Credit Bank Default Prediction Based on Machine Learning Approaches

DOI

10.2991/978-94-6463-030-5_85How to use a DOI?

Keywords

Machine Learning; Stock Price Prediction; Credit Bank Default; Deep Learning; Bigdata Analysis

Abstract

With the rapid development of Internet, data science is playing a more and more important role in all fields. Especially in the financial industry, the application level of big data has become the embodiment of enterprise competitiveness. Contemporarily, among many types of data science, deep learning has developed most rapidly, where plenty of relevant achievements have been achieved accordingly. It’s a combination of data science and human neural networks. This paper will first introduce the basic descriptions and principles of deep learning and model description. Subsequently, a specific application of bank default prediction combined will be demonstrated with prediction variables, common models, existing disadvantages, and prospects. Based on the analysis, it should be noted that a clear limitation appears from both the perspectives of theoretical knowledge and construction of mode, i.e., scholars still could not solve the problem of high requirement of training sample and computation sources. These results shed light on guiding financial application in terms of machine learning concepts.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Runqi Jiang
PY  - 2022
DA  - 2022/12/20
TI  - Credit Bank Default Prediction Based on Machine Learning Approaches
BT  - Proceedings of the 2022 International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2022)
PB  - Atlantis Press
SP  - 859
EP  - 867
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-030-5_85
DO  - 10.2991/978-94-6463-030-5_85
ID  - Jiang2022
ER  -