Introduction:
Artificial Intelligence (“AI”) and Machine Learning (“ML”) have emerged as transformative technologies that have reshaped global securities markets. Integration of AI/ML tremendously improves efficiency and accuracy and attracts investors, while it also presents newer regulatory and ethical challenges. Recognizing both the potential and perils of AI, regulators worldwide, including IOSCO, ESMA, SEC, and SEBI, are formulating frameworks to ensure responsible innovation without stifling technological advancement. The paper discusses the evolving regulatory landscape governing the use of AI/ML in the securities sector, focusing on consultation guidelines introduced by the Securities and Exchange Board of India (“SEBI”). It discusses in-depth these guidelines in comparison to the established frameworks of the United States and the European Union. The paper evaluates the extent of convergence in regulatory philosophy, identifies gaps in compliance, and gives recommendations to bring alignment in India’s approach for governance of AI with global best practices.
Part 1: Role of AI/ML in the Securities Market
The financial market is never behind in decoding how to make more money. It has always explored ways to expand its horizon beyond its existing capabilities. Therefore, keeping up with this attitude and technological pace, the market has adopted the use of AI and ML. Market participants, including investors, intermediaries, and regulatory bodies, utilise the AI/ML model to enhance their market performance by increasing efficiency and returns to investors. The integration of AI in financial markets has been ongoing since the 1980s, when APEXS, Inc. developed PlanPower, an AI-based financial technology designed to generate financial plans for individuals earning more than $75,000 annually.[1]
Let’s examine, with a focus on capital markets, how AI leverages a vast amount of data to identify patterns and generate informed investment responses. The domain of AI is pervasive; within this domain, there is a subfield called ‘machine learning’. ML can be described as developing a series of actions to solve a problem, called algorithms, that automatically optimise through experience with minimal or no human intervention. These techniques can identify patterns in vast amounts of data from more diverse and innovative sources.[2] The diversification and rapid growth of AI make it challenging to categorise its types; therefore, AI is often used as a collective term for all AI technologies.
In the capital market, the incorporation of AI has improved efficiency, accuracy, and risk management.[3]The most promising uses of AI are in investment management and customer service. Businesses use AI models to create investment portfolios, provide financial advice, and give clients trading recommendations and real-time insights.[4]This results in improved client satisfaction and retention, providing a competitive edge to firms that adoptthis technology.[5] However, just having the knowledge or experimenting with AI is not sufficient; understanding how the AI technology works and what are the risks involved are equally important.
World Economic Forum (“WEF”) and International Organisation of Securities Commissions (“IOSCO”), in their reports, provide a sound evidential basis for assessing where AI is most active and where regulatory priorities should be concentrated.[6]According to the reports, AI is significantly utilised in both front-office and back-office functions. IOSCO’s review documents that AI applications are particularly prevalent in client communications, algorithmic trading, robo-advising/asset management, as well as surveillance/fraud detection, with significant adoption also reported for predictive analytics, transaction automation, and internal productivity support.³ This distribution reflects a pragmatic prioritisation by firms of tools that either (a) scale client engagement and advisory services at low marginal cost, or (b) enhance trading performance and monitoring capabilities.
[1]Joanna England, FinTech: AI and the Future of Financial Services, FinTech Magazine (Oct. 30, 2025), https://fintechmagazine.com/financial-services-finserv/fintech-ai-and-future-financial-services.
[2]Financial Stability Board (FSB), Artificial Intelligence and Machine Learning in Financial Services(Nov. 1, 2017), https://www.fsb.org/uploads/P011117.pdf.
[3]Sunil Kumar Das, Shaista Anwar, Urvee Tulsyan, Yash Gupta, Rahul Vudatta& Syed Hassan Imam Gardezi, The Role of AI in Financial Markets: Impacts on Trading, Portfolio Management and Price Prediction, Journal of Electrical Systems (2024), https://www.jespublication.com
[4]Quantum Computing for Market Volatility Prediction, In the Valley Blog, https://inthevalley.blog/ai-generated-articles/industry-insights/fintech/quantum-computing-for-market-volatility-prediction/.
[5]World Economic Forum, Industries in the Intelligent Age: White Paper Series (2025), https://reports.weforum.org/docs/WEF_Artificial_Intelligence_in_Financial_Services_2025.pdf.
[6]International Organization of Securities Commissions (IOSCO), Artificial Intelligence in Capital Markets: Use Cases, Risks and Challenges (Consultation Report, 2025).