Introduction
In today’s financial landscape, Artificial Intelligence (AI) stands out as a game-changer. In the past ten years, the Securities Markets have seen an unprecedented level of incorporation of machine learning, predictive analytics, and natural language processing in investment and trading processes that are driven by algorithms. AI tools are now being used by financial firms, brokerage houses, hedge funds, and retail investors to make fast and accurate decisions on market trends, price movements, investment risks, and trading strategies. These technological developments have played an important role in increasing market efficiency, lowering transaction costs and increasing liquidity. At the same time, however, they have established major regulatory problems for securities regulators of all jurisdictions and in this Article you will understand everything about Artificial intelligence and insider trading.
The capital markets in India have been undergoing a rapid sea change in the wake of the Securities and Exchange Board of India (SEBI) regulations to become digital-first. Many of the practices in the financial markets today, such as algorithmic trading, high-frequency trading (HFT), robo-advisory services, and portfolio management using AI, are a testament to the reliance on technology for financial decision-making. The financial markets today are heavily reliant on technology for financial decision-making, including algorithmic trading, high-frequency trading (HFT), robo-advisory services, and AI-driven portfolio management. These innovations are designed to boost economic efficiency, but are also fraught with challenging legal issues around liability, disclosure, market manipulation and insider trading. The traditional securities law is based on the premise that the act of making an unlawful trading decision is a deliberate one by human beings. However, AI systems work by analysing vast amounts of data, learning from transactions and making changes to their actions, and completing transactions with limited human involvement. This development makes it more difficult to apply current insider trading laws, which are based on knowledge, intent and possession of unpublished price sensitive information (UPSI).
Insider trading is a long-standing problem that has been perceived as a significant issue for integrity in the markets, as it erodes faith and trust in the markets and negates the equality of having information. Insider trading is regulated by Indian law, the Securities and Exchange Board of India Act, 1992 and the Securities and Exchange Board of India (Prohibition of Insider Trading) Regulations, 2015. These regulations prevent trading during the time that the insider holds the UPSI and create a fiduciary duty for an insider to ensure market fairness. However, the use of AI can create new types of informational inequality that are not easily covered by current laws and regulations. They can use sophisticated algorithms for advanced pattern recognition to discover confidential market signals, can also use publicly available information to make an impression on non-public information, and can freely use trading strategies without manual instructions. The developments not only make it hard to distinguish between legitimate market information and illegal use of confidential information, but they also cause these two concepts to merge.
International regulators have started taking action on these issues by looking at the regulation and governance of AI in financial markets. The U.S., E.U., and U.K. regulatory bodies have come to understand that the risks to the market posed by AI go beyond what might be considered conventional fraud or market abuse. They are working on algorithmic accountability, explainability, supervisory technology and strengthened compliance requirements for financial institutions implementing AI-powered trading systems. The developments in these jurisdictions offer insights to other jurisdictions like India, where securities regulation is still in early stages and has yet to be developed specifically for AI.
In this context, the article critically reviews the ability of the current regime of insider dealing to regulate the securities market in India in the context of using AI. It explores the legal principles underlying the regulation of insider trading, discusses the new opportunities and threats that AI trading brings, contrasts the global approaches to regulation and suggests changes that enhance the regulatory framework while maintaining innovation. While the current securities laws in India appear to be comprehensive enough to govern traditional insider trading, there is a need for specific adaptations to tackle the unique nature of AI and automated trading in finance.
How AI can Revolutionise the Securities Market in India?
Artificial Intelligence has been woven into today’s securities markets, bringing about a change in the way financial data is gathered, analysed and used to inform investment decisions. AI-powered systems are capable of learning and adapting over time, which allows them to refine their performance continuously, as opposed to traditional software that can only execute specific tasks based on pre-programmed algorithms. Unlike traditional software systems, AI-powered systems can continuously adapt and learn from data, allowing them to make accurate predictions. As a result, increasingly investment decisions are made using predictive models that are able to analyse millions of market variables at the same time.
The use of AI in India’s securities market has been on the rise as the overall securities market has been digitalised. The adoption of AI-driven technologies has become commonplace in the stock exchanges, brokerage firms, and other investment firms, such as mutual funds and institutional investors, as they strive to boost their operational efficiency and maximise returns on investments. AI-powered tools have become a staple across stock markets, brokerage firms, mutual funds and other institutional investors, helping them to streamline operations and optimise investment outcomes. AI applications are used to benefit multiple aspects of securities trading, such as algorithmic trading, high frequency trading, portfolio optimisation, risk assessment, fraud detection, compliance monitoring, customer advisory services and market surveillance.
