Credit Risk Analysis Using Machine and Deep Learning Models

Risks

PeterMartey Addo¹  Dominique Guegan²  Bertrand Hassani² 

[1] Direction du Numérique, AFD—Agence Française de Développement, Paris 75012, France;Laboratory of Excellence for Financial Regulation (LabEx ReFi), Paris 75011, France;

关键词: credit risk;    financial regulation;    data science;    Big Data;    deep learning;   

DOI  :  10.3390/risks6020038

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown