From Analysts to Agents; how AI is aiding in decoding wealth management
AI is becoming a more efficient tool through its rapid processing of data using various algorithms, aiding fast decision-making in financial markets.
Artificial Intelligence (AI) has surfaced as one of the most widely discussed subjects across the global financial corridors. AI has shown impact on diverse sectors including banking, financial services, defense manufacturing, data processing, data mining, and agricultural production. According to market expert Manoj Kumar Jain, AI integration within financial markets is now extensively recognized and embraced owing to its specific strengths in rapid search functionalities, spam filtering, trend identification, sophisticated planning, and its adaptive capacity to adhere to complex instructions.
Earlier, Excel transformed the financial world, especially financial markets, by enabling the processing of large datasets in a single spreadsheet and deriving required results in a fraction of a second using predefined formulas. However, with technological advancements, markets needed faster and more efficient tools to process larger amounts of data and deliver results in shorter timeframes. AI is becoming a more efficient tool through its rapid processing of data using various algorithms, aiding fast decision-making in financial markets.
I personally view the evolution of AI in financial markets in three phases:
Phase 1: The Analyst Era (2020-2024) - What Has Changed?
AI started by reading reports, calculating various ratios, summarising earnings calls, and detecting fraud. It was used as an assistant to make the right financial decisions.
By 2024, only 8 per cent of banking institutions were using Generative AI tactically. By 2026, that number jumped to 78 per cent, according to IBM's Global Banking Outlook.
AI moved into:
Risk & Compliance: Drafting regulatory reports, cross-referencing risk databases, and flagging compliance issues autonomously. Market compliance and regulatory requirements rapidly increased post-COVID, and AI greatly helped the entire financial ecosystem process these tasks proactively in a very short span of time.
Personalisation: Hyper-personalised budgeting, micro-investing, and credit-building for retail investors. AI also helps the market ecosystem create personalised budgets and provide effective solutions for investing at micro levels.
Fraud & Security: Adaptive systems that learn from global threat data on a real-time basis, send regular fraud alerts, and encourage the industry to adopt advanced security systems.
Phase 2: The Agentic Era (2026) - Where We Are 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 question is no longer whether to adopt AI, but how to govern it. Success in this phase will be defined by how effectively markets govern AI and Superintelligence to make appropriate decisions in a rapidly changing world amidst global economic uncertainty and hyper-geopolitics.
Composable Finance: The future stack is AI + APIs + Instant Settlement. AI-driven decision-making for credit, fraud, and personalisation will be embedded directly into the banking and financial services sector for quick settlement processes, minimising risk and preventing fraud.
Democratised Intelligence: Quantum-inspired algorithms will simulate millions of portfolio scenarios. Many platforms are already delivering over 2 per cent alpha returns over benchmarks. This level of sophistication will become available to retail investors in India through financial service providers, helping mass investors secure better returns on their portfolios.
Human + AI Hybrid: In this era, finance professionals won't be replaced; rather, they will be freed from repetitive tasks to focus on strategic decision-making. The winning model is hybrid advisory. With the help of AI, financial service providers can make effective and efficient use of their manpower to serve mass clients in less time.
New Risks – New Governance: The "black box" nature of models makes transparency and auditability critical. Regulators are intensifying focus on market stability as AI gains trading authority. While AI brings numerous challenges, governments are making concerted efforts to govern these systems effectively in a complex environment for the benefit of society while minimising risk.
My belief is simple: The most successful investors will view AI not merely as a technological tool, but as a strategic partner in financial markets. The future of financial markets is not man vs. machine. It is man with machine—faster, more inclusive, and more intelligent.
Platforms such as Kwala Intel are already attempting to bring conversational, multilingual market research closer to retail investors.