One of the major areas of application of AI in India’s capital markets is algorithmic trading. The Algorithmic Trading model allows computer programs to place trades automatically, following specific trading rules, such as price movements, trading volume, and market volatility, just to mention a few financial indicators. High-frequency trading (HFT) also contributes to this ability, allowing traders to make thousands of trades in just a fraction of a second, a speed that is much faster than what any human can process. These technologies are also helping to enhance liquidity and tighter spreads, but can also create a challenge for regulators as trades can happen without direct human involvement.
Another major area where AI has made a significant impact in the investment industry is in the creation of robo-advisors. These automated systems offer investors financial guidance based on their risk tolerance, financial goals, and market conditions. Machine learning algorithms analyse past market trends, macroeconomic data, corporate communications, and live market data to suggest investment decisions for specific investors. Likewise, institutional investors are increasingly turning to AI-driven predictive analytics to forecast market trends, uncover undervalued instruments and also to refine portfolio allocation.
Another field that has been developing is the real-time analysis of corporate announcements, financial news, earnings reports, social media conversations, and macroeconomic publications using Natural Language Processing (NLP) technologies. AI systems can analyse structured and unstructured data in real time, picking up on market trends that human investors might not notice until later in the process, allowing them to make quicker trades with greater precision. This kind of power increases informational efficiency, but also has implications of unfairness, information asymmetry, and avoidance of current insider trading laws.
This growing reliance on AI is a double reality. At the same time, innovation in technology bolsters market efficiency, better investment decision-making and greater regulatory oversight. At the same time, there are new legal risks and dangers from self-determination, black-box algorithmic processes, and advanced data analysis, which undermine the basic tenets of securities regulation.
The current regulatory framework for Insider Trading under the Indian
The anti-insider trading rules are an important part of securities regulation and are designed to ensure that all investors have the same information when they invest in the securities market. The intent behind the laws on insider trading is to ensure market integrity (no one should be allowed to use information they know that the public doesn’t, for their own benefit). The legal regime regulating insider trading in India is mainly provided by the Securities and Exchange Board of India Act, 1992 (SEBI Act) and the regulations issued by SEBI to prohibit insider trading, namely the SEBI (Prohibition of Insider Trading) Regulations, 2015 (PIT Regulations). All these instruments aim at the prevention of unfair trading practices, consumer confidence and transparency in securities markets in India.
An “insider” is defined broadly in the PIT Regulations, 2015, to include any person who is connected with a company or who has access to or has Unpublished Price Sensitive Information (UPSI) in his possession. Information about a company or its securities that is not generally available and that, if disclosed to the public, would be likely to materially affect the price of the securities is referred to as unpublicized information. Typical information is financial results, dividends, mergers and acquisitions, capital structure changes, major litigation, key managerial appointments and significant business developments.
The 2015 Regulations extend the definition of trading while “in possession of” UPSI as compared to previous regulatory requirements, which required proof of actual use of such information. Once UPSI has been acquired, it then becomes the role of the accused to prove that the trade was conducted under one of the statutory exceptions or was otherwise lawful. It is in line with international best practices, which acknowledge the difficulties of establishing the subjective purpose of securities transactions.
The regulatory regime also imposes a heavy compliance burden on the listed companies and market intermediaries. The companies are obliged to put in place codes of conduct, have structured digital databases of people who have access to the same UPSI, to define fair disclosure policies and appoint compliance officers who will monitor insider trading risks. These things taken together aim to limit the potential for the misuse of confidential corporate information through trading windows, pre-clearance procedures and ongoing disclosure. Such compliance systems presume that insider trading is a result of human wrongdoings: a situation in which one clearly identifiable individual uses confidential information to their own advantage.
The interpretation of the provisions of insider trading by Indian courts and SEBI has always been favourable towards investor protection and market fairness. In Rakesh Agrawal v. SEBI, the Securities Appellate Tribunal pointed out that the rules on insider trading ought to be interpreted in the context of the overall aim of maintaining market integrity instead of just penalising unscrupulous practices. Likewise, in N. Narayanan v. Adjudicating Officer, SEBI, the Supreme Court reiterated that the securities regulation is of public interest and has to maintain investor confidence and fairness in the capital markets. The Court acknowledged the importance of financial markets requiring transparency, and that loss of confidence in financial markets is a loss of confidence that ultimately affects economic development.
