Credit Risk Analysis Using machine and Deep Learning Models

Dominique Guegan ()
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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, University of Ca’ Foscari [Venice, Italy]

Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) from HAL

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 classidiers 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.

Keywords: Credit risks; Big data; Regulation; Machine Learning (search for similar items in EconPapers)
Date: 2018-09-27
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Citations: View citations in EconPapers (41)

Published in 3small Business Risk, Financial Regulation and Big Data Analytics, Sep 2018, Palazzo Franchetti - Venice, Italy

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Persistent link: https://EconPapers.repec.org/RePEc:hal:cesptp:halshs-01889154

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