Machine Learning Integration for Real-time Fraud Detection in Near Field Communication (NFC) Card Transactions

This paper aims at identifying and evaluating a machine learning approach to monitor real-time fraud rate in Near Field Communication (NFC) card transactions. Built on the expanding use of (NFC) technology for contactless payments, the study responds to the emergent threat of fraud that revolves around NFC transactions. The application of machine learning algorithms in an Android application will seek to identify transaction trends, identify irregularities, as well as give the users instant notifications. The system employing supervised learning techniques measures transaction attributes like frequency, location, and transaction values to learn deviations from standard. The first set of data were collected, cleaned and split into a training set and a test set and is capable of reaching a near perfect score in recognizing fraudulent transactions. The study also describes how users experience the effectiveness of the promised functionalities of apps, such as usability, accuracy of information, and receipt of real-time alerts. Having analyzed the outcomes, the author concludes that integrating machine learning into the workflow is a reasonable way to boost the level of security of NFC operations as a win-win solution for customers and financial organizations. Subsequent versions of this technology may make them more effective in anticipating emerging fraudulent techniques and integrate this solution into more various spheres of cybersecurity.