When AI Meets Due Diligence, Bad Manager Data Doesn't Stay Hidden

Two of the financial industry’s most influential regulators are putting renewed attention on a basic problem with AI: the quality of the output depends on the quality of the data behind it.

For asset managers, that raises an important question: What happens when AI-assisted research is working from incomplete, outdated, or inconsistent manager data?

In its 2026 Annual Regulatory Oversight Report, published in January, FINRA highlighted risks associated with the growing use of generative AI in client-facing operations, including data quality, model bias, and governance of AI-generated communications. The report specifically cautions against reliance on limited or outdated datasets.

The Financial Stability Board has raised similar concerns about AI adoption across financial markets, including the broader data governance challenges that come with it.

For asset managers, the implications extend beyond the systems they use internally.

Why this matters beyond broker-dealers

FINRA's report is focused on broker-dealer supervision, not consultant databases. But the underlying data-quality issue is relevant to any environment where AI is being used to analyze financial information.

Allocators, consultants, and research teams are increasingly using technology to search, filter, compare, and evaluate investment managers. As those workflows become more automated, the data being analyzed matters more.

Consider what happens when a manager's AUM is outdated in one database, a strategy description differs across platforms, or key professionals and supporting documents haven't been updated.

Historically, those gaps may have been viewed primarily as a database-management or marketing issue. Increasingly, they can affect how a manager is represented when investment professionals are using technology to conduct research.

A manager may have a strong strategy and a compelling track record, but if the information available to the research process is incomplete or inconsistent, the resulting picture may not accurately reflect the firm.

Data governance is becoming part of due diligence

This is where consultant database management starts to look less like a marketing task and more like a data-governance issue.

The goal isn't simply to have a completed profile. It's to make sure the information available across the databases consultants and allocators use is accurate, consistent, current, and complete.

That includes the fundamentals — AUM, performance, strategy and vehicle information — as well as the broader profile data that can influence how a manager is evaluated: investment professionals, firm information, narratives, documents, and historical data.

As AI becomes more integrated into research and due diligence, the quality of that underlying information becomes increasingly important.

Where IMSS fits

This is the work IMSS has focused on for years: helping asset managers maintain accurate, current, and consistent information across the consultant databases that matter to institutional investors.

AI doesn't eliminate the need for good data. It makes good data more important.

For asset managers, that means database coverage deserves a closer look — not simply because an incomplete profile can reduce visibility, but because the information being surfaced about your firm should accurately represent what you actually offer.

Is your database presence ready for an increasingly AI-assisted research environment?

Reach out to IMSS for a review of your consultant database presence and a plan to address the gaps.