AI and Algorithmic Portfolio Management in Indian Mutual Funds

Executive Summary

Artificial intelligence has moved from a back-office research tool to an active input in portfolio construction, trade execution, and investor servicing across India’s ₹82-lakh-crore mutual fund industry. SEBI has responded not with a single sweeping rule but with a layered regulatory architecture: a February 2025 amendment introducing Regulation 16C for intermediaries, a June 2025 consultation paper on a Responsible AI/ML framework covering mutual funds, AIFs, and portfolio managers, and a parallel, already-live algorithmic trading framework that reached full mandatory rollout on April 1, 2026. For AMC leadership, the strategic task is no longer whether to adopt AI in investment processes, most large houses already have, but how to build governance, disclosure, and audit trails robust enough to survive scrutiny under a regulatory regime still being finalised.

Introduction

AI-driven portfolio management in India covers a wide spectrum: natural-language processing tools that scan corporate filings and news for sentiment signals, machine-learning models that flag portfolio concentration risk, and, at the more advanced end, algorithms that generate or influence buy-sell decisions directly. SEBI Chairman Tuhin Kanta Pandey has publicly framed the opportunity in terms of automation and efficiency gains, while explicitly flagging cyber risk as the corresponding downside the regulator is moving to address through advisories aimed at SEBI-regulated entities.

Industry Background

India’s regulatory engagement with algorithmic decision-making did not begin with AI specifically; it began with algorithmic trading more broadly. SEBI’s 2019 circulars first required intermediaries to report on algorithmic tools, and this baseline has been progressively layered with more specific AI/ML obligations as adoption has deepened across trading desks, portfolio construction, and client-facing advisory tools.

Current Market Landscape

By 2026, algorithmic participation in Indian equity markets has become mainstream rather than niche: algo participation in stock futures rose from roughly 39% in FY15 to about 73% in the current fiscal year, according to NSE’s Market Pulse data. Retail traders now access algorithmic strategies through empanelled vendors and broker APIs under a fully live SEBI framework, while institutional participants, mutual funds, hedge funds, banks, and proprietary trading firms, continue to use more advanced automated systems for execution. Portfolio management specifically has lagged execution in regulatory specificity, which is precisely the gap SEBI’s Responsible AI/ML consultation aims to close.

[Insert Diagram: Layered SEBI regulatory architecture, 2019 reporting circulars, Regulation 16C (2025), Responsible AI/ML framework (2025–26), Algo-ID mandate (April 2026)]

Latest Industry Statistics

SEBI’s algorithmic trading framework, notified via circular in February 2025, moved through phased broker readiness and testing across 2025 before reaching full mandatory status on April 1, 2026, when all algorithmic orders were required to carry an exchange-issued Algo-ID for origin tracking. Separately, SEBI’s June 2025 consultation paper explicitly scoped its Responsible AI/ML framework to cover mutual funds, NBFCs using AI for SEBI-regulated activities, stockbrokers, depositories, AIFs, portfolio managers, RTAs, and investment advisers, effectively every category of regulated entity that might deploy AI anywhere in the investment value chain.

Regulatory & Policy Updates

Three regulatory instruments now define the compliance perimeter. First, Regulation 16C, introduced via the SEBI (Intermediaries) (Amendment) Regulations, 2025, dated February 10, 2025, establishes a baseline obligation for AI use disclosure among regulated intermediaries. Second, the June 20, 2025 consultation paper on Responsible AI/ML usage sets out the substantive framework, expected to translate into binding rules through FY 2026-27, requiring entities to document which AI systems are in scope, map them to specific business functions, and prepare for statutory auditor review of AI-driven decisions against investor disclosures. Third, the algorithmic trading framework, though originally designed for trade execution rather than portfolio construction, now intersects with AI-driven strategies wherever a mutual fund’s execution desk uses automated order generation, requiring both an Algo-ID and exchange empanelment for the underlying vendor.

