Fraud Detection in UPI Transactions

The introduction of Unified Payments Interface (UPI) has changed the digital transaction landscape in India, notwithstanding the possibility of several fraudulent operations. The use of machine learning (ML) techniques for UPI fraud detection is covered in brief here. In order to identify unusual patterns that might point to fraudulent activity, machine learning (ML) models examine transaction data using a combination of supervised, unsupervised, and semi-supervised learning techniques. As essential to optimizing the models’ performance, effective feature selection and engineering techniques further improve the process. Additionally, combining anomaly detection algorithms with cooperative techniques improves the accuracy of fraud identification. The resilience of UPI security is increased, and quicker responses to new fraud techniques are made possible by the implementation of real- time monitoring mechanisms and adaptive learning strategies. Financial institutions can enhance the security of UPI transactions and preserve their integrity while fostering user trust by utilizing machine learning capabilities.