Credit Card Fraud Detection Using Machine Learning Techniques

Year : 2024 | Volume :11 | Issue : 01 | Page : 1-7
By

Avanish Kumar Singh

Himanshu Singh

  1. Research Scholar MCA, Thakur Institute of Management Studies, Career Development & Research (TIMSCDR) Mumbai Maharashtra India
  2. Research Scholar MCA, Thakur Institute of Management Studies, Career Development & Research (TIMSCDR) Mumbai Maharashtra India

Abstract

Credit card fraud has become a significant concern in today’s digital economy, with billions of dollars being lost annually to fraudulent transactions. Conventional rule-based approaches frequently prove inadequate in addressing the constantly changing strategies employed by fraudsters. Machine learning methods have emerged as robust solutions for detecting credit card fraud, presenting the capability to accurately identify fraudulent transactions promptly. In this study, we investigate the efficiency of three widely used machine learning algorithms—logistic regression, random forest, and decision tree—for the detection of credit card fraud. Through an extensive comparative study, we analyze the performance, accuracy, and efficiency of each algorithm on a real-world credit card transaction dataset. Our findings provide valuable insights into the strengths and limitations of these techniques in addressing the challenges posed by credit card fraud.

Keywords: Credit card frauds, training system, decision tree classifiers. random forest algorithms, logistic regression

[This article belongs to Journal of Open Source Developments(joosd)]

How to cite this article: Avanish Kumar Singh, Himanshu Singh. Credit Card Fraud Detection Using Machine Learning Techniques. Journal of Open Source Developments. 2024; 11(01):1-7.
How to cite this URL: Avanish Kumar Singh, Himanshu Singh. Credit Card Fraud Detection Using Machine Learning Techniques. Journal of Open Source Developments. 2024; 11(01):1-7. Available from: https://journals.stmjournals.com/joosd/article=2024/view=140068





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Regular Issue Subscription Review Article
Volume 11
Issue 01
Received February 29, 2024
Accepted March 26, 2024
Published April 5, 2024