Rethinking AI in Finance: From Automation to Intelligence

General-purpose AI has seen a meteoric rise in adoption, and while brilliant at writing poems and summarizing Wikipedia articles, there are still large gaps that need to be addressed for it to be useful inside a bank or fund manager.

The problem isn’t the technology; it’s the fit (or lack of it) within the finance professional’s workflow. Finance demands accuracy, transparency, compliance, and context that generic chatbots were never designed to provide. This is why foundational models struggle to meaningfully improve the daily work of finance professionals.

At Finster, we are changing that. We have built and deployed systems designed from the ground up for the way bankers and analysts actually work. Our mission is simple: to build intelligent AI agents that understand your workflow, your data, and your process as well as you do. When implemented successfully, your organization functions as “AI-native” with workflows that still accomplish your goals but are built for the AI era.

Why AI in Finance Needs a Different Approach

When I was at DeepMind, we were focused on how machines learn. But in finance, the challenge isn’t just learning, it’s trust. A single incorrect number can destroy credibility or even move markets. That’s why applying AI to finance requires a fundamentally different mindset.

Financial professionals operate in an environment of:

• Low tolerance for error: Everything must be auditable and explainable.
  
  

• Complex, siloed data: From SEC filings and IR decks to internal notes, third-party data and deal docs.
  
  

• Time pressure: Analysts and bankers don’t have the luxury of slow iteration cycles, they need to deliver results under pressure.
  
  

Generic AI tools fail because they weren’t built for these constraints. They can’t tell you why an answer is true, can’t show you where it came from, and don’t understand the nuances of your workflow. And if you can't trust your AI, you can’t use it. AI in finance must therefore be purpose-built to be private, precise, and integrated with the data the organization uses to make decisions.

Beyond Productivity: Building Intelligence

Most people still talk about AI in finance as a productivity play. They think of saving time and automating routine tasks. While important, that’s just the first step.

True financial intelligence comes when AI starts to reason and to challenge your assumptions, test theses, and critically question your judgment. Imagine a system that reviews your model and highlights which assumptions are too optimistic. Or AI that flags inconsistencies between your pitch deck and a company’s latest filing before your MD ever sees it. This is something you would expect a senior colleague to do, and that’s the point.

This is where we’re heading: from reactive tools to proactive thought partners that help analysts and bankers think better, not just work faster.

A New Division of Labor Between Humans and Machines

In investment banking and research, the best teams don’t just collect data, they interpret it and make decisions with it. The real value is in asking the right questions, connecting dots others miss, and communicating insights clearly.

AI shouldn’t replace that; it should amplify it.

• Analysts can move beyond manual data gathering to hypothesis testing and creative thinking.
  
  

• Associates and VPs can iterate faster, using AI to simulate how markets or clients might react.
  
  

• Managing Directors can walk into meetings with automatically generated briefs that integrate both public and internal insights.
  
  

In other words, AI doesn’t flatten the hierarchy, but accelerates learning across it. AI helps codify institutional knowledge that has a stubborn way of staying locked inside the minds of a few SMEs. Fully leveraging this knowledge can become a force-multiplier across the entire organization.

Privacy and Compliance Aren’t Optional

While I’m sure all of this sounds great to people performing under pressure until 2 o’clock in the morning, none of this works without trust. Financial data is sensitive, and firms cannot afford models that leak or “learn” from proprietary information only to be used elsewhere outside their org. That’s why our approach to AI in finance starts with privacy: on-premise or virtual private cloud deployments, strict data lineage tracking, and full audit trails for every output.

AI can only become a true co-worker if users know it’s compliant, secure, and transparent. Trust isn’t an add-on feature, it’s the foundation. Everything else is secondary.

Personalized Financial Intelligence is Already Here

Imagine logging in tomorrow and finding a morning brief already waiting with summaries of the companies you follow, the filings that changed overnight, and even the next steps for your current pitch. Imagine an intelligent AI colleague who acts like a sparring partner, finding gaps in your reasoning, missing data in your decks. That’s not science fiction; it’s what our clients are seeing right now.

Finance has always rewarded those who can combine insight with speed. The future belongs to firms that can do both—with the help of AI that thinks like a partner, not a tool.

Are you using AI to the fullest?

Reach out to me directly, let’s talk about it!