Credit card fraud detection using machine learning

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, ReginoCriado, 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.

@article{1704006,
      author = {Sushant Agrawal},
      title = {Credit card fraud detection using machine learning},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {7},
      pages = {126-131},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1704006.pdf},
      abstract = {For clients to avoid being charged for products they did not buy, credit card issuers must be able to recognise fraudulent credit card transactions. Data Science may be used to solve issues, and coupled with machine learning, its significance cannot be understated. With the use of credit card fraud detection, this research aims to demonstrate the modelling of a data set using machine learning. The Credit Card Fraud Detection Problem includes modelling prior credit card transactions using data from those that turned out to be fraudulent. This technique then determines the validity of a new transaction. The goal here is to minimise inaccurate fraud categories while detecting 100% of the fraudulent transactions. A classic example of categorization is the detection of credit card fraud. The analysis and pre-processing of data sets, as well as the use of several anomaly detection techniques to PCA-transformed Credit Card Transaction data, have been the main points of this approach.},
      month = {January},
  }