Indian Brokerages Embrace AI for Smarter Analysis, But Draw the Line at Stock Picking
Artificial intelligence is steadily reshaping India’s broking landscape—but not in the way many retail investors might expect. Instead of flashy AI-driven stock tips or automated buy-sell recommendations, brokerages are deploying AI in quieter, more deliberate ways: analysing portfolios, summarising data, reducing friction and making investing more conversational.
At the same time, firms are consciously avoiding the final step—letting AI pick stocks or make investment decisions. The reason lies at the intersection of regulation, accountability and behavioural risk, where mistakes carry real financial consequences.
Interviews with senior leaders at Angel One, Dhan, Groww, Fyers and Zerodha reveal a sector that is experimenting actively, but cautiously, as AI moves closer to the investor’s decision loop.
From Back-End Automation to an Intelligence Layer
The first wave of AI adoption in broking has little to do with automated trading. Instead, it is focused on scaling intelligence across operations—processing information faster, reducing costs and improving user experience.
At Angel One, Group CEO Ambarish Kenghe describes AI not as a feature, but as an organisational capability.
“AI democratises intelligence,” Kenghe said, comparing its impact to calculators democratising math and Google democratising information.
Angel One has moved its customer support chatbot in-house, uses AI to draft email responses and delivers AI-generated multilingual podcasts to partners. Internally, multiple teams are experimenting with AI tools to improve speed and scale.
The guiding principle, Kenghe noted, is to deploy AI only where it clearly adds value—not for novelty.
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AI and Humans as Complements, Not Competitors
Even smaller wealth platforms see AI as an assistive layer rather than a replacement for human judgement.
“At FinEdge, we see AI and human expertise as complementary, not competing,” said Harsh Gahlaut, Co-founder and CEO of FinEdge.
“We use AI to analyse data, personalise investment plans and track client behaviour in real time, but wealth creation is deeply emotional and cannot be achieved by algorithms alone.”
This hybrid approach reflects a broader industry view: AI can enhance analysis, but decision-making still requires empathy, context and accountability.
Vertical AI Models Gain Preference Over General Chatbots
As retail investors increasingly upload portfolio screenshots to generic AI tools like ChatGPT or Gemini, brokerages are growing wary. Executives argue that horizontal AI models lack deep market context and often operate with incomplete or outdated data.
This has pushed firms toward vertical AI models trained specifically on Indian financial markets.
Dhan, operated by Raise Financial Services, is betting on this approach with ‘Fuzz’, an agentic AI model built for finance. Trained on India-specific datasets, regulatory filings and real-time market data, Fuzz is designed to deliver source-backed outputs.
“Just like Dhan, this is meant for serious investors and analysts,” founder Pravin Jadhav said earlier, adding that user data “never leaves the country”.
Beyond Fuzz, Raise already uses AI to generate and analyse over 1,000 news items daily and has handled more than 32.5 lakh customer support interactions through AI-driven systems.
AI as an Interface, Not a Dashboard
Another shift underway is architectural rather than visual. Model Context Protocol (MCP) is emerging as a way for AI agents to securely interact with brokerage systems.
Zerodha has leaned into this by opening its systems through MCP, allowing users to connect portfolios to AI assistants like Claude and Cursor. Instead of building a proprietary AI interface, Zerodha lets users bring their own agents.
“AI tools have become so good that you don’t need a UI anymore,” Zerodha CEO Nithin Kamath wrote in a LinkedIn post.
Users echoed the sentiment, with one describing it as “one of the most quietly powerful things released by a brokerage in India”.
Investing Turns Conversational, Not Automated
For consumer platforms, AI is also changing how investors interact with apps. Traditional dashboards are giving way to natural language commands.
At Groww, investors testing the upcoming AI assistant say they can research, analyse and review portfolios using conversational prompts. The system factors in historical behaviour and risk profiles and can even execute trades under a mandate.
Still, not all brokers are comfortable crossing that line.
At Fyers, the FIA GPT assistant focuses strictly on analysis—portfolio performance, chart patterns and market data—without executing trades.
“We are not replacing human decisions,” said co-founder and CTO Yashas Khoday. “The aim is to reduce friction.”
SEBI Signals Guardrails for Responsible AI Use
Regulatory caution is a major reason AI stock-picking remains off-limits. In a July 2025 consultation paper, SEBI laid out guiding principles for responsible AI use in securities markets.
While acknowledging productivity gains, SEBI flagged risks around:
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Bias and fairness
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Transparency and explainability
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Accountability for errors
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Data security and third-party dependencies
The regulator made it clear that as AI adoption deepens, expectations around governance and supervision will rise.
The Line Brokerages Are Not Crossing—Yet
Under current rules, AI systems cannot legally provide direct investment advice without proper licensing and safeguards. Explainability remains a core challenge, as large models cannot always justify why a decision was made.
Zerodha’s CTO Kailash Nadh has warned against over-reliance on opaque AI systems, questioning accountability when things go wrong.
“What about errors and failures with real-world implications?” he asked, pointing to risks around traceability and governance.
For now, brokerages are clear: AI can analyse, summarise and personalise—but not decide.
As Angel One’s Kenghe put it,
“We experiment internally, but public deployment is a different threshold altogether.”
How long this boundary holds will shape the next phase of India’s investing ecosystem.
