The Algorithmic Advantage: AI-Driven Insider Trading And Regulatory Gaps In Indian Regulation
- IJLLR Journal
- Jan 15
- 2 min read
Anoushka Sinha, B.A.LL.B., KPMSOL, NMIMS Mumbai
ABSTRACT
With the help of artificial intelligence (AI) and machine learning (ML), the way financial markets operate is being changed significantly, especially in the areas of information handling and decision-making. This paper studies how the use of autonomous trading algorithms has led to such forecasts that they can be mistaken for the effects of Unpublished Price Sensitive Information (UPSI), thus raising a big regulatory question. Insider trading laws like the SEBI (Prohibition of Insider Trading) Regulations, 2015 in India are still based on human-centric ideas of the mind (intent) and things (possession), which are not enough to address the problem of AI-driven trading raised by these complexities. By comparing the Indian law with international best practices, the research uncovers the inadequacies of the Structured Digital Database (SDD) as a compliance tool and the problem of "Black Box" in understanding algorithmic decision-making.
Initially, the study aims at locating elements to underlie the trade of moving away from the proof of intent to focusing on the consequences of trade operations. It draws on the examples of the European Union’s MiFID II and the U.S. regulatory approach under FINRA to motivate such a shift. It envisions changes such as the imposition of the examination of trading algorithms as a compulsory function, the growth of the degree of XAI (Explainable AI) transparency, and the engagement of developers in a dialogue so as to make them responsible. The target is to raise a legal framework adaptable to the new era that would ensure that the market remains fair and that investor confidence is retained in the AI-driven finance world.
The intent is to create a modern legal framework that would support market fairness and investor confidence in the AI-empowered financial era.
