Artificial intelligence is beginning to reshape one of finance’s oldest divides: the gap between the tools available to professional investors and those available to everyone else.

Over the past decade, fintech has broadened access to financial markets. Trading is cheaper, information is everywhere, and almost anyone can build a portfolio from their phone. But access to markets has not necessarily translated into access to the intelligence needed to navigate them. Professional investors still rely on teams of analysts, sophisticated research tools and years of experience to interpret an overwhelming amount of financial information. This has long remained out of reach for the general population. AI is changing that.

The next shift in financial technology is from information on demand to intelligence that is always on. AI systems can increasingly understand context, monitor thousands of signals continuously, connect information across sources and proactively surface what matters, work that has historically required teams of professionals.

This could fundamentally change who has access to financial intelligence. Instead of an investor having to know what to search for, which questions to ask or which signals to watch, AI can increasingly do that work in the background, which could make more sophisticated financial intelligence available to individuals at lower cost.

That is the opportunity Anmol Verma set out to address when she co-founded Finn (formerly Finvest), with the goal of democratizing financial intelligence by giving every investor an intelligent system working on their behalf.

Anmol Verma: From institutional investor to AI fintech founder

For Verma, the idea that technology could reshape how people invest came from years spent on the other side of the table.

Before co-founding Finvest, she spent several years as a global public-markets investor across Mumbai, Singapore, London and San Francisco, researching companies and helping manage investment portfolios for institutions. The experience gave her a firsthand view into how professional investors make decisions: the depth of research, tools and expertise that sit behind what can ultimately look like a simple decision to buy or sell a stock.

It also made the gap between professional and everyday investors difficult to ignore. While individuals had gained unprecedented access to markets, they were largely left to navigate an increasingly complex financial world on their own.

Verma eventually decided she wanted to move from investing to building. She co-founded Finvest, bringing years of experience making investment decisions into the design of an AI-native consumer financial product. At Finvest, she has helped shape how the product turns the tools and thinking of professional investors into something a broader set of people can use. Now rebranded as Finn, the platform says it has grown to thousands of users and landed hundreds of millions in connected assets.

Bringing context to financial intelligence

Financial decisions depend on connecting information that rarely arrives in one place. Market developments, company research, portfolio exposures, liquidity needs and individual preferences all shape a decision, but often sit across separate systems and sources.

AI could help connect fragmented information into a more complete picture. The same market event can have very different implications depending on an investor’s holdings, time horizon and tolerance for risk. LLMs can personalize analysis by reasoning across unstructured information and relating it to an investor’s specific goals and constraints.

Agents can bring that intelligence into the workflows where decisions are made: connecting new information to an investment thesis, flagging changes in underlying assumptions or assembling evidence to evaluate a potential action.

The power of these systems lies in their ability to work proactively around the clock: monitoring developments, surfacing potential opportunities and identifying blind spots as conditions change. Structured feedback from actions, outcomes and mistakes can help refine their performance over time, making financial intelligence a living system that is responsive to changing needs and conditions.

Building AI for money requires a different playbook

Building an AI system around people’s money comes with a different set of stakes. An AI assistant getting a restaurant recommendation wrong is inconvenient. Getting a financial decision wrong can have bigger consequences.

For Verma, that means the challenge is not simply making AI more capable, but deciding where and how those capabilities should be used. A system can monitor thousands of signals, synthesize information and personalize what it surfaces to an individual. But as it moves closer to influencing or taking action, questions around accuracy, explainability and accountability become increasingly important.

One principle that has guided Verma’s approach is to match the level of AI autonomy to the consequence of the action. At Finn, higher-stakes actions, such as moving money or executing an investment, are designed to keep the user in the loop, with additional guardrails around what the system can do. Lower-stakes actions, such as setting a personalized alert or monitoring a portfolio for a particular change, give AI more autonomy.

Much like self-driving cars, financial AI will earn autonomy gradually. The team’s approach is to climb that trust ladder over time, starting with low-stakes actions and earning the right to take on more consequential ones as the technology matures and people’s trust grows.

From intelligence to decisions

As AI becomes capable of analyzing vast amounts of information, reasoning across an individual’s financial life and increasingly acting on their behalf, many capabilities that once required significant time, expertise or money can become available to everyone. The gap between what a professional can do and what an individual can do on their own could narrow considerably.

But that creates a new paradox. As intelligence becomes abundant, simply having access to more analysis becomes less of an advantage. The scarce resource increasingly becomes knowing what matters, when to act and when not to.

For Verma, that shifts the frontier in finance from access to intelligence to something harder: how that intelligence is used to make better decisions. That is the north star. Ultimately, the promise of AI in finance should be measured not by how much intelligence it can produce, but by whether it helps people arrive at their desired financial outcomes.

This article is for informational purposes only and does not constitute investment, financial, tax or legal advice, or a recommendation to buy or sell any security. All investing involves risk, including possible loss of principal. Readers should consult a qualified financial professional before making investment decisions.

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