Proceedings of the 2022 International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2022)

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Credit Allocation Considering Loaner’s Credit Risk and Willingness for Acceptance: A Hybrid XGBoost-Topsis Enabled Optimization Approach

Authors
Genglin Zhu1, Zixin Peng1, Mu Li1, Jiantao Fan2, Xinjun Lai3, *
1School of Electro-Mechanical Engineering, Guangdong University of Technology, Guangzhou, China
2School of Economics and Commerce, Guangdong University of Technology, Guangzhou, China
3State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangdong CIM Provincial Key Lab, School of Electro-Mechanical Engineering, Guangdong University of Technology, Guangzhou, China
*Corresponding author. Email: xinjun.lai@gdut.edu
Corresponding Author
Xinjun Lai
Available Online 2 December 2022.
DOI
10.2991/978-94-6463-010-7_99How to use a DOI?
Keywords
Credit Decision; Goal Programming; Topsis; Xgboost Classified Forecast
Abstract

Banks provide financial support for enterprises but may bear more risks lending to SMEs. This paper aims to reduce the credit risk of banks while maximizing their revenue. To provide banks with optimal credit strategy, this paper considers the accuracy of enterprise default risk. In this paper, banks screen out lending objects and enterprises choose whether to accept loans. The difficulty lies in that some enterprises lack credit records. Based upon the existing credit records, this paper uses XGBoost model to predict credit rating of these enterprises and evaluates the default risk through Topsis model. The solution results are in high accuracy and easy interpretation. To get the bank’s optimal credit strategy, we obtained the data of non-credit record enterprises from the official network of Contemporary Undergraduate Mathematical Contest in Modeling. Starting from whether to consider the willingness, bank provides loans to 131 non-credit records enterprises. When considering the willingness, we find that bank can get an income of CNY 7.30189 million by providing a credit of CNY 96.44 million. Without considering it, the credit is CNY 49.27 million, and the income is CNY 5.10599 million. Therefore, the bank credit decision-making model considering the willingness to increases the income of CNY 2.1959 million.

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 2022 International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2022)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
2 December 2022
ISBN
978-94-6463-010-7
ISSN
2589-4919
DOI
10.2991/978-94-6463-010-7_99How 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  - Genglin Zhu
AU  - Zixin Peng
AU  - Mu Li
AU  - Jiantao Fan
AU  - Xinjun Lai
PY  - 2022
DA  - 2022/12/02
TI  - Credit Allocation Considering Loaner’s Credit Risk and Willingness for Acceptance: A Hybrid XGBoost-Topsis Enabled Optimization Approach
BT  - Proceedings of the 2022 International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2022)
PB  - Atlantis Press
SP  - 985
EP  - 995
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-010-7_99
DO  - 10.2991/978-94-6463-010-7_99
ID  - Zhu2022
ER  -