FINANCIAL TECHNOLOGY AND AI-DRIVEN FRAUD DETECTION IN REAL-TIME TRANSACTIONS

Abstract

Financial Technology (FinTech) has rapidly transformed the financial industry, particularly by enabling real-time payments, which streamline financial services. However, the speed of these transactions has significantly increased the risk of fraud, posing a major challenge to financial institutions. AI-driven fraud detection systems leveraging machine learning algorithms have emerged as powerful tools to combat this issue. These systems utilize predictive analytics, anomaly detection, and behavioral analysis to identify and block fraudulent transactions in real time. However, the effectiveness of these systems is heavily dependent on network infrastructure, particularly ultra-low-latency networks, which are crucial for timely fraud detection. This paper examines the integration of AI technologies with ultra-low-latency networks to secure real-time financial transactions and highlights case studies where financial institutions have successfully adopted these solutions. As AI and network infrastructure continue to evolve, their combined potential for proactive fraud detection is expected to grow, offering enhanced protection for real-time transactions in the future.