Augmenting Regulatory Intelligence: How Ai-Driven Behavioral Analytics Can Inform Sebi’s Oversight Framework in the Age of Algorithmic Trading and Retail Investor Proliferation

Abstract

In a time when the number of retail investors is growing quickly, the Securities and Exchange Board of India (SEBI) and other organizations have a revolutionary opportunity to improve market oversight through the incorporation of artificial intelligence (AI) into financial regulation. By combining computational power and human judgment, this study suggests a novel framework called Augmenting Regulatory Intelligence that uses AI-driven behavioral analytics to improve SEBI's supervisory capabilities. Capital markets are more susceptible to behavioral distortions like herding, overconfidence, and fear-of-missing-out (FOMO) dynamics as retail participation grows through digital brokerages and social media "influencers." These biases, which are well-documented in the literature on behavioral finance, have the potential to increase volatility and enable manipulative tactics like well-planned pump-and-dump schemes. Concurrently, market intermediaries' use of AI systems allows for high-frequency and data-intensive strategies that frequently surpass conventional regulatory monitoring tools, resulting in disparities in the technological capabilities of regulated entities and regulators. The study recommends the strategic use of machine learning and natural language processing (NLP)-driven regulatory technology (RegTech) solutions to rectify this imbalance. Large-scale transactional data and unstructured digital content can be analyzed by these tools to find early warning signs of coordinated manipulation or illogical investor behavior. SEBI can improve regulatory responsiveness while maintaining accountability by integrating AI-generated insights into human-led supervisory procedures. The study promotes a hybrid governance model in which artificial intelligence (AI) enhances rather than replaces human intelligence by drawing on interdisciplinary viewpoints from behavioral finance, regulatory governance, and public policy. The study adds to the current discussions on the moral application of AI in financial regulation and makes recommendations that are pertinent to policy for creating regulatory frameworks that are flexible, open, and behaviorally aware.