The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Credit Risk Analysis Using Machine and Deep Learning Models
Open accessAbstract
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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Cited by
Statistical and machine learning models in credit scoring: A systematic literature survey
Dastile, X., Celik, T., & Potsane, M. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106263 · Cited by 404
Towards Human-Centered Explainable AI: A Survey of User Studies for Model Explanations
Rong, Y., Leemann, T., Nguyen, T.-T., Fiedler, L., Qian, P., Unhelkar, V., Seidel, T., Kasneci, G., & Kasneci, E. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3331846 · Cited by 278
Heng, Y. S., & Subramanian, P. (2023) · Lecture Notes in Networks and Systems · doi:10.1007/978-3-031-18461-1_39 · Cited by 18
Metadata
- Authors
- Addo, P., Guegan, D., & Hassani, B.
- Venue
- Risks
- Publisher
- MDPI AG
- Published
- 2018-04-16
- Volume
- 6
- Issue
- 2
- Pages
- 38
- ISSN
- 2227-9091 (ISSN-L)
- OpenAlex
- W2788025656
- Cited by
- 317
- References
- 34
- FWCI
- 93.44
- Type
- journal article
- Language
- en
- Open access
- gold
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