Research on Credit Risk Assessment of Commercial Banks Based on Machine Learning

DOI

10.2991/978-94-6463-042-8_160How to use a DOI?

Keywords

machine learning; neural network; risk assessment

Abstract

The globalization and liberalization of the financial industry have intensified the operating risks of financial institutions. In the context of slowing macroeconomic growth, the rise in the rate of non-performing loans highlights the rising risks of the financial industry. This further illustrates the necessity and urgency of conducting credit risk analysis and early warning research. In order to alleviate the continuous increase of non-performing loans, this paper studies the credit risk analysis model based on machine learning. Through analysis, this paper proposes an XGBoost model for user loan risk prediction. This model has good prediction accuracy. Based on the results of the model, some suggestions are provided for the online lending platform to identify high-risk lending users.

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  - Yan Chen
PY  - 2022
DA  - 2022/12/29
TI  - Research on Credit Risk Assessment of Commercial Banks Based on Machine Learning
BT  - Proceedings of the 2022 International Conference on mathematical statistics and economic analysis (MSEA 2022)
PB  - Atlantis Press
SP  - 1119
EP  - 1124
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-042-8_160
DO  - 10.2991/978-94-6463-042-8_160
ID  - Chen2022
ER  -