Surveillance Technology and Market Regulation: Are Regulators Equipped for the Algorithmic Age? — A Legal Examination of Regulatory Gaps in India

INTRODUCTION

The modern financial market is fundamentally shaped by data algorithms and real time analysis. High-frequency variant of Algorithmic Trading, has reshaped financial market by executing thousands of trades in milliseconds based on pre-coded strategies. Traditionally regulatory bodies were designed to monitor human decision-making in trading, but now it has to supervise machine, which evolve, learn on its own even creator can’t fully predict. This shift in market dynamics has led to the extinction of traditional form of market surveillance. Algorithms now account for a majority of trading volumes on major Indian exchanges, such as the National Stock Exchange (NSE), which reported in recent years that over 50% of its total trades involve some form of automated execution. While the Securities and Exchange Board of India (SEBI) has issued several circulars and discussion papers to regulate this space, the regulatory framework remains largely reactive, soft-law-based, and technologically constrained. As the market architecture becomes increasingly complex, the disconnect between technological sophistication and regulatory preparedness has become a critical issue affecting investor protection, market integrity, and systemic stability. The absence of a unified legal framework governing surveillance in algorithmic trading leaves a critical gap in Indian financial regulation. This lacuna not only hampers SEBI’s enforcement capability but also risks undermining investor confidence and market fairness.

This research paper seeks to address the central question: Are Indian regulators equipped to effectively govern market behaviour in the algorithmic age? To answer this, the paper adopts a legal and institutional lens, focusing on:

• The technological landscape of algorithmic and high-frequency trading in India;
• Existing regulatory mechanisms and their legal enforceability;
• Comparative analysis of surveillance frameworks in leading global jurisdictions.
• Structured recommendations for regulatory, legislative, and institutional reform.

SURVEILLANCE TECHNOLOGY AND ALGORITHMIC TRADING: AN OVERVIEW

Understanding Algorithmic Trading and Its Evolution

Algorithmic trading refers to the use of computer algorithms to automate trading decisions in financial markets. These algorithms execute buy or sell orders based on predetermined criteria such as price, volume, timing, or other mathematical models. Over the past two decades, algorithmic trading has evolved from simple rule-based systems to complex AI-driven engines capable of executing thousands of trades per second, often without human intervention. A particularly disruptive subset is High-Frequency Trading (HFT), which uses ultra-low-latency infrastructure to gain microsecond-level advantages over competitors. These systems exploit arbitrage opportunities, market inefficiencies, or rapid fluctuations in order books, often outpacing the capacity of traditional surveillance mechanisms to detect misconduct in real time.

Risks Associated with Algorithmic Trading

While AT and HFT have improved market liquidity and narrowed bid-ask spreads, they introduce new classes of risks:

• Market Manipulation: Algorithms can be programmed to manipulate prices through
•  practices such as spoofing (placing and then cancelling orders to create false demand) or layering (submitting multiple orders at different price levels to mislead market participants).
• Flash Crashes: Errant algorithms or feedback loops between competing systems can cause abrupt market collapses, as evidenced by the 2010 Flash Crash in the United States.
• Opacity and Accountability: Many algorithmic strategies are black-box systems whose decision-making logic is not transparent to regulators or even developers, making post-facto analysis difficult.
• Inequality of Access: High-frequency traders benefit from proximity hosting, low-latency data feeds, and advanced infrastructure that are unavailable to most retail investors.

Evolution of Market Surveillance Technology

Surveillance technology in financial markets traditionally relied on batch data analysis, where trading patterns were reviewed after market hours to detect abnormalities. However, with the advent of AT, such post-trade surveillance is no longer sufficient.

Modern surveillance systems increasingly deploy:

• Real-Time Trade and Order Book Monitoring: Capturing all quotes, modifications, and cancellations as they occur.
• Artificial Intelligence and Machine Learning: Used to detect anomalies, identify new manipulation patterns, and flag suspicious actors based on behavioural patterns.
• Consolidated Audit Trails (CAT): As implemented in the U.S., these systems integrate trading data from multiple sources to create a single, unified timeline of events for every trade and order.

In India, however, the surveillance infrastructure has not evolved at the same pace. While exchanges like NSE employ some level of real-time monitoring, SEBI’s centralised capacity to process and act on algorithmic data remains limited. Regulatory oversight is fragmented, and inter-exchange data consolidation is still a work in progress.

