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Fraud Transaction Detection System
Subject area: Science,Engineering and Technology · Area of research: Machine learning
Abstract
To prevent customers from being charged for unauthorized purchases, it is crucial for credit card issuers to be able to identify fraudulent transactions. Data science, in conjunction with machine learning, plays a significant role in addressing this issue. This study focuses on utilizing machine learning to model a dataset for credit card fraud detection. The approach involves analyzing past credit card transactions, particularly those that were later identified as fraudulent, in order to assess the legitimacy of new transactions. The objective is to minimize false categorizations of fraud while accurately identifying all instances of fraudulent activity. One prominent example of categorization is the detection of credit card fraud. This approach involves analyzing and pre-processing datasets, as well as employing various anomaly detection techniques on PCA-transformed credit card transaction data.
References
[1] "Credit Card Fraud Detection Based on Transaction Behavior -by John Richard D. Kho, Larry A. Vea" was included in the proceedings of the 2017 IEEE Region 10 Conference (TENCON), which was held in Malaysia from November 5-8, 2017.
[2] CLIFTON PHUA, VINCENT LEE, KATE SMITH, & ROSS GAYLER are the authors. Published by the School of Business Systems, Faculty of Information Technology, Monash University, Wellington Road, Clayton, Victoria 3800, Australia, "A Comprehensive Survey of Data Mining-based Fraud Detection Research"
[3] Research Scholar, GJUS&T Hisar HCE, Sonepat, "Survey Paper on Credit Card Fraud Detection by Suman," published in International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), Volume 3 Issue 3, March 2014.
[4] Wen-Fang YU and Na Wang's "Research on Credit Card Fraud Detection Model Based on Distance Sum" was published by the 2009 International Joint Conference on Artificial Intelligence.
[5] By Massimiliano Zanin, Miguel Romance, Regino Criado, and Santiago Moral, "Credit Card Fraud Detection using Parenclitic Network Analysis-By, Hindawi Complexity Volume 2018, Article ID 5764370, 9 pages."
[6] AUGUST 2018 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, VOL. 29, NO. 8, "Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy"
[7] "Credit Card Fraud Detection-by Ishu Trivedi, Monika, Mrigya, and Mridushi" appeared in the January 2016 issue of the International Journal of Advanced Research in Computer and Communication Engineering.
[8] "Plastic Card Fraud Detection Using Peer Group Analysis" Springer, Issue 2008. David J.Wetson, David J.Hand, M. Adams, Whitrow, and Piotr Jusczak.
How to cite this paper
@article{1704592,
author = {Sushant Agrawal},
title = {Fraud Transaction Detection System},
journal = {Iconic Research And Engineering Journals},
year = {2023},
volume = {6},
number = {12},
pages = {84-88},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1704592.pdf},
abstract = {To prevent customers from being charged for unauthorized purchases, it is crucial for credit card issuers to be able to identify fraudulent transactions. Data science, in conjunction with machine learning, plays a significant role in addressing this issue. This study focuses on utilizing machine learning to model a dataset for credit card fraud detection. The approach involves analyzing past credit card transactions, particularly those that were later identified as fraudulent, in order to assess the legitimacy of new transactions. The objective is to minimize false categorizations of fraud while accurately identifying all instances of fraudulent activity. One prominent example of categorization is the detection of credit card fraud. This approach involves analyzing and pre-processing datasets, as well as employing various anomaly detection techniques on PCA-transformed credit card transaction data.},
month = {June},
}