Key findings
- SEBI has required market intermediaries, exchanges and mutual funds to report the AI and machine learning systems they use since 2019.
- In June 2025 SEBI proposed guidelines for responsible use of AI and ML. They cover senior management accountability, testing and monitoring, responsibility for third-party providers, transparency with clients and good-quality, unbiased data, with a lighter regime for internal uses.
- In August 2026 SEBI's chairman said the final guidelines would require human oversight, data controls and kill switches, and on 10 October 2026 he said they would be issued shortly.
- For retail algo trading, an AI-driven strategy whose logic is not disclosed falls in the black-box category of SEBI's 2025 framework, and its provider must be registered with SEBI as a research analyst.
- AI-generated deepfakes of market officials have been used in investment scams, and both NSE and BSE have warned investors about them.
Where AI is used in Indian markets
| Area | Typical uses | Who uses it |
|---|---|---|
| Surveillance | Spotting unusual trading patterns, front-running, pump-and-dump schemes and fake social media tips | Exchanges, SEBI |
| Customer service | Chatbots, ticket sorting, call summaries | Brokers, mutual funds |
| Onboarding and compliance | Document checks, fraud detection, monitoring client communication | Brokers, banks |
| Research | Summarising company filings, earnings calls and news | Brokers, analysts, funds |
| Trading | Signals from prices, order flow and text; execution timing | Institutions, prop desks, some retail algos |
| Advice and distribution | Robo-advice, portfolio suggestions | Registered advisers, fund platforms |
The rules that apply
SEBI's proposed AI and ML guidelines
SEBI's consultation paper of 20 June 2025 proposed guidelines for the responsible use of AI and ML in securities markets. Its main points:
| Principle | What it asks of market participants |
|---|---|
| Governance | Senior management with technical knowledge is responsible for AI models through their life cycle |
| Testing and monitoring | Models are tested before use and monitored for drift and errors, with fallback plans |
| Third parties | The regulated firm stays accountable when it uses an outside AI provider |
| Disclosure | Clients are told when AI is used in services that affect them, with its risks and limits |
| Data | Data is good quality, free of bias and handled under data protection law |
| Proportion | A lighter regime for internal uses that do not directly affect clients |
Comments closed on 11 July 2025. Speaking at FICCI's Capital Markets Conference on 19 August 2026, SEBI chairman Tuhin Kanta Pandey said the final guidelines would follow a tiered approach and require human oversight, data controls and kill switches. On 10 October 2026 he said they would be issued shortly.
The retail algo framework
SEBI's framework for retail algorithmic trading, in force for all brokers since 1 April 2026, applies whether or not a strategy uses AI:
- every algo order is tagged with an identifier from the exchange, the Algo ID,
- algo providers are empanelled with exchanges and act as agents of brokers, and
- black-box strategies, whose logic the user cannot see or replicate, need a provider registered with SEBI as a research analyst, and must be registered again with the exchange whenever their logic changes.
Most AI trading models are black-box by nature, because their decisions come from learned weights rather than rules a user can read. Our guide to SEBI's algo trading rules explains the framework.
Advice and data protection
An AI tool that recommends specific trades to clients is giving investment advice or research, which needs SEBI registration as an investment adviser or research analyst. Personal data used to train or run AI systems falls under the Digital Personal Data Protection Act, 2023, whose main duties apply from May 2027.
What language models can do for traders
Large language models read and write text. For traders, that makes them useful for some tasks and unreliable for others.
| Task | How well it works | Watch for |
|---|---|---|
| Summarising filings, results and circulars | Well | Check numbers against the source document |
| Explaining concepts and rules | Well | Rules change; check the date of the source |
| Writing and checking strategy code | Useful as a draft | Test every line; models invent functions that do not exist |
| Classifying news as positive or negative | Fair | Results depend on the prompt, and on English versus Hindi or regional news |
| Predicting prices | Poorly | No reliable evidence of an edge after costs |
| Giving live prices | Not suitable | Models do not see live data unless connected to it |
Sentiment from news and filings
Sentiment models turn text, such as news, company announcements and social media posts, into a score. A trader might test whether stocks with very positive news outperform the next day. Building a fair test is harder than it looks:
- Timestamps. The score must use only text published before the trade. A story time-stamped at 9:05 but updated at 11:00 creates look-ahead bias.
