Authors: Aadhithyan K, Pranauv Raaj N
Abstract: Card-based transactions and modern digital payment systems face sophisticated and rapidly evolving security threats, necessitating advanced fraud detection methods. Traditional approaches, often reliant on fixed rules and descriptive analytics, are slow to adapt to new fraud schemes and struggle with the volume of real-time transactions. This presentation analyzes the effectiveness of AI-driven fraud detection, specifically focusing on the integration of Real-Time Analytics, Deep Learning (DL), and Agentic AI systems to enhance security and prevent financial losses. The study highlights that DL models, such as hybrid Recurrent Neural Networks (RNNs) combined with attention mechanisms, offer superior performance by modeling sequential data and addressing challenges like data imbalance. Furthermore, adopting the Deep Learning–Sector–Governance (DLSG) framework is crucial, as it ensures that technical innovations are aligned with sector-specific constraints and regulatory requirements, such as the need for explainability and data privacy. The synthesis of these technologies provides a proactive, adaptive solution to safeguard complex financial ecosystems.
