Credit Risk Analysis Using Machine and Deep Learning Models
Dominique Guegan (),
Peter Addo () and
Bertrand Hassani ()
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Dominique Guegan: UP1 - Université Panthéon-Sorbonne, CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, Labex ReFi - UP1 - Université Panthéon-Sorbonne, IPAG Business School, University of Ca’ Foscari [Venice, Italy]
Peter Addo: AFD - Agence française de développement, Labex ReFi - UP1 - Université Panthéon-Sorbonne
Bertrand Hassani: Labex ReFi - UP1 - Université Panthéon-Sorbonne, CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, Capgemini Consulting [Paris], UCL-CS - Computer science department [University College London] - UCL - University College of London [London]
Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) from HAL
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.
Keywords: financial regulation; deep learning; Big data; data science; credit risk (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ban, nep-big, nep-cmp and nep-rmg
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Published in Risks, MDPI, 2018, Computational Methods for Risk Management in Economics and Finance, 6 (2), pp.38. 〈http://www.mdpi.com/2227-9091/6/2/38〉. 〈10.3390/risks6020038〉
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Persistent link: https://EconPapers.repec.org/RePEc:hal:cesptp:halshs-01835164
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