Key Industry Challenges

The central compliance challenge is one of scope-mapping: many AMCs use AI across multiple, loosely connected functions, sentiment analysis for research, chatbots for investor servicing, anomaly detection for compliance monitoring, without a single inventory of which systems exist, what data they touch, and which investment decisions they influence. SEBI’s framework effectively requires that inventory to exist before a formal audit obligation lands. A second challenge is explainability: where an AI system contributes to portfolio construction, statutory auditors are expected to review AI-driven decisions against investor disclosure documents, which is difficult if the model’s decision logic cannot be reconstructed after the fact. A third, more forward-looking challenge is cyber risk, which SEBI’s chairman has specifically flagged as rising in tandem with AI adoption across the regulated ecosystem.

Strategic Analysis

AMCs are likely to fall into three governance postures over the next 18 months. Leading houses will treat the Responsible AI/ML framework as a floor rather than a ceiling, building internal AI governance committees, model inventories, and audit trails ahead of binding rules, a stance that also functions as a competitive signal to institutional allocators increasingly asking about AI governance during due diligence. A middle group will comply reactively once rules are finalised, accepting some implementation lag risk. A smaller group, particularly newer digital-first AMCs built on proprietary algorithmic infrastructure (the Jio BlackRock model, leveraging BlackRock’s Aladdin technology, is instructive here), will treat robust AI governance as a core part of their brand proposition from day one, since their entire operating model depends on demonstrating that automation is safe rather than merely efficient.

Technology Trends

The direction of travel is toward AI systems that assist rather than fully automate portfolio decisions in the near term, sentiment and anomaly-detection tools sit alongside human portfolio managers rather than replacing them, partly because SEBI’s disclosure obligations create friction for any structure where decision accountability cannot be clearly attributed to a specific person or documented process. At the execution layer, algorithmic order generation continues to scale under the now-mandatory Algo-ID framework, with brokers introducing lower-cost, simplified API access for both retail and institutional automation.

Business Implications

For CFOs and CTOs, the practical near-term implication is budget for AI governance infrastructure that was previously treated as optional: model documentation systems, decision-logging capable of surviving statutory audit, and legal review of investor disclosure language wherever AI materially influences portfolio construction. For CA firms and statutory auditors serving SEBI-regulated entities, mutual funds among them, this represents a genuinely new audit line item, requiring either in-house AI/ML audit capability or formal engagement of an auditor’s technical expert under the SA 620 framework.

Case Studies

Jio BlackRock’s Aladdin-powered launch. Jio BlackRock Mutual Fund, a 50:50 joint venture between Jio Financial Services and BlackRock, combined BlackRock’s global systematic investing infrastructure with Jio’s Indian digital distribution reach, crossing an estimated ₹50,000 crore in AUM within ten months of its May 2025 SEBI registration. The venture’s proposition rests explicitly on demonstrating that globally proven, technology-driven investment processes, the kind SEBI’s Responsible AI/ML framework is designed to scrutinise, can be deployed safely at scale for Indian retail investors, making it a live test case for how regulators and institutional allocators will judge algorithm-heavy fund management going forward.

SEBI’s phased algo-trading rollout as a governance template. Rather than mandating AI/algo compliance overnight, SEBI structured its algorithmic trading framework in stages, finalisation of implementation guidelines by April 2025, broker readiness and testing through 2025, and full mandatory Algo-ID compliance from April 1, 2026. This phased-rollout approach, developed in consultation with the Brokers’ Industry Standards Forum, is widely expected to serve as the template for how the broader Responsible AI/ML framework is eventually operationalised across mutual funds and portfolio managers.

Best Practices

AMCs should build a living inventory of every AI/ML system touching investment decisions, research, execution, or investor communication, mapped explicitly to the business function and data sources involved. Decision logs for any AI-influenced portfolio action should be retained in a form a statutory auditor can review without needing to interrogate the underlying model. Finally, disclosure language in Scheme Information Documents should be reviewed specifically for AI-related claims, ensuring marketing language about “smart” or “AI-powered” strategies does not outpace what the fund’s actual governance and audit trail can substantiate.

Executive Recommendations

Boards should require a standing AI governance report at least twice yearly, covering the current system inventory, any material changes to AI-driven investment processes, and readiness against SEBI’s Responsible AI/ML framework as it moves from consultation to binding rule through FY 2026-27. Compliance and audit committees should proactively engage statutory auditors now on scoping AI-related audit procedures, rather than waiting for the framework to formalise, given the lead time needed to build defensible documentation.

