Why Asset Managers Need AI Research

Hello, this is Treasurer.

How are global hedge funds using AI in their investment work?

When people think of AI entering financial markets, they often picture AI picking stocks and placing trades on its own.

But the way global investment firms are adopting AI today is a little different.

Global investment firm Balyasny Asset Management (BAM) recently brought Google Cloud’s Gemini into its in-house investment research platform.

Balyasny’s 200+ investment teams around the world analyze a vast amount of information every week, from earnings releases and corporate filings to central bank announcements and market data.

To process it faster, the firm built its own AI research system, BAMAgent, and connected it to more than 80 internal financial databases and work tools.

Large hedge funds are already bringing AI into their actual investment research work, not just using it as a search tool.

But does every asset manager need to build a system like this from scratch?

What AI is changing first is the process before the investment decision

Source: Mirae Asset Group

Imagine analyzing a single company.

An analyst has to check earnings releases, filings, news, and financial statements, then compare competitors and market data as well.

The problem isn’t a lack of information.

The real inefficiency in financial research is that there is too much to check, and the data you need is scattered across different places.

This process is exactly what AI is changing first, before it ever takes over investment decisions.

Take a question like “Find companies whose earnings outlook has shifted sharply, and analyze why.” A simple web search isn’t enough to answer it. You need to check earnings consensus, financials, corporate filings, news, and market data together.

That is also why Balyasny connected AI to a wide range of internal financial data.

The key to financial AX is not which AI model you use, but how well you connect financial data to real work.

Not every asset manager needs to build its own AI research system

Large firms like Balyasny can build AI research systems in-house, using their own engineering teams and data infrastructure.

But for most asset managers and investment firms, connecting dozens of databases and building AI agents and a research platform from scratch is hard.

What matters is not copying Balyasny’s system, but connecting AI to the tasks each financial firm actually repeats day to day.

To support this work, Treasurer offers a range of specialized financial agents.

For example, when a new issue comes up at a company, you can link

News search → DART filing check → Research report search → Consensus & target price changes → Earnings estimate comparison

into a single flow and cross-check a wide range of financial data.

This lets analysts spend less time finding and organizing data, and focus more on forming hypotheses, testing them, and making judgments.

Treasurer connects the entire research process into one flow

Treasurer Research Bench – one research workflow, from data collection to report generation

If individual agents find the data you need, Research Bench connects that process into a single research workflow.

Data collection → Analysis → Report generation → Source tracing

Financial institutions can set up a research environment that fits their existing data and workflows, turn the analysis into reports in the format they want, and then trace back to the original sources behind them.

Because in finance, being able to see where an answer came from matters just as much as getting it fast.

Source: GTT Korea

Balyasny’s case shows that financial AI is moving beyond experiments and into real research work.

At the same time, Balyasny focused on one more element: data security.

Using Google Cloud’s VPC Service Controls, it designed the system so that trading data and investment strategies do not leave its private cloud environment.

Treasurer can likewise run in a client-dedicated VPC or on-premises, with the client holding the encryption keys. Client data is not taken outside or used for model training, and Treasurer is certified under ISO/IEC 27001, the international standard for information security management systems.

Bringing the AI research environment that large hedge funds are building in-house to each financial firm, fitted to its own data and workflows, faster and more securely.

That is the financial AX Treasurer is building.