Credit Risk Analysis using Machine and Deep learning models

Peter Martey Addo (), Dominique Guegan () and Bertrand Hassani
Additional contact information
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

No 2018:08, Working Papers from Department of Economics, University of Venice "Ca' Foscari"

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: Credit risk; Financial regulation; Data Science; Bigdata; Deep learning (search for similar items in EconPapers)
JEL-codes: C55 (search for similar items in EconPapers)
Pages: 32 pages
Date: 2018
New Economics Papers: this item is included in nep-ban, nep-big, nep-cmp and nep-rmg
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Citations: View citations in EconPapers (53)

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Persistent link: https://EconPapers.repec.org/RePEc:ven:wpaper:2018:08

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