Avanish Kumar Singh
Himanshu Singh
- Research Scholar, MCA, Thakur Institute of Management Studies, Career Development & Research (TIMSCDR) Mumbai, Maharashtra, India
- 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)]
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Journal of Open Source Developments
| Volume | 11 |
| Issue | 01 |
| Received | February 29, 2024 |
| Accepted | March 26, 2024 |
| Published | April 5, 2024 |