Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)

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Using Machine Learning Models to Assess Users’ Credit Default Risk

Authors
Yuxi Huang1, *
1School of Mechanical and Electrical Engineering, Guangzhou Huali College, Guangzhou, China
*Corresponding author. Email: 740783840@qq.com
Corresponding Author
Yuxi Huang
Available Online 10 August 2023.
DOI
10.2991/978-94-6463-198-2_33How to use a DOI?
Keywords
Credit Cards; Credit Default Risk; Principal Component Analysis Techniques; BP Neural Networks; Logistic Regression
Abstract

As far as the current social situation is concerned, more and more people choose credit cards as a way to pay for their daily expenses. When it comes to credit cards, we have to mention the credit default risk associated with them. We know that when a person's credit default risk is too high, his credit card may be frozen. It means that the credit default risk likes a kind of judgment for whether a person can apply for a credit card, and it is an important factor in continuing to use a credit card. In this paper, we analyze how to assess a user's credit default risk. First, we use principal component analysis (PCA) to extract several key factors for judging credit default risk, then we use BP neural network to evaluate and analyze the extracted key factors, and finally, we analyze the user's credit default risk through the results obtained.

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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Volume Title
Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)
Series
Atlantis Highlights in Computer Sciences
Publication Date
10 August 2023
ISBN
10.2991/978-94-6463-198-2_33
ISSN
2589-4900
DOI
10.2991/978-94-6463-198-2_33How to use a DOI?
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.

Cite this article

risenwbib
TY  - CONF
AU  - Yuxi Huang
PY  - 2023
DA  - 2023/08/10
TI  - Using Machine Learning Models to Assess Users’ Credit Default Risk
BT  - Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)
PB  - Atlantis Press
SP  - 297
EP  - 323
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-198-2_33
DO  - 10.2991/978-94-6463-198-2_33
ID  - Huang2023
ER  -