Another important case is SEBI v Rakhi Trading Pvt. Ltd., where the Supreme Court has confirmed the powers of SEBI in investigating sophisticated market manipulation by means of algorithmic trading strategies. The case was about fraudulent and manipulative trading, not about insider trading, but it did so show that the judiciary could recognise that technological advancements can create a market abuse that is more difficult than traditional types of trading. The ruling reaffirmed the Securities and Exchange Commission’s view that securities laws need to keep pace with the changing nature of markets and technology.
India’s overall regime on insider trading is a well-established and technology-agnostic legal regime that can tackle traditional securities trading offences. If the concepts of “possession”, “communication”, “intent”, and even “trading” lose meaning in the context of investment decisions being increasingly made by autonomous or semi-autonomous systems, however, this is certainly not a trivial issue.
Artificial Intelligence and the new challenges for the regulation of insider trading
AI revolutionises the way financial data is gathered, analysed, and applied. AI systems can analyse millions of data points at a time, constantly updating their analytical models via machine learning, and make trades in milliseconds, which is not possible for human traders. The change poses new problems for the regulation of insider trading, since the current law was devised to control the behaviour of humans rather than computing programs.
The first regulatory question is about which sources constitute informational advantage. There are cases of conventional insider trading that use information that has been intentionally disclosed to an insider. AI systems, however, often make very accurate predictions using thousands of publicly available datasets. Machine learning models can make these predictions based on sophisticated pattern recognition, without ever touching traditional UPSI, and without breaking any confidentiality of the company’s activities. For example, an AI system that can analyse data from satellite imagery, supply chain logistics, social media activity, patent applications, hiring data, shipping logs and financial disclosures could accurately and precisely foresee a looming merger or earnings announcement.
This phenomenon leaves the question unanswered if this is a legitimate market investigation or a criminal informational exploitation. There are no existing laws against insider trading based upon deliberate analysis of public information, although there are laws against trading on non-public information. However, with AI’s increasing ability to piece together confidential information from scattered, publicly accessible information, it’s possible that regulators will have difficulty deciding if there is an illegal informational imbalance.
The second challenge is related to algorithmic autonomy and accountability. Once deployed, modern trading algorithms are often used with little human supervision. Machine learning models constantly adapt their behaviour to the newly acquired market information, which makes it difficult for even their developers to explain each of their trading decisions. The responsibility of the legal entity that is actually responsible for the transaction often becomes more complicated, particularly when an AI system carries out the transaction that seemingly benefits from confidential data. There may also be a liability for the software developer, the brokerage firm, the compliance officer, the investment manager or the institution implementing the algorithm. Legislation for securities trading assumes that people make conscious choices to trade, and few guidelines are provided when autonomous systems make their own investment decisions.
There’s the “black-box” issue as well, which has to do with the lack of transparency around the decisions that an AI makes. A significant number of AI and, even more so, the advanced AI models, especially those that are deep learning-based, offer precise predictions but lack the ability to provide clear explanations of the conclusions drawn. This is a lack of explainability – and it makes it very difficult for regulators to conduct investigations. While the SEBI investigators may be able to detect unusual trading activity, they cannot determine whether the algorithm used a legitimate market analysis, inadvertently included confidential information or took advantage of an informational advantage that is not allowed. The rules and principles of evidence, which depend on proof of knowledge or purpose, are more difficult to apply in cases where the underlying reasoning process is not readily discernible.
Machine intelligence also increases the risk of insider trading through AI, where artificial entities manipulate AI to cover up unlawful activities. Rather than making trades personally, the people with UPSI can use sophisticated algorithms to split up trades among a number of accounts, time trades for optimal execution or mask suspicious trading patterns among thousands of legitimate transactions. These practices render the identification of these trades by the regulators very difficult, and make the identification of insider trading more challenging in a traditional way.
Finally, the use of AI contributes to the overall concerns of market equity and investor trust. However, the capital markets can only work well if investors think that securities prices are not based on the private information of others. If this trend of AI systems surpassing traditional investors with technologies that are hard to keep up with continues to grow, the integrity of the market may gradually falter. As technology progresses, the use of AI should not be prohibited; securities regulation should foster the development of AI to enhance and not supplant the basic principles of capital markets, which include fairness, transparency and equal opportunity in efficient capital markets.
Thus, the advent of AI not only enables novel ways of engaging in insider trading but also poses a conceptual challenge to the laws against insider trading. As financial technologies become more independent, issues of purpose, ownership of information, cause, accountability, and standards for evidence must be rethought. The challenges highlighted illustrate the need for India’s insider trading regulations to be carefully developed to address the unique characteristics and dynamics of AI-driven securities markets.
