Open AccessOnline Transaction Fraud Detection System Based on Machine Learning
Author(s) -
Bocheng Liu,
Xiang Chen,
Kumlan Yu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2023/1/012054
Subject(s) - credit card fraud , database transaction , transaction data , computer science , upload , credit card , online transaction processing , the internet , transaction processing , distributed transaction , computer security , transaction processing system , artificial neural network , data mining , machine learning , database , world wide web , payment
With the rapid development of Internet technology, the scale of online transactions is constantly expanding. At the same time, the related network transaction fraud problem has become more significant. Compared with the credit card transaction, the network transaction has the characteristics of low cost, wide coverage and high frequency, which makes the detection of fraud more complex. Aiming at the problem of difficult fraud detection in network transactions, this paper designed two fraud detection algorithms based on Fully Connected Neural Network and XGBoost, whose AUC values can achieve 0.912 and 0.969 respectively. Meanwhile, we designed an interactive online transaction fraud detection system based on XGBoost model, which can automatically analyze the transaction data uploaded and return the fraud detection results to users.