ABSTRACT: The quick acceptance of digital payment systems such as the Unified Payments Interface (UPI), mobile wallets, online banking, and transactions using cards has led to greater convenience and accessibility in the area of financial services. At the same time, the increasing number of digital transactions has also raised the likelihood of fraudulent activities. Since traditional methods based on rules may have limitations when it comes to detecting complex and changing transaction patterns, this study introduces UniCon FraudX, an AI-driven digital payment fraud detection and risk monitoring system which uses machine learning techniques to spot potentially suspicious transactions. The system carries out data preprocessing, feature engineering, transaction analysis, and machine learning-based classification in order to tell the difference between normal and possibly fraudulent transactions. Apart from making predictions, the system is also intended to offer the fraud probability, various risk levels, a transaction history, and visual analytics via a web-based dashboard. Where suitable data is available, transaction features such as amount, type, frequency, time, and location can be analysed. The study includes a user survey in order to look at people's experience with digital payments, their level of awareness about fraud, and their expectations concerning automated fraud detection. Several machine learning models can be assessed using measures such as accuracy, precision, recall, and the F1-score to judge how effective they are. The system proposed seeks to establish an accessible and explainable method of identifying and monitoring fraud risk in digital payments, while at the same time laying the groundwork for future developments like real-time detection, explainable artificial intelligence, and advanced behavioural analysis.
KEYWORDS: Digital Payment, Fraud Detection, Machine Learning, Artificial Intelligence, Risk Monitoring, Transaction Analysis, Explainable AI, UPI.
UNICON FRAUDX — AI-POWERED DIGITAL PAYMENT FRAUD DETECTION AND RISK MONITORING SYSTEM.
AAKASH JAYAWANT DAKE