Paper Contents
Abstract
ABSTRACTIn the face of increasing cyberthreats, this report highlights the vital role that online payment fraud detection systems play in protecting digital transactions by providing a comprehensive analysis of current systems. The sophistication of fraud schemes targeting businesses and individuals is increasing along with the growth of online commerce. Focusing on cutting-edge techniques like machine learning, pattern recognition, and anomaly detection, the study examines the goals and tactics that facilitate efficient fraud detection. Analyzing transaction data in real time is essential for spotting fraud before it causes significant losses. These systems strengthen online transaction security, increase consumer trust, and safeguard financial assets by using sophisticated algorithms to examine large amounts of transaction data for anomalies that could point to fraud. The report tackles the need for robust solutions that change with new threats by highlighting flexible fraud detection systems. It lists the prerequisites for efficient systems and highlights how crucial they are to spotting, stopping, and reducing different types of online fraud, such as identity theft and illegal transactions, while promoting constant technological development to guarantee safe, reliable online trade.
Copyright
Copyright © 2024 NAVANEETHA TALARI. This is an open access article distributed under the Creative Commons Attribution License.