Legal Challenges of Surveillance in Algorithmic Contexts

The growing complexity of algorithmic trading systems creates significant legal and evidentiary challenges:

• Causality and Attribution: Determining who is legally liable when an algorithm causes market disruption—trader, developer, or firm—requires new legal standards.
• Due Process and Fairness: The use of AI in surveillance raises questions about explainability, audibility, and protection against arbitrary blacklisting.
• Jurisdictional Issues: Algorithms can operate across borders within milliseconds, often outside the territorial control of any one regulator.

These challenges demand a new generation of regulatory strategies—rooted in technological literacy, inter-agency cooperation, and statutory clarity—that move beyond the limitations of conventional enforcement models.

SURVEILLANCE TECHNOLOGY: A GLOBAL PERSPECTIVE

As algorithmic and high-frequency trading reshape global financial markets, regulators worldwide have responded with sophisticated surveillance tools and regulatory frameworks. These efforts reflect an understanding that traditional mechanisms of oversight—manual inspection, periodic audits, and ex-post analysis—are grossly inadequate in a trading environment dominated by speed, volume, and algorithmic opacity. The emergence of “SupTech” (supervisory technology) as a regulatory subfield underscores a global recognition that effective market supervision must be both proactive and technologically advanced. A comparative analysis of international frameworks reveals key insights that are instructive for India’s evolving approach.

United States: SEC and the Consolidated Audit Trail

In the United States, the Securities and Exchange Commission (SEC) has been at the forefront of integrating technology into market surveillance. The pivotal regulatory initiative in this context is the Consolidated Audit Trail (CAT), launched pursuant to Rule 613 of Regulation NMS[1]. The CAT mandates the collection of comprehensive data on every order, quote, and trade in the U.S. equity and options markets across all venues, effectively creating a unified audit trail. The objective of CAT is to allow regulators to reconstruct market events, detect abusive trading patterns, and identify bad actors across complex trading environments[2]. It also allows for faster investigation of market disruptions such as the 2010 Flash Crash—an event that was a significant catalyst for the project. The SEC and the Financial Industry Regulatory Authority (FINRA) use CAT to monitor and enforce compliance with securities laws in near real-time. Despite some delays in implementation due to concerns around cybersecurity and data privacy, CAT remains the most comprehensive surveillance infrastructure among global capital markets[3].

European Union: MiFID II and Market Abuse Regulation (MAR)

The European Union has similarly adopted a technologically progressive stance. The Markets in Financial Instruments Directive II (MiFID II), implemented in 2018, and the accompanying Market Abuse Regulation (MAR) represent a robust legal regime for governing algorithmic trading [4]. MiFID II specifically requires firms engaging in algorithmic trading to maintain adequate systems and controls to avoid disorderly trading and manipulation. It mandates pre-trade risk controls, continuous monitoring, and even registration of trading algorithms with competent authorities. Under MAR, market surveillance responsibilities are delegated to national competent authorities (NCAs), such as the Financial Conduct Authority (FCA) in the UK and BaFin in Germany, which employ AI-driven systems to detect unusual trading patterns[5]. Furthermore the Automated Surveillance and Analysis System (ASAS) employed by the European Securities and Markets Authority (ESMA) aggregates cross-border data to flag potential cases of market abuse, layering, spoofing, and quote stuffing[6]. These regulations also impose heightened compliance burdens on firms, including requirements to store and timestamp algorithmic decisions, maintain kill-switches, and regularly test algorithms under various market conditions. The result is a legal framework that not only facilitates enforcement but also prevents potential abuse by compelling firms to build safety and transparency into their trading infrastructure.

United Kingdom: Financial Conduct Authority (FCA)

The UK’s Financial Conduct Authority (FCA) has leveraged predictive analytics and machine learning in its market monitoring processes. Post-Brexit, the FCA has retained and extended several elements of MiFID II and MAR[7]. Its Market Data Processor system enables integration of transaction data across trading platforms, enabling surveillance staff to detect insider trading and market abuse in real time. The FCA’s strategy emphasises early detection and pre-emptive intervention, supported by sandbox mechanisms that encourage fintech innovation without regulatory blind spots.

