SMART FRAUD DETECTION: THE ROLE OF AI IN SECURING FINANCIAL TRANSACTIONS

Abstract

The rise of digital wallets—such as PayPal, Google Pay, and Apple Pay—has revolutionized financial transactions, offering unparalleled convenience. However, this shift has also led to an increase in financial fraud, exposing vulnerabilities in traditional security measures. Rule-based fraud detection systems, once the industry standard, struggle to keep pace with increasingly sophisticated cybercriminals. This research explores how Artificial Intelligence (AI) and Machine Learning (ML) can enhance digital payment security by detecting and preventing fraudulent transactions in real time. We evaluate advanced AI/ML models, including Random Forests and Recurrent Neural Networks (RNNs), demonstrating their superior accuracy in identifying fraudulent activity while minimizing false positives—a critical improvement over conventional methods. Unlike static rule-based systems, AI-driven solutions adapt dynamically to emerging fraud patterns, offering robust protection against evolving threats. Despite their potential, challenges remain, including computational costs, model interpretability, and data privacy concerns. Our findings suggest that integrating AI/ML-based fraud detection into digital wallet infrastructures can significantly enhance security, safeguarding users’ financial data while maintaining transaction efficiency. This study highlights the transformative role of AI in creating smarter, safer financial ecosystems.