AI-DRIVEN SURVEILLANCE AND BLOCKCHAIN INTEGRATION FOR INSIDER TRADING DETECTION: A REGULATORY FRAMEWORK FOR SEBI

Abstract

The rapid evolution of financial markets, coupled with the limitations of traditional regulatory mechanisms, poses significant challenges in detecting and preventing insider trading. This study introduces an AI-driven trade surveillance framework integrated with blockchain-based compliance mechanisms to enhance SEBI’s regulatory oversight. The proposed approach leverages deep learning models (CNN + LSTM) for real-time anomaly detection in trading activities, while blockchain-powered smart contracts ensure secure, transparent, and tamper-proof trade records. Additionally, Explainable AI (XAI) improves the interpretability and legal admissibility of AI-generated evidence, addressing concerns related to regulatory transparency and judicial scrutiny. The findings indicate that AI-powered surveillance significantly enhances the accuracy of insider trading detection, while blockchain strengthens compliance, auditability, and cross-border regulatory cooperation. Integrating AI and blockchain into SEBI’s enforcement framework can accelerate regulatory interventions, bolster whistleblower protections, and uphold financial market integrity. Future research should explore extending AI-driven surveillance to decentralized finance (DeFi) ecosystems and refining blockchain-based governance models to align with global financial regulations.