INDIA’S REGULATORY FRAMEWORK FOR MARKET SURVEILLANCE

The Indian securities market is regulated primarily by the Securities and Exchange Board of India (SEBI), established under the SEBI Act, 1992, with a statutory mandate to protect investor interests and promote orderly development of the securities market[8]. While SEBI has taken significant steps to regulate market conduct and implement surveillance technology, the legal and operational frameworks have struggled to keep pace with the sophistication and velocity of algorithmic trading (AT) and high-frequency trading (HFT). This section critically examines the key elements of India’s market surveillance infrastructure, regulatory initiatives, and limitations in addressing emerging risks in the algorithmic age.

SEBI’s regulatory powers emanate from several statutes, including:

• The SEBI Act, 1992 – confers investigative, adjudicatory, and policy-making authority on SEBI.
• The Securities Contracts (Regulation) Act, 1956 (SCRA) – governs listing, trading, and market infrastructure institutions.
• The Depositories Act, 1996 – regulates dematerialisation and record-keeping of securities.

Despite these foundational laws, none provide a statutory definition or legal treatment specific to algorithmic or high-frequency trading. Consequently, SEBI has relied on its quasi-legislative powers to issue a series of circulars, guidelines, and advisories for regulating automated trading activity. While these instruments provide operational clarity, their enforceability is sometimes questioned due to their non-legislative nature.

SEBI’s 2012 Circular on Algorithmic Trading

In its most significant regulatory step, SEBI issued a circular in 2012 titled “Broad Guidelines on Algorithmic Trading”, which mandates the following key provisions:[9]

1. Exchange Approval – All algorithmic software must be approved by the relevant stock exchange prior to deployment.
2. Risk Controls – Pre-trade checks such as price bands, quantity limits, and order value limits are compulsory.
3. Order-to-Trade Ratio – Brokers must maintain a healthy ratio to discourage excessive order cancellation.
4. Throttling Mechanisms – To curb quote stuffing and system abuse, limits were placed on the number of messages per second.
5. Kill-Switches and System Logs – Firms are required to maintain the ability to disable malfunctioning algorithms and preserve audit logs for regulatory inspection.

While this circular represented a substantial regulatory advancement at the time, it has not evolved in tandem with market dynamics. It also lacks the legal certainty of a statutory mandate, exposing enforcement actions to challenges on jurisdictional and procedural grounds.

The NSE Co-location Controversy: A Regulatory Watershed

The NSE co-location case, investigated extensively by SEBI and the Central Bureau of Investigation (CBI), stands as the most high-profile example of the surveillance limitations in India[10] . Between 2010 and 2014, certain brokers allegedly exploited NSE’s co-location servers to gain faster access to market data, thereby obtaining unfair latency advantages over other participants. An algorithm developed by OPG Securities, a brokerage firm, was able to connect preferentially to the exchange’s tick-by-tick data feed, allowing them to front-run trades and engage in market manipulation.

Despite strong circumstantial evidence, SEBI’s enforcement action was constrained by the lack of clear legal definitions, technological inability to reconstruct trades, and absence of codified liability standards for algorithmic misconduct.[11]While SEBI ultimately barred key individuals from the securities market and levied financial penalties, the case exposed deep fissures in India’s surveillance apparatus

Regulatory Inertia and Expertise Deficit

A core challenge faced by SEBI and Indian exchanges is the lack of in-house expertise in data science, AI/ML techniques, and algorithmic forensics. Regulatory audits and investigations are often conducted post facto and rely heavily on voluntary disclosure or whistleblowers, rather than predictive analytics or automated detection systems.⁷ Furthermore, SEBI has not yet established a dedicated algorithmic trading supervisory unit, nor has it developed a regulatory sandbox for testing surveillance tools in real-market conditions—a mechanism adopted by several progressive jurisdictions like Singapore and the UK.

RECOMMENDATIONS AND THE WAY FORWARD

To bridge the growing divide between surveillance capabilities and the rapidly evolving landscape of algorithmic trading, Indian regulators must undertake transformative reforms. These reforms must encompass legislative innovation, technological modernisation, institutional restructuring, and international collaboration. Drawing upon lessons from comparative jurisdictions and domestic case studies, this section proposes a comprehensive strategy aimed at equipping market regulators for the algorithmic age.

