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Credit Risk Analysis using Machine and Deep learning models

Author

Listed:
  • Peter Martey Addo

    (Expert Synapses SNCF Mobilité; LabEx ReFi)

  • Dominique Guegan

    (University Paris 1 Pantheon Sorbonne; Ca' Foscari Unversity of Venice; IPAG Business School; LabEx ReFi)

  • Bertrand Hassani

    (Capgemini Consulting; LabEx ReFi)

Abstract

Due to the hyper technology associated to Big Data, data availability and computing power, most banks or lending financial 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 modelling process to test the stability of binary classifiers by comparing performance on separate data. We observe that tree-based models are more stable than models based on multilayer artificial neural networks. This opens several questions relative to the intensive used of deep learning systems in the enterprises.

Suggested Citation

  • Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep learning models," Working Papers 2018:08, Department of Economics, University of Venice "Ca' Foscari".
    • Handle: RePEc:ven:wpaper:2018:08
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    More about this item

    Keywords

    Credit risk; Financial regulation; Data Science; Bigdata; Deep learning;
    All these keywords.

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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