390/risks6020038
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01835164
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    Keywords

    financial regulation; deep learning; Big data; data science; credit risk;
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