Comparative Perspectives: The Regulation of AI and Insider Trading in Securities Markets Around the World
To date, no other development has as profoundly impacted how securities markets are regulated as the integration of Artificial Intelligence into these markets has shaken regulators around the world to direct their attention toward rethinking the way securities markets are supervised and insider trading is enforced. Currently, there is no specific legislation targeting a specific form of insider trading, but some regulators have taken a technology-neutral approach, along with AI governance principles, algorithmic accountability measures, and stronger supervisory powers. The analysis of these changes offers some key lessons for India as it strives to modernise securities laws while fostering technological innovation.
United States
Insider trading is still largely governed by Rule 10b-5 of the Securities Exchange Act of 1934, by judicial doctrines arising out of decades of securities litigation generally, and by the specific language of the statute. The U.S. Securities and Exchange Commission (SEC) has not introduced specific legislation about insider trading in AI; instead, it has focused on how current securities laws also govern securities trading that utilises AI and algorithmic investment systems.
In response to the rise of AI’s impact, the SEC has responded to increase its focus on algorithmic trading platforms, robo-advisory offerings, and predictive analytics used by financial institutions. Various regulatory developments have drawn attention to potential conflicts of interest, institutional interests over investor interest, and opaque decision-making processes that could enable market manipulation. This has led the SEC to call for more transparency, explainability, and human oversight of AI-driven financial technologies.
The American model shows that a politically neutral law can be effective when coupled with forward-looking guidance from the regulatory agencies and advanced oversight of the market. Instead of defining what insider trading is, regulators increasingly are paying attention to making sure that AI systems are compliant with current fiduciary and disclosure duties, and still hold market participants accountable for using AI tools.
European Union
In Europe, the regulatory thinking about AI is relatively dynamic. The EU has also adopted the Artificial Intelligence Act, the first-ever EU-wide legal framework on AI based on risk categories, while at the same time, the EU has kept the Market Abuse Regulation (MAR) in place to address the enforcement of insider trading.
While not all financial trading systems are considered ‘high-risk AI’ under the AI Act, institutions using more advanced AI solutions should follow the principles of transparency, human oversight, documentation, risk management and accountability. The obligations, in addition to the current financial regulations make sure AI systems are explainable and auditable throughout their lifespan.
The European regulatory model acknowledges that the governance of AI should not only focus on avoiding illegal outcomes, but it should also address the design, deployment and monitoring of AI systems in advance of any harm. This proactive strategy helps to minimise the risk of regulatory gaps and to build consumer trust in new technology.
United Kingdom
The UK has instead taken a principles-based approach to AI regulation, avoiding in-depth regulations. The Financial Conduct Authority (FCA) actively fosters responsible innovation via its regulatory sandbox and improves its supervisory framework over algorithmic trading and financial technologies.
British regulators focus on governance, accountability and operational resilience rather than on a specific technology. Financial institutions should have good internal control, carry out periodic algorithmic audits, and provide for suitable human oversight of the automated decision-making process. This flexible structure allows for innovation while maintaining market integrity and investor protection.
Lessons for India
The latter approaches can be seen as general patterns in the regulation of international approaches. While advanced jurisdictions are not seeking to replace current insider trading rules, they are introducing and applying AI governance principles to traditional securities regulation, such as transparency, accountability, explainability, and institutional responsibility.
India’s insider trading regime already has a technologically neutral base on which it is possible to regulate traditional insider trading. But, in contrast to the European Union or the United States, India does not have specific regulatory clarity about the use of AI in securities markets. The rapid pace of AI adoption could potentially provide a positive opportunity for SEBI to adopt internationally recognised principles of good governance and tailor them to India’s rapidly growing digital economy.
Enhancing the regulatory governance system in India
The securities regulatory landscape of India has traditionally shown great adaptability in reacting to financial innovation. However, there are challenges arising with the introduction of AI into securities markets, which go beyond traditional insider trading enforcement. The goal should not be to control artificial intelligence itself, but to prevent its use in ways that compromise the integrity of the markets, investor confidence or regulatory accountability.
SEBI must first have specific regulations for the use of AI in the trading of securities. This guidance should set minimum standards for transparency, algorithmic governance, record keeping, and risk management, and acknowledge the importance of proprietary trading technologies to the commercial end of the business.
Secondly, financial institutions using AI-powered trading systems should keep algorithmic audit trails that can be used in regulatory inquiries to explain major trading decisions. The introduction of explainability mechanisms would significantly strengthen the capability of SEBI for the investigation of suspicious transactions without affecting the fairness of the regulated entities.
