From Analyst Era to Agentic Era: How evolving role of AI is rewriting financial markets

Artificial Intelligence (AI) has emerged as one of the trending topics to talk about globally in recent developments. Market expert Manoj Kumar Jain explained that AI is influencing everyone across the world, including banking, financial services, defence manufacturing, data processing, data mining, and agricultural production. The role of AI in financial markets has also been widely considered due to its fast search capabilities, spam filters, trend recognition, advanced planning, and flexibility in following instructions.

Earlier, Excel revolutionised the financial world, particularly financial markets, by making it possible to process large datasets within a single spreadsheet and derive required results in a fraction of a second using predefined formulas. However, as technology advances, markets demand faster and more efficient tools capable of handling larger amounts of data and delivering results in even shorter timeframes. AI is also becoming a more efficient tool by using various algorithms to process data faster and enable quick decision-making in financial markets.

The expert viewed the evolution of AI in financial markets in three phases:

Phase 1: The Analyst Era (2020–2024) - What Has Changed?

AI's journey in finance began with reading reports, calculating multiple ratios, summarising earnings calls, and finding fraud, acting largely as an assistant to support better financial decision-making. IBM's Global Banking Outlook data suggest that only 8 per cent of banking institutions were using Generative AI tactically by 2024, but that figure has surged to 78 per cent by2026.

AI moved into:

Risk & Compliance: It is now autonomously drafting regulatory reports, cross-referencing risk databases, and flagging compliance issues. Market compliance and regulatory requirements have risen sharply post-COVID, and AI has greatly assisted the entire financial ecosystem in processing these tasks proactively in a very short span of time.

Personalisation: AI powers hyper-personalised budgeting, micro-investing, and credit-building tools for retail investors, helping the market ecosystem deliver tailored financial solutions even at micro levels.

Fraud & Security: Adaptive AI systems learn from real-time global threat data, issue fraud alerts, and push the industry toward adopting more advanced security measures.

Phase 2: The Agentic Era (2026) - Where are we now?

This is the biggest shift of 2026. AI is moving from ‘AI tools’ to ‘AI systems’. Banks and financial services institutions are no longer just using chatbots. They are deploying Agentic AI—digital co-workers with transactional authority.

• Goldman Sachs is building autonomous agents with Anthropic’s Claude to handle trade accounting and client onboarding.
• Lloyds Banking Group is deploying agentic AI enterprise-wide, targeting £100 million in value in 2026 by automating fraud investigations.
• HSBC's investment bank CEO stated this week that AI agents could replace humans trading in financial markets within the next 2–4 years, powered by quantum computing and distributed ledger technology.
• Deloitte's 2026 FSI Predictions report calls this ‘AI as a profit engine’, estimating AI-native banking products could add $75 billion in incremental revenue for large banks by 2030.
• For financial markets and wealth management experts, AI means armies of analysts reading thousands of reports, analysing sentiment, and generating actionable insights alongside human advisors to make appropriate investment decisions at the right time.

Phase 3: The Autonomous Future (2027–2030) - What Comes Next?

The key question has shifted from whether to adopt AI to how it should be governed. Success in this next phase will hinge on how effectively markets can govern AI and Superintelligence to drive sound decisions amid global economic uncertainty and intensifying geopolitics.

Composable Finance: The financial stack of the future rests on AI, APIs, and instant settlement. AI-powered decision-making for credit assessment, fraud detection, and personalisation will become embedded directly within banking and financial services, enabling faster settlements while reducing risk and fraud.

Democratised Intelligence: Quantum-inspired algorithms are set to simulate millions of portfolio scenarios, with several platforms already generating alpha returns exceeding 2 per cent over benchmarks. This level of sophistication will increasingly reach retail investors in India through financial service providers, helping everyday investors achieve stronger portfolio returns.

Human + AI Hybrid: Rather than replacing finance professionals, this era will free them from repetitive tasks to focus on strategic decision-making. Hybrid advisory models will prevail, with AI enabling financial service providers to deploy their workforce more efficiently and serve larger client bases in less time.

New Risks, New Governance: The opacity of “black box” AI models makes transparency and auditability essential. As AI systems gain greater authority over trading decisions, regulators are sharpening their focus on market stability. While AI introduces significant challenges, governments are working concertedly to govern these systems effectively within a complex landscape, aiming to benefit society while containing risk.