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Conference papers

Credit Risk Analysis Using machine and Deep Learning Models

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 classidiers 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.
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Contributor : Dominique Guégan <>
Submitted on : Friday, October 5, 2018 - 4:40:41 PM
Last modification on : Sunday, January 19, 2020 - 6:38:38 PM


  • HAL Id : halshs-01889154, version 1



Dominique Guegan. Credit Risk Analysis Using machine and Deep Learning Models. 3small Business Risk, Financial Regulation and Big Data Analytics, Sep 2018, Palazzo Franchetti - Venice, Italy. ⟨halshs-01889154⟩



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