Synergistic Integration of Machine Learning and Behavioural Analytics for Fraud Mitigation in the Unified Payments Interface Ecosystem: A Socio-Technical Evaluation

This paper focuses on enhancing the security and reliability of digital payment transactions by developing an advanced UPI fraud detection system. The primary objective is to leverage machine learning algorithms and data analytics to analyse transaction patterns and detect anomalies that may indicate fraudulent activity. The system is designed to effectively address various types of UPI fraud, including phishing, identity theft, and unauthorized transactions. Additionally, it aims to incorporate a real-time monitoring mechanism capable of promptly identifying suspicious activities and triggering immediate alerts for intervention. The scope of this work extends to the integration of cutting-edge technologies such as artificial intelligence, machine learning, and data analytics, enabling the creation of a sophisticated fraud detection model. By analysing massive datasets of UPI transactions in real time, the model will uncover patterns, anomalies, and trends associated with fraudulent activities, thereby strengthening the defence against evolving digital payment threats.