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 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.
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https://halshs.archives-ouvertes.fr/halshs-01835164
Contributeur : Dominique Guégan <>
Soumis le : jeudi 18 octobre 2018 - 17:20:16
Dernière modification le : mardi 6 août 2019 - 16:08:04
Document(s) archivé(s) le : samedi 19 janvier 2019 - 15:04:33

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Dominique Guegan, Peter Addo, Bertrand Hassani. Credit Risk Analysis Using Machine and Deep Learning Models. Risks, MDPI, 2018, Computational Methods for Risk Management in Economics and Finance, 6 (2), pp.38. ⟨10.3390/risks6020038⟩. ⟨halshs-01835164⟩

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