Future Outlook

Expect SEBI’s Responsible AI/ML framework to move from consultation to substantive rule during FY 2026-27, likely mirroring the phased-rollout approach used for algorithmic trading. Cyber risk oversight will likely tighten in parallel, given the regulator’s explicit public signalling on this front. Longer term, AI’s role in Indian mutual fund management is more likely to expand within a documented, audited perimeter than to proceed as an unregulated arms race, given how directly SEBI has already scoped mutual funds into its framework.

Conclusion

AI in Indian mutual fund portfolio management has outpaced formal regulation by only a modest margin, and SEBI’s layered framework, spanning intermediary disclosure, a dedicated Responsible AI/ML consultation, and an already-mandatory algo-trading regime, signals that the gap is closing quickly. AMC leadership that treats governance as a parallel build alongside AI adoption, rather than an afterthought, will be better positioned when binding rules land.

Frequently Asked Questions

1. What is SEBI’s Responsible AI/ML framework? A regulatory framework, developed from a June 2025 consultation paper, covering AI/ML usage by SEBI-regulated entities including mutual funds, AIFs, portfolio managers, stockbrokers, and investment advisers, expected to become substantively binding through FY 2026-27.

2. What is Regulation 16C? A provision introduced by the SEBI (Intermediaries) (Amendment) Regulations, 2025, effective February 10, 2025, establishing disclosure obligations for AI use among regulated intermediaries.

3. Is algorithmic trading fully regulated in India now? Yes, SEBI’s algorithmic trading framework reached full mandatory status on April 1, 2026, requiring all algorithmic orders to carry an exchange-issued Algo-ID.

4. Do mutual funds need to disclose AI use to investors? Under the emerging framework, AI systems materially influencing investment decisions are expected to require documentation reviewable by statutory auditors against investor disclosure documents, though detailed binding disclosure rules are still being finalised.

5. What cyber risks has SEBI flagged around AI adoption? SEBI’s chairman has publicly noted that cyber risk rises alongside AI adoption across regulated entities, prompting advisories on protecting the SEBI-regulated ecosystem.

6. How does Jio BlackRock use AI in portfolio management? The AMC leverages BlackRock’s global systematic investing infrastructure, including its Aladdin technology platform, combined with Jio’s digital distribution network, positioning it as a live example of technology-driven asset management at scale in India.

7. Will statutory auditors need new skills for AI-related audits? Yes, CA firms auditing SEBI-regulated entities are expected to need AI/ML-specific audit procedures, potentially engaging an auditor’s expert under SA 620 where in-house technical capability is insufficient.

8. Does SEBI’s framework apply only to large AMCs? No, the framework’s scope covers the full range of SEBI-regulated entities using AI in relevant functions, though the practical compliance burden will likely scale with the sophistication of AI deployment at each entity.

References

• CORAA. “SEBI Responsible AI / ML Framework: What Statutory Auditors of SEBI-Regulated Entities Need to Know.” 2026. https://coraa.ai/blog/sebi-responsible-ai-framework-statutory-auditor-obligations
• AxonFlow Documentation. “SEBI AI/ML Compliance.” 2026. https://docs.getaxonflow.com/docs/compliance/sebi/
• Open Magazine. “SEBI Plans AI Trading Rules to Tackle Rising Cyber Risks in Indian Markets, Says Chairman Tuhin Kanta Pandey.” 2026. https://openthemagazine.com/business/sebi-plans-ai-trading-rules-amid-rising-cyber-risks-says-chairman
• StockGro. “SEBI Regulations on Algorithmic Trading.” 2026. https://www.stockgro.club/blogs/trading/sebi-regulations-on-algorithmic-trading/
• QuantInsti. “Algorithmic Trading in India (2026): SEBI Framework and Career Guide.” 2026. https://www.quantinsti.com/articles/algorithmic-trading-india/
• Univest. “JioBlackRock Mutual Fund 2026: Schemes, NAV, AUM, Returns.” https://univest.in/blogs/jioblackrock-mutual-fund