UPI FRAUD DETECTION USING MACHINE LEARNING

In recent years, the rise of digital payment systems, particularly Unified Payments Interface (UPI) in India, has revolutionized financial transactions, making them more accessible and efficient. However, this surge in usage has also led to an increase in fraudulent activities, posing significant risks to consumers and financial institutions. This paper presents a comprehensive approach to UPI fraud detection, employing advanced machine learning techniques and data analytics. We explore various types of UPI fraud, including phishing, account takeover, and unauthorized transactions. By utilizing a dataset comprising transaction histories, user behavior patterns, and contextual features, we develop a predictive model aimed at identifying fraudulent transactions in real-time. Our findings demonstrate the effectiveness of ensemble methods and anomaly detection algorithms, achieving a notable improvement in detection accuracy and reduction in false positives. The proposed framework not only enhances the security of UPI transactions but also provides insights for stakeholders to implement robust preventive measures, ultimately contributing to a safer digital payment ecosystem. The methodology begins with data collection, gathering a diverse dataset that includes transaction records, user profiles, device information, and contextual features such as transaction timing and location. Feature engineering plays a crucial role in enhancing model performance by identifying key indicators like transaction frequency and average transaction amounts. Various machine learning algorithms are then explored, including decision trees for pattern interpretation, random forests for accuracy improvement, support vector machines for classification, and neural networks for capturing complex data relationships. The Unified Payments Interface (UPI) has transformed the landscape of digital payments in India, facilitating seamless transactions for users and merchants alike. However, this convenience has also attracted fraudsters, leading to various types of financial crimes, including phishing attacks, account takeovers, merchant fraud, and transaction reversal fraud. The results demonstrate significant improvements in detecting fraudulent activities, characterized by reduced false positive rates and enhanced detection accuracy. By leveraging ensemble methods, the predictive model achieves a robust sensitivity and specificity, ensuring that legitimate transactions are not incorrectly flagged.