Machine Learning-Based Approach for Detecting Online Payment Fraud
V. S. Naresh, G. Venkata Sridevi, Popuri Srinivasarao, N. Hema Kiran, Ch. Sai Babu, P. Lazar Dan · 2024
Online payment systems have become an integral part of the modern digital economy, facilitating convenient and efficient transactions. However, they are also susceptible to various types of fraudulent activities. The potential for substantial financial losses to both businesses and consumers underscore the urgency of addressing this escalating threat. Consequently, there is a crucial need to develop resilient fraud detection systems. The main goal is to construct an effective and efficient online payment fraud detection system that can promptly identify and thwart fraudulent transactions in real-time., thus enhancing security and preserving the integrity of digital payment platforms. Our methodology begins by collecting a comprehensive dataset containing transaction information; including transaction amount, location, time, and various other relevant features. This dataset forms the basis for conducting training and evaluating our machine learning models, such as the Random Forest Classifier and Logistic Regression models, which can help detect fraudulent activities.