Research on credit card transaction risk prediction model based on multi-source data fusion and its application
Year: 2025
Type: article
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.
Source: ZKG International
DOI: 10.63386/619087
Author: Ziang Qi
Institution: Duke University
Language: English
Subfield: Accounting
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4411699197