Document Type

Article

Publication Date

Summer 6-19-2025

Abstract

In this paper, the credit card risk control and early warning model is constructed to predict and control the credit card risk by fusing multi-source data through data mining technology. After analyzing the theoretical application of XGBoost algorithm on credit card transaction risk, its model parameters are optimized by particle swarm algorithm to construct PSO-XGBoost credit card transaction risk prediction model. The credit card transaction risk prediction performance of the PSO-XGBoost model is verified and applied to the abnormal transaction risk assessment of Bank A. The AUC value, accuracy, F1 value, precision rate, and recall rate of the PSO-XGBoost model are the largest among all the algorithms, and the correct detection rate of the PSO-XGBoost model is significantly higher than that of other algorithms, and the error detection rate significantly lower than other algorithms, with the best risk prediction performance. Among the 10 Bank A credit card customers, customer 4 and customer 5 have very high risk in their credit card transactions, customer 2 and customer 3 have high risk, customer 1, 5, 7 and 8 have medium risk in their credit card transactions, and customer 9 and customer 10 have low risk.

Program or Discipline Name

Analytics

Secondary Program or Discipline Name

Analytics

Publication Title

ZKG INTERNATIONAL

ISSN

2366-1313

DOI

10.63386/619087

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