现代信息科技2025，Vol.9Issue(4)：173-178,6.DOI:10.19850/j.cnki.2096-4706.2025.04.033
基于集成学习的金融交易欺诈识别研究
Research on Financial Transaction Fraud Identification Based on Ensemble Learning
摘要
Abstract
Financial fraud seriously threatens the stability of financial markets,and the existing anti-fraud methods have the problems of singleness and inefficiency.Therefore,this paper constructs a financial transaction fraud recognition model based on the Ensemble Learning method,aiming to improve the fraud recognition effect.In the research,four basic models are constructed by Bagging and Boosting,and two models with better effects are selected by optimizing parameters.Subsequently,the Stacking method is used to conduct fusion training for the two models,which further improves the recognition rate of the model.The experimental results show that the fusion model has significant advantages in financial transaction fraud identification.Compared with the basic model,it has higher accuracy with different datasets,especially in dealing with complex fraud patterns and new means,showing higher accuracy and stability.This improved model method provides effective decision support for financial decision makers and relevant departments,and helps to improve the security of financial markets.关键词
集成学习/金融欺诈/Boosting/StackingKey words
Ensemble Learning/financial fraud/Boosting/Stacking分类
信息技术与安全科学引用本文复制引用
郑德铭,李思佳,潘彦恺,郑健龙..基于集成学习的金融交易欺诈识别研究[J].现代信息科技,2025,9(4):173-178,6.基金项目
河北省社会科学基金项目(HB22SH011) （HB22SH011）
