Customer Credit Rating by Machine Learning

Authors

  • Chengyijing Wang
  • Haining Jiang
  • Xiaoyan Jin
  • Ziyu Zhou

DOI:

https://doi.org/10.54691/bcpbm.v36i.3490

Keywords:

Credit Card, Credit Rating, Machine Learning, Customer credit

Abstract

Recently, people's consumption attitudes have also changed, being inclined to spend in advance. Banks and other financial institutions use credit rating models as a tool to evaluate the credit score of individuals, determine whether to grant the loan to the applicant. One of the biggest challenges for the banking industry in assessing the customers’ credit is that it is unlikely to provide a manual review to classify them because of the huge volume of data on applicants. Therefore, it is necessary to establish a suitable and effective credit rating model to help banks evaluate the quality of applicants. This paper focuses on the problems existing in the development of personal credit rating system and tries to find the best solution in the field of personal credit rating system. By selecting independent variables that are highly correlated with delinquency behavior, using different models for testing, and comparing the results of the models, this paper finally draws the conclusion that different algorithms combined by the group decision method can make better decisions.

Downloads

Download data is not yet available.

References

Kui Wang, Meixuan Li, Jingyi Cheng, Xiaomeng Zhou, Gang Li, Research on personal credit risk evaluation based on XGBoost[C], Volume 199, 2022, Pages 1128-1135, ISSN 1877-0509,.

Yunke Cheng, Research on Credit Strategy Based on XGBoost Algorithm and Optimization Problem[D], China: School of Electronic Information of Wuhan University, 2021

Javadpour, A., Saedifar, K., Wang, G. et al. Improving the Efficiency of Customer's Credit Rating with Machine Learning in Big Data Cloud Computing[C]. Wireless Pers Commun 121, 2699–2718 (2021).

Li, X., Sun, Y. Application of RBF neural network optimal segmentation algorithm in credit rating[D]. Neural Comput & Applic 33, 8227–8235 (2021).

Baisong Li, Huiyu Li, Mengmeng Gong, Establishment of a Mathematical Model for Enterprise Credit Risk Recognition and Rating Based on Hybrid Learning Algorithms[C], IOP Conference Series: Materials Science and Engineering, Volume 563, Issue 5

Aurora Y.MU, A Hybrid Machine Learning Model with Cost-function Based Outlier Removal and Its Application on Credit Rating[C], J ournal of Physics: Conference Series, doi:10.1088/1742-6596/1584/1/012001

KIROLOS ATEF, Credit Card Approval Prediction without vintage, https://www.kaggle.com/code/kirolosatef/credit-card-approval-prediction-without-vintage/notebook

Remy Estran, Antoine Souchaud, David Abitbol, Using a genetic algorithm to optimize an expert credit rating model[C], Volume 203, 2022, 117506, ISSN 0957-4174,.

Chih-Fong Tsai, Ming-Lun Chen, Credit rating by hybrid machine learning techniques[C], Volume 10, Issue 2, 2010, Pages 374-380, ISSN 1568-4946.

Liu Z , Tian H , Wang H . Staff's Perception of Credit Bank System Based on Logistics Regressions Regression Model: A Survey of Faculty Members at Model: at 11 Municipal RTVUs in Shanxi Province, Northwest China[J]. Distance Education in China, 2014.

Downloads

Published

2023-01-13

How to Cite

Wang, C., Jiang, H., Jin, X., & Zhou, Z. (2023). Customer Credit Rating by Machine Learning. BCP Business & Management, 36, 387–395. https://doi.org/10.54691/bcpbm.v36i.3490