Credit Risk Analysis Using Machine and Deep Learning Models

Peter Martey Addo, Dominique Guegan and Bertrand Hassani
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Peter Martey Addo: Direction du Numérique, AFD—Agence Française de Développement, Paris 75012, France
Dominique Guegan: Laboratory of Excellence for Financial Regulation (LabEx ReFi), Paris 75011, France
Bertrand Hassani: Laboratory of Excellence for Financial Regulation (LabEx ReFi), Paris 75011, France

Risks, 2018, vol. 6, issue 2, 1-20

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.

Keywords: credit risk; financial regulation; data science; Big Data; deep learning (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (48)

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