Artificial intelligence is rapidly altering how capital moves across global markets, and the stability of the financial system over the next decade may hinge on regulators’ ability to keep pace with these changes, according to Chief Economic Adviser Dr. V. Anantha Nageswaran. Speaking at the Global Securities Markets Conclave 2.0 organised by GIFT-IFSCA, he outlined how AI-enabled finance is simultaneously improving market efficiency and creating new systemic vulnerabilities.

The core shift, he noted, lies in AI’s ability to dramatically reduce the cost of prediction while accelerating information processing. In traditional markets, decision-making relied on human analysis constrained by time, cognitive limits, and access to data. Today, algorithmic systems can process vast datasets in real time, continuously refine models, and execute trades at scale. Portfolio allocation, risk management, and liquidity strategies are increasingly shaped by machine learning systems rather than human judgement.

This transformation is changing capital allocation globally. Investors can react to new information almost instantly, and digital platforms distribute signals across markets within seconds. While this enhances efficiency and price discovery, it also reduces the friction that historically dampened market swings. Nageswaran cautioned that the same tools that improve responsiveness can amplify volatility when large numbers of investors rely on similar models.

Citing the International Monetary Fund’s October 2025 Global Financial Stability Report, he highlighted a growing tendency toward herd behaviour driven by AI. When institutional investors deploy comparable algorithms trained on similar datasets, their responses to market signals can become synchronized. Micro-information circulating through digital and social channels may trigger simultaneous buying or selling across funds, intensifying price movements beyond what fundamentals would justify.

Such dynamics could lengthen financial cycles while making them more extreme. Instead of gradual booms and corrections, markets may experience prolonged build-ups followed by sharper adjustments. The speed of AI-driven trading also increases the likelihood that shocks in one region or asset class propagate rapidly across borders, reducing the effectiveness of traditional circuit breakers or policy responses.

Nageswaran specifically pointed to algorithmic trading strategies as potential channels for systemic transmission. Automated systems can execute large volumes of transactions within milliseconds, meaning disturbances whether geopolitical, macroeconomic, or technical can cascade across jurisdictions before regulators or market participants fully understand the trigger. This creates feedback loops in which volatility feeds on itself.

Supervisory frameworks, he argued, must therefore evolve beyond conventional prudential monitoring. Regulators need capabilities to detect model-driven herd behaviour, operational weaknesses in digital infrastructure, and concentration risks in the supply chains that power AI systems, including cloud providers and data vendors. A failure at a critical node could disrupt multiple financial institutions simultaneously.

At the same time, declining informational barriers are not purely negative. Economies that demonstrate transparency, credible institutions, and technological readiness could attract more capital in this new environment. Faster information flows reward markets where governance standards are trusted and data quality is high. However, this also increases policy responsibilities, as any perceived weakness may be punished quickly by global investors.

For India, the remarks carry particular relevance as the country positions the GIFT International Financial Services Centre as a global hub for capital markets. The adoption of advanced analytics and algorithmic systems could enhance competitiveness, deepen liquidity, and broaden participation. Yet it also exposes the market to global volatility patterns shaped by AI-driven funds operating across jurisdictions.

From a sectoral perspective, financial services firms, exchanges, brokerages, and asset managers will likely face rising compliance costs as regulators demand greater transparency in algorithmic strategies and model governance. Technology providers supplying data infrastructure and cloud services may become systemically important, attracting closer oversight. Meanwhile, retail participation through digital platforms could increase vulnerability to rapid sentiment shifts driven by automated signals.