How Claude Code Is Transforming Finance—Without Turning You Into a Coder

TL;DR: As the head of financial services consulting at Every, Brooker Belcourt has helped take top hedge funds, asset managers, and research teams from AI-curious to AI-native. Today, he shares the reasons why the financial industry—one of the best placed to take advantage of AI—still isn’t getting the most out of this new technology. It doesn’t require investors to become coders, just proficiency with Claude Code and a clear view of the tasks to automate—lessons that apply to any industry.—Kate Lee

Plus: If you work in finance and want to learn more, join us on March 13 for a day-long Claude Code for Finance course. We’ll get you set up with Claude Code, download the Every investor plugin, set up the Daloopa MCP and Carbon Arc MCP, and customize the plugin to your investment philosophy. Register for the course.

If any industry was made for AI, it’s finance. The workflows are structured, the tasks easy to map out. Investment processes live as written procedures, compliance requirements, and repeatable research frameworks. An earnings review has defined steps. That predictability is exactly the environment where AI thrives.

But working with hedge funds, asset managers, and research teams as part of Every’s consulting team, I’ve learned that investors aren’t always getting the most out of AI. We’ve seen this pattern repeatedly: A team gets excited about AI, spends a few weeks trying to get something working, hits a technical snag, and quietly goes back to doing things the old way.

It’s a funny little conundrum that firms in one of the industries best fit for AI struggle to figure out how to implement it. But several low-hanging solutions can accelerate AI adoption in finance.

Here’s a primer on how to get started, based on what I have seen from six months of supporting firms representing more than $100 billion in assets under management.

Start with Claude Code

Financial services teams scour through earnings calls, portfolio reviews, and limited partner updates—massive amounts of data. AI is a natural solution to synthesize these inputs and quickly find patterns in them. However, common LLM tools don’t connect to all of your data, and it can take too much time working with LLMs to get it right. If you’re only using ChatGPT or Claude chat, they aren’t built to handle complex, multi-step workflows and incorporate structured and unstructured data.

Usually, the first step I take with finance teams is to recommend Claude Code. Unlike alternative LLM tools, Claude Code can run for hours on a single task, access all your files without limits—including folders and files stored locally on your computer—and write and execute code automatically. It can also plan, allocate agents to a task, and run work in parallel. This lets you tap into the full capability of newer models such as Opus 4.6 and GPT-5.3 Codex.

It’s also the best tool for large amounts of data and complex tasks. Data is useless unless you can connect data sets that don’t naturally speak to each other.

For example, in an earnings preview, which lays out what analysts and investors should expect to see in a company’s quarterly earnings, Claude Code excels at connecting multiple data sets that are rarely paired together, including alternative data sets and fundamental data. This could be data that lives in the browser, such as economic data releases from a central bank, or in someone’s Downloads folder, and historically would take an engineer to bring together.

Define what you’d like to get done

The second step for teams is to clearly define the task they need to complete. These are the most common tasks that we help financial professionals accelerate with AI:

1-Preparing for meetings

Meetings are important to close deals and make decisions quickly, but preparing for them is a major time sink. An analyst might spend hours pulling performance data, refreshing their notes on each stock holding, and summarizing what’s changed since the last meeting before the team assembles to review an investment portfolio. Before an institutional investor updates their investors, someone on the team must manually compile returns data, write commentary, and make sure the narrative is coherent across the whole document.

The firms we work with that have gotten the most out of Claude Code start here. An analyst defines what a good prep document looks like once, as a standalone skill or within a plugin. Then it’s a question of the AI populating that format (be it email or notes) with the right data each time. Meetings are more consistent, rigorous, and effective.