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Risks 2018, 6(2), 38; https://doi.org/10.3390/risks6020038

Credit Risk Analysis Using Machine and Deep Learning Models

1
Direction du Numérique, AFD—Agence Française de Développement, Paris 75012, France
2
Laboratory of Excellence for Financial Regulation (LabEx ReFi), Paris 75011, France
3
IPAG Business School, University Paris 1 Pantheon Sorbonne, Ca’Foscari Unversity of Venezia, Venezia 30123, Italy
4
Université Paris 1 Panthéon-Sorbonne, CES, 106 bd de l’Hôpital, Paris 75013, France
5
Capgemini Consulting, Courbevoie 92400, France
6
University College London Computer Science, 66-72 Gower Street, London WC1E 6EA, UK
The opinions, ideas and approaches expressed or presented are those of the authors and do not necessarily reflect any past or future Capgemini’s positions. As a result, Capgemini cannot be held responsible for them.
*
Author to whom correspondence should be addressed.
Received: 9 February 2018 / Revised: 3 April 2018 / Accepted: 9 April 2018 / Published: 16 April 2018
(This article belongs to the Special Issue Computational Methods for Risk Management in Economics and Finance)
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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 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. View Full-Text
Keywords: credit risk; financial regulation; data science; Big Data; deep learning credit risk; financial regulation; data science; Big Data; deep learning
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Addo, P.M.; Guegan, D.; Hassani, B. Credit Risk Analysis Using Machine and Deep Learning Models. Risks 2018, 6, 38.

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