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Insider Trading In India: Evaluating SEBI’s Regulatory Gaps And The Promise Of AI-Based Surveillance




Rajdeep Mukherjee, O.P Jindal Global University


ABSTRACT


Insider trading remains one of the most difficult forms of market abuse to regulate in India. Currently, there are millions of people entering the Indian securities market every day. They are all prejudiced by the inequity that is created due to insider trading. India’s primary market watchdog-SEBI- has introduced a robust and comprehensive legal and regulatory framework as per the SEBI (Prohibition of Insider Trading) Regulations, 2015 to counter this problem. However, these regulations continue to face multiple challenges, specifically in relation to its enforcement. This paper critically evaluates the strengths and limits of SEBI’s insider trading regime, focusing specifically on institutional inefficiencies, delays in procedure and the low rate of successful convictions.


This paper further examines the state at which modern technological advancements such as artificial intelligence(AI) and machine learning(ML) have been implemented in market surveillance mechanisms. Drawing comparative insights from the U.S Securities and Exchange Commission and their utilisation of systems like ARTEMIS and MIDAS, the paper evaluates how SEBI’s own initiatives such as IMSS, DWBIS and the Data Lake project may lead to a more efficient Indian regulatory system.


Keywords: Insider Trading, SEBI, Market Surveillance, Artificial Intelligence, Enforcement Challenges, Unpublished Price Sensitive Information (UPSI), Data Analytics, DWBIS, SEC, Comparative Regulation



Indian Journal of Law and Legal Research

Abbreviation: IJLLR

ISSN: 2582-8878

Website: www.ijllr.com

Accessibility: Open Access

License: Creative Commons 4.0

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All research articles published in The Indian Journal of Law and Legal Research are fully open access. i.e. immediately freely available to read, download and share. Articles are published under the terms of a Creative Commons license which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

 

Disclaimer:

The opinions expressed in this publication are those of the authors. They do not purport to reflect the opinions or views of the IJLLR or its members. The designations employed in this publication and the presentation of material therein do not imply the expression of any opinion whatsoever on the part of the IJLLR.

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