Fake Credit Transaction Detection Using Machine Learning

Abstract

In today's digital age, detecting and preventing fraudulent credit card transactions is of paramount importance. As technology advances, criminal methods are also becoming more sophisticated. The use of machine learning in credit card fraud detection and mitigation has grown significantly. In this research study, a novel method for identifying fraudulent credit card transactions with machine learning algorithms is presented. The proposed system leverages past transaction data and various characteristics associated with each transaction, such as location, transaction amount, and time, to build a predictive model. These models are trained to recognize patterns that point to fraudulent activity using supervised learning methods like random forests and support vector machines. Several metrics, including accuracy, precision, recall, and F1 score, are used to assess the performance of the model. According to experimental data, the suggested method works better than conventional rule-based fraud detection systems and achieves high accuracy. The system can effectively detect fraudulent credit card transactions while minimizing false positives. Machine learning improves the security of credit card transactions by detecting fraud in real time. In summary, this study advances the field of credit card fraud detection by using machine learning algorithms to counteract the constantly changing tactics used by fraudsters.