AI-Driven Fraud Detection: Safeguarding Financial Data in Real-Time

Abstract

In an era of rapidly evolving cyber threats, financial institutions face unprecedented challenges in protecting sensitive data from sophisticated fraud tactics. Traditional fraud detection methods, while effective to a point, struggle to keep up with the speed, scale, and complexity of modern cyberattacks. This paper explores an AI-driven approach to fraud detection, leveraging machine learning algorithms, deep learning models, and real-time data analytics to identify and prevent fraudulent activities with greater accuracy and speed. By continuously learning from vast datasets, AI systems adapt to new fraud patterns, enabling predictive insights that go beyond rulebased systems. This adaptive capability ensures robust protection against emerging threats, minimizes false positives, and improves the efficiency of fraud response processes. Additionally, the integration of AI with big data enhances risk management and regulatory compliance, aligning financial institutions with stringent data security standards. This study presents a comprehensive analysis of AI-powered fraud detection systems and their transformative impact on securing financial data in real-time