- Language. Much Indian market news is in Hindi and regional languages. A model trained on English text may misread it.
- Already priced in. Big news moves prices within seconds, before most retail systems can act.
- Costs. Short-term signals trade often, and costs can exceed the edge. Include them in every backtest.
- Overfitting. Many prompts and thresholds can be tried. Keep data aside for a final test, as our guide to avoiding overfitting explains.
Risks for retail traders
- Deepfake tips. Videos and voice clips that appear to show exchange officials, fund managers or well-known investors recommending stocks have been used in scams. NSE and BSE have both warned investors that their officials do not give stock tips.
- "AI-powered" return claims. Promises of guaranteed or very high returns from an AI bot are a warning sign, whatever the technology.
- Black-box bots from unregistered providers. Under SEBI's framework, these should not be offered to retail traders through brokers' APIs.
- Confident wrong answers. Language models can state wrong figures fluently. Check anything that affects a trade.
- Herding. If many traders use similar models on similar data, their trades can crowd into the same positions.
What this means for traders
- Use AI to read, summarise and draft, and check what it produces against the source.
- Treat any AI trading signal as a strategy to test, with the same backtest, walk-forward testing and paper trading checks as any other rule.
- Prefer strategies whose rules you can see and understand. Rules that are disclosed and can be replicated also fall in SEBI's white-box category.
- Verify who is behind any AI product: SEBI registration, broker partnership and exchange empanelment.
- Ignore tips from videos or messages, however real they look.
Our guide to machine learning in finance covers how models are trained and tested on market data.
Related guides
- Machine learning in finance: predicting price moves with supervised learning, and its limits
- Algo trading growth in India: how the algo share of turnover has changed
- SEBI rules for algo trading: the retail framework, lot sizes and expiries
- What is algo trading?: how automated trading works in India
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Frequently asked questions
Is AI trading legal in India?
Yes, within SEBI's rules. Automated orders through a broker must follow the retail algo framework, and a strategy whose logic is hidden needs a provider registered as a research analyst.
Can ChatGPT or another chatbot tell me what to buy?
General chatbots are not registered advisers and do not see live market data unless connected to it. Use them to understand concepts, not for trade decisions.
Has SEBI issued final rules on AI?
Not as of 20 October 2026. SEBI published proposals in June 2025, and on 10 October 2026 its chairman said final guidelines would be issued shortly. Check SEBI's website for the current position.
Does QuantPower use AI to trade for me?
No. QuantPower is trading software that runs rule-based strategies whose rules stay fully visible to you, such as those you build yourself or adapt from its templates. Orders go from your own broker account through the broker's official API, and QuantPower gives no tips or buy or sell advice.
Sources
- SEBI, consultation paper on guidelines for responsible usage of AI/ML in Indian securities markets, 20 June 2025 (sebi.gov.in)
- SEBI, circulars on reporting of AI and ML applications by intermediaries, exchanges and mutual funds, 2019
- SEBI, circular on safer participation of retail investors in algorithmic trading, 4 February 2025, and glide-path circular, 30 September 2025
- Speech by SEBI chairman Tuhin Kanta Pandey at FICCI's 23rd Annual Capital Markets Conference, 19 August 2026, as reported by Outlook Money, and his remarks at the BSE Brokers' Forum Capital Market Confluence, 10 October 2026, as reported by ANI
- NSE and BSE investor cautions on deepfake videos of their officials, 2024
- Digital Personal Data Protection Act, 2023, and Digital Personal Data Protection Rules, 2025