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Research on personal credit scoring model based on deep learning
- 1 Financial Engineering in UCLA
* Author to whom correspondence should be addressed.
Abstract
The increasing integration of internet technology and the financial industry has led to the gradual replacement of traditional credit evaluation models with those based on deep learning, which have demonstrated excellent accuracy. This has become a prominent area of research. Nevertheless, the credit scoring model based on a deep neural network encounters significant challenges in terms of its applicability in the field of credit scoring, largely due to the opaque nature of its learning and decision-making processes. The application of deep learning to personal credit scoring has been shown to enhance the accuracy of the resulting scores by leveraging large amounts of data. The model employs a deep neural network (DNN) architecture that integrates multiple input features, including the user's transaction history, social behaviour and other relevant data. The model is trained using supervised learning, with a large amount of labelled data used to optimise its prediction performance. Experimental results demonstrate that the deep learning-based model exhibits a notable improvement in accuracy and robustness compared to traditional credit scoring models.
Keywords
Credit Scoring Model, Deep Neural Network, Personal Features, Prediction Performance
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Cite this article
Yan,T. (2024). Research on personal credit scoring model based on deep learning. Applied and Computational Engineering,87,203-208.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 6th International Conference on Computing and Data Science
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