Real-Time Transaction Monitoring: Combining AI, Big Data, and Biometric Authentication for Secure Payments

Real-time transaction monitoring is an essential part of maintaining the security of payment systems. With the increasing number of online transactions, financial institutions face the challenge of detecting and preventing fraudulent transactions in real time. This paper discusses a real-time transaction monitoring system developed using a combination of AI, big data, and biometric authentication. The system has been implemented as a workbench and is divided into five modules. The transaction information is captured and analyzed for fraud detection in the monitoring and analysis module. The transaction analysis is based on big data analysis technology, which incorporates transaction information, customer information, and biometric data for AI modeling. The biometric authentication technology is used to enhance the security of the payment process. The biometric templates are created during customer registration and verified during subsequent transactions. The digital signatures generated using biometric data prevent unauthorized access to customer information. The monitoring and analysis module of the system analyzes the uncertainty and time series of the transaction data. The uncertainty analysis identifies the transaction data that lacks sufficient information for effective fraud detection. Time series analysis is used to detect suspicious transactions occurring at unusual times. The monitoring and analysis module integrates a fraud detection model based on AI technology trained using big data analysis results. It also incorporates additional analysis models to monitor the uncertainty and time-series characteristics of the transaction data. The detected frauds are classified as high, medium, and low risk and reported using different levels of monitoring. High-risk frauds are immediately blocked and reported to the fraud management unit for investigation. Low-risk frauds are flagged for later knowledge updating. The system is trained adaptively, using new knowledge acquired from fraud investigations to retrain the AI model.