Will leverage tech to crack down on market manipulators, says SEBI chief

SEBI Signals Technology Push: ₹500 crore Budget Allocation to Curb Market Manipulation

On 2 March 2026, Securities and Exchange Board of India (SEBI) Chairperson Madhabi Puri Buch announced an aggressive tech-enabled drive to counter market manipulators. From AI-based surveillance mechanisms to data analytics tools capable of detecting trading anomalies in real-time, SEBI is positioning itself as a watchdog equipped for the complexities of modern markets. Notably, the regulator dedicated ₹500 crore from its annual budget solely for technology upgrades and operational uniformity across regions. The move comes amidst a surge in controversial price manipulations that destabilized mid-cap stocks earlier this year.

Why This Crackdown Signals a Departure

The ₹500 crore technology allocation breaks away from SEBI's historical reliance on manual auditing and retrospective regulation. In many infamous cases of insider trading, such as the 2023 Adani-Hindenburg debacle, surveillance mechanisms lagged behind perpetrators who exploited regulatory loopholes faster than audits could uncover them. Until recently, SEBI’s market surveillance systems lacked scalability; they struggled to monitor rising volumes in algorithmic trades that account for over 50% of daily transactions now.

Moreover, this signals an important budgetary precedent. In previous years, SEBI rarely earmarked significant funding—that too at a granular level—for surveillance upgrades. For instance, technology-related spending accounted for just ₹100 crore in SEBI’s 2022-23 expenditures, spread thin across compliance and analytics functions. A five-fold increase specifically targeting manipulative activity is not merely corrective but potentially transformative.

How SEBI’s Institutional Machinery is Mobilizing

This push derives from SEBI’s broader mandate enshrined in Sections 11 and 11B of the SEBI Act, 1992, which empower the regulator to prevent securities fraud. In addition, the Prohibition of Insider Trading Regulations, 2015, remains the primary legal framework for identifying insider activity. Yet, the critical machinery here will rely on provisions under the Securities Transactions Tax (STT) regime, collecting transaction-level data that powers full-spectrum AI monitoring models.

The groundwork for this effort is already underway. SEBI’s Technology and Data Analytics Department has partnered with institutes like IIT Bombay to design algorithms capable of flagging abnormal trading patterns compared to historical averages. Further collaboration is ongoing with the National Stock Exchange (NSE) and Bombay Stock Exchange (BSE) for seamless integration of their surveillance databases into SEBI’s new systems. Additionally, all brokers and fund houses will soon be required to report not only trading volumes but metadata tagging the source of transactions.

Where Official Claims and Ground Realities Diverge

The SEBI chief has framed this initiative as a silver bullet against systemic manipulation, but available data suggests tempered optimism is warranted. While SEBI maintains that new AI models have already reduced detection times for irregularities by 30%, prosecution rates remain abysmally low. Between 2020 and 2025, SEBI successfully prosecuted only 18% of the insider cases flagged, primarily due to evidentiary challenges in proving intent—a gap technology alone cannot bridge.

Additionally, SEBI’s claims of robust statewide uniformity in enforcement are dubious. States like Maharashtra, holding 36.5% of India’s trading volume, dominate regulatory priorities, leaving smaller states with insufficient oversight. Interviews with brokers in tier-2 markets like Gujarat suggest most cases flagged by SEBI do not translate into penalties. Local enforcement capacity is yet another weak link that technology upgrades alone won't address.

Uncomfortable Questions Linger

First, is this ₹500 crore earmarked for technology justified when SEBI’s overall capacity-building remains uneven? The regulator critically underfunds training for human resource expertise in securities fraud—only ₹20 crore was spent last year on capacity-building programs targeted at enforcement professionals.

Second, could this focus on technology distract attention from SEBI’s cumbersome legal process? Insider trading cases often stagnate in courts for years, with firms exploiting procedural loopholes like appeals and adjournments to delay consequences. Streamlining institutional accountability—tightening enforcement timelines under Section 12B of the SEBI Act—may yield similar benefits without a lavish technological overhaul.

Finally, one cannot ignore the role of political timing here. The announcement comes months before national elections, when alleged market manipulation becomes a public relations flashpoint. Is SEBI playing electoral optics in prioritizing high-tech surveillance now instead of administrative capacity overhaul?

Comparing South Korea’s Precedent in 2018

South Korea tackled its insider trading crisis with a hybrid approach in 2018. The Financial Supervisory Service (FSS) implemented AI tools specifically to monitor trades in blue-chip stock derivatives while simultaneously revising prosecution frameworks to allow lawsuits filed without exhaustive investigation dossiers. This dual strategy increased enforcement rates by 48% within three years, mitigating regulatory bottlenecks linked to judicial delays.

In contrast, SEBI's approach veers toward technological centralization to solve a fundamentally legal and procedural issue. Without prosecutorial reform akin to South Korea's precedents, the effectiveness of AI surveillance may remain partial—a tool that flags issues but struggles to resolve them meaningfully.

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