Enhanced E-commerce Fraud Prediction Based on a Convolutional Neural Network Model

作     者：Sumin Xie Ling Liu Guang Sun Bin Pan Lin Lang Peng Guo 

作者机构：Chenzhou Vocational Technical CollegeChenzhou423000China Hunan University of Finance and EconomicsChangsha410205China University Malaysia SabahKota Kinabalu88999Malaysia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2023年第75卷第4期

页      面：1107-1117页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China (No.72073041,No.61903131) 2020 Hunan Provincial Higher Education Teaching Reform Research Project (Nos.HNJG-2020-1130,HNJG-2020-1124) 2020 General Project of Hunan Social Science Fund (No.20B16) Outstanding Youth of Department of Education of Hunan Province (No.20B096)and the China Postdoctoral Science Foundation (No.2020M683715) 

主　　题：CNN model detection e-commerce fraud 

摘      要：The rapidly escalating sophistication of e-commerce fraud in recent years has led to an increasing reliance on fraud detection methods based on machine ***,fraud detection methods based on conventional machine learning approaches suffer from several problems,including an excessively high number of network parameters,which decreases the efficiency and increases the difficulty of training the network,while simultaneously leading to network *** addition,the sparsity of positive fraud incidents relative to the overwhelming proportion of negative incidents leads to detection failures in trained *** present work addresses these issues by proposing a convolutional neural network(CNN)framework for detecting ecommerce fraud,where network training is conducted using historical market transaction *** number of network parameters reduces via the local perception field and weight sharing inherent in the CNN *** addition,this deep learning framework enables the use of an algorithmiclevel approach to address dataset imbalance by focusing the CNN model on minority data *** proposed CNN model is trained and tested using a large public e-commerce service dataset from 2018,and the test results demonstrate that the model provides higher fraud prediction accuracy than existing state-of-the-art methods.