Credit Risk Analysis using Machine and Deep Learning models

Peter Addo (), Dominique Guegan () and Bertrand Hassani ()
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Peter Addo: Lead Data Scientist - SNCF Mobilité
Dominique Guegan: UP1 - Université Paris 1 Panthéon-Sorbonne, Labex ReFi - UP1 - Université Paris 1 Panthéon-Sorbonne, University of Ca’ Foscari [Venice, Italy], CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, IPAG Business School
Bertrand Hassani: Labex ReFi - UP1 - Université Paris 1 Panthéon-Sorbonne, Capgemini Consulting [Paris]

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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.

Keywords: Bigdata; Data Science; Deep learning; Financial regulation; Credit risk (search for similar items in EconPapers)
Date: 2018-02
New Economics Papers: this item is included in nep-ban, nep-bec, nep-big and nep-rmg
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01719983
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (42)

Published in 2018

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