Enactment of a Dedicated Statutory Framework on Algorithmic Trading

India’s current reliance on circulars and informal guidelines to regulate algorithmic trading is insufficient for the complexity of modern financial markets. There is a pressing need to enact a comprehensive legislative framework, either through amendments to the Securities Contracts (Regulation) Act, 1956 or the passage of a dedicated Algorithmic Trading Regulation Act. Such a framework should:

• Define key terms such as “algorithmic trading,” “high-frequency trading,” “latency arbitrage,” and “automated market manipulation.”
• Impose mandatory registration and disclosure obligations for all trading algorithms deployed by market participants.
• Mandate pre-deployment testing, audit trails, and “kill switch” capabilities.
• Establish clear liability provisions for developers, brokers, and exchanges in cases of algorithmic misconduct.

Modernisation of Surveillance Infrastructure and Real-Time Analytics

SEBI and other market infrastructure institutions must invest in real-time surveillance systems capable of detecting sub-second market anomalies and behavioural patterns. This includes:

• Complex Event Processing (CEP) engines to track large volumes of event streams and identify suspicious activities like spoofing, layering, and quote stuffing in milliseconds.
• Deployment of Artificial Intelligence (AI) and Machine Learning (ML) tools for pattern recognition, anomaly detection, and prediction of market abuse.
• Integration of Natural Language Processing (NLP) to analyse unstructured data from social media, messaging apps, or encrypted trading platforms that could indicate coordinated market manipulation.

Development of a Centralised Audit Trail and Unified Market Data Platform

To eliminate surveillance blind spots caused by data silos, India must build a centralised, consolidated audit trail (CAT)analogous to the U.S. model. This would require:

• Real-time integration of order, execution, and routing data from all exchanges, clearing corporations, and brokers.
• Uniform time-stamping protocols using synchronised clocks to trace order life cycles.
• Regulatory APIs for secure and tamper-proof data submission.

CONCLUSION:

The rise of algorithmic trading, artificial intelligence, and surveillance technologies has fundamentally altered the landscape of financial markets worldwide. In this new environment, regulators must evolve not only in terms of tools and technologies but also in legal thought, institutional design, and normative frameworks. This paper has examined how the growing sophistication of algorithmic systems has outpaced the ability of India’s current market regulation and surveillance frameworks to govern them effectively.

At present, the Indian legal system offers only fragmented responses to the structural and technical challenges posed by the algorithmic age. While SEBI has issued guidelines on algorithmic trading, the absence of statutory definitions, real-time data monitoring capabilities, and AI-aided surveillance tools creates a compliance vacuum. The comparative insights from jurisdictions like the EU, Singapore, and the United States demonstrate that a proactive, tech-integrated, and law-backed regulatory response is both possible and necessary.

There are five core takeaways from this research:

1. Algorithmic trading has outgrown the capacity of static regulatory frameworks: India’s laws must move beyond principles of human-centric intent and culpability to account for autonomous and emergent behaviour by AI-driven systems.
2. Surveillance technology is necessary but insufficient: Real-time detection of manipulative patterns is crucial, but so is the legal infrastructure to respond meaningfully. Without judicial preparedness, institutional competence, and clear accountability mechanisms, surveillance is toothless.
3. Cross-jurisdictional risks demand cross-border cooperation: In an era of global capital flows and decentralised platforms, India must participate in multilateral frameworks for algorithmic trading governance.
4. SupTech and RegTech adoption is non-negotiable: Regulators must undergo digital transformation—not just in hardware and software, but in mindset. This includes hiring data scientists, collaborating with ethical technologists, and creating real-time supervision units.
5. Law must become anticipatory: Reactive regulation is no longer viable. India must invest in scenario planning, sandbox experimentation, and legal forecasting to stay ahead of technological disruptions.

Reference(S):

[1] SEC Rule 613, 17 C.F.R. § 242.613

[2] SEC Offices of Analytics and Research, “CAT Implementation Timeline,” 2022.

[3] Benjamin Bain, “U.S. Consolidated Audit Trail Faces Cybersecurity Scrutiny,” Bloomberg News (2021).

[4] Directive 2014/65/EU (MiFID II).
[5]Market Abuse Regulation (EU) No. 596/2014.

[6] ESMA Annual Report 2022.
[7]FCA Policy Statement PS21/20, 2021.

[8] SEBI Act, No. 15 of 1992, § 11.

[9] SEBI Circular No. CIR/MRD/DP/16/2012, May 30, 2012.

[10] SEBI Final Order, In the Matter of NSE Co-location Case, Apr. 30, 2019.

[11] Ibdi