Thirdly, human responsibility should be the main focus of securities regulation. The use of AI systems should not be a substitute for legal actors but a means of supporting them in making decisions. Investment firms, brokerage houses, asset managers and listed companies must continue to be legally liable for any trading decisions made via AI systems under their control. Human accountability will not develop if it is not explicitly present and maintained in the system.
Fourth, SEBI needs to significantly enhance its own technological infrastructure, such as by investing in AI-driven tools and systems that can handle vast amounts of transactional data, detect abnormal trading activities and patterns, and monitor algorithmic activity in real time. With the adoption of advanced AI technologies by market participants, regulators need to have similar expertise to be effective in their duties.
Lastly, there should be a greater focus on interdisciplinary collaboration of the law, computer science, economics, data science, and financial regulators in India. The regulation of AI demands a knowledge that goes beyond simple legal analysis, especially related to machine learning models, algorithmic bias, and computational explainability. This collaboration would help enable evidence-based policy-making and help to maintain the proportion between the risks of technology and regulatory interventions.
Conclusion
The securities market around the world is undergoing an AI revolution, altering the way financial data is analysed, investment decisions are made, and securities transactions are executed. The technological advancements present potentially significant economic advantages in terms of greater efficiency, improved liquidity, lower transaction costs, and advanced market analysis. But they also question some of the assumptions that underlie traditional insider trading laws.
The current insider trading regime in India, as in the SEBI Act, 1992 and the SEBI (Prohibition of Insider Trading) Regulations, 2015, is relatively broad enough to address classic insider trading offences. However, challenges arise with the application of concepts like possession of unpublished price-sensitive information, trading purpose, causation and evidentiary standards when the investment decisions are produced by autonomous or semi-autonomous AI systems. This trend of using machine learning, predictive analytics and algorithmic trading therefore, highlights significant regulatory omissions and gaps, which were not foreseen by the law.
By examining past regulatory experiences, including the U.S., we can see that the regulatory framework does not need to be replaced by a new approach to securities regulation, but rather supplemented by specific standards for AI governance, such as transparency, explainability, human oversight and institutional accountability. International regulatory developments offer India valuable lessons for designing a structure for a framework that is appropriate for India’s financial system.
In the end, the question for Indian regulators is what is the right balance between the promotion of technological innovation and the maintenance of the integrity of the market. AI governance should not stifle financial innovation or allow for the complexity of technology to get in the way of legal accountability. The shift to AI means that a forward-looking regulatory framework that can keep up with the rapid pace of technological advancement will be crucial for investor confidence and the continued fair, transparent and competitive nature of India’s securities markets.
Frequently Asked Questions (FAQs)
1. What is insider trading?
Insider trading is buying or selling securities using unpublished price-sensitive information (UPSI).
2. How does AI affect securities trading?
AI automates market analysis and trading decisions using machine learning and predictive analytics.
3. Is AI-driven insider trading illegal in India?
Yes, if AI is used to misuse UPSI, it may violate SEBI’s insider trading regulations.
4. Does India have laws specifically regulating AI in trading?
No. India currently relies on existing SEBI laws without AI-specific regulations.
5. Why is AI a challenge for regulators?
AI systems can make autonomous decisions that are difficult to explain and investigate.
6. What is UPSI?
UPSI is confidential information that can significantly affect a company’s share price when made public.
7. Can AI itself be held legally liable?
No. Legal responsibility rests with the individuals or institutions using the AI.
8. How are other countries regulating AI in financial markets?
Countries like the US, EU, and UK are introducing AI governance, transparency, and oversight measures.
9. What reforms should SEBI introduce?
SEBI should adopt AI-specific guidelines, algorithm audits, and stronger market surveillance.
10. What is the main conclusion of this article?
India should strengthen its insider trading framework by incorporating AI governance principles while encouraging technological innovation.
Nitish Kumar.P is a 5th year B.A.LLB student at PES University, Bengaluru. His research expertise is in Technology Law, Artificial Intelligence and Law, Intellectual Property Rights, Data Protection and Privacy Law, Corporate law, Commercial litigation and Mergers & Acquisitions. His teaching and research interests focus on the nature and future of the intersection between law, technology, and commerce, and he frequently writes about these subjects. His research interests are in the area of emerging legal issues related to artificial intelligence, digital governance, IP and corporate regulation, and Nitish seeks to make a contribution to contemporary legal scholarship and informed policy debate.