Balfour Capital Group on AI and Reputation. A Powerful Tool That Still Needs Human Judgement

Balfour Capital Group (BCG) today released a new white paper, The Reputational Risk of Generative AI: How AI-Generated Corporate Profiling, Hallucination, and Entity Confusion Create Material Risk for Businesses, Investors, and Financial Institutions, authored by the firm’s Chief Investment Officer, Steve Alain Lawrence.

The paper addresses a fast-emerging but largely unmanaged category of risk: the growing tendency of investors, journalists, regulators, compliance officers, and prospective clients to begin due diligence not with a search engine or a regulatory database, but with an AI assistant — and to treat its output as verified fact.

“Large language models do not retrieve and report the truth; they predict the most statistically probable sequence of words,” said Lawrence. “Most of the time the prediction is accurate. But when it is wrong, the output is delivered with exactly the same fluency and apparent authority as when it is right. For the reader, there is no visible difference between a verified fact and a confident fabrication.”

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A tool to be used, not a verdict to be trusted

The paper is emphatic that it is not an argument against artificial intelligence. These systems are genuinely transformative and here to stay, Lawrence writes, and the right response is neither alarm nor avoidance. The distinction at the heart of the paper is one of posture: AI is an extraordinarily capable research aid, but it is not a perfect or authoritative source, and it should be used as a starting point rather than a conclusion.
“A search engine hands you the library; a language model hands you a verdict — and rarely shows its work,” said Lawrence. “The firms and investors who navigate this well are the ones who use these tools deliberately, as one unverified input among many, and who always confirm what matters against primary sources.”

A material risk, not a theoretical one

The white paper documents how AI systems can state that a firm is unlicensed, under investigation, or sanctioned when the underlying facts are materially different — and how they can confuse one company with an unrelated entity that merely shares a name. It cites Stanford research finding that even purpose-built legal AI tools hallucinate on between 17% and 33% of queries, and an independent legal database now tracking more than 700 court decisions worldwide involving fabricated AI-generated citations, roughly 90% of them arising in 2025 alone.

The paper grounds its argument in documented cases — including Moffatt v. Air Canada, Mata v. Avianca, the Brian Hood and Arve Hjalmar Holmen matters, and Deloitte Australia’s AU$440,000 government report found to contain fabricated references — to show that confident AI output accepted without verification has already produced measurable financial, legal, and reputational harm across industries.

Why financial services is most exposed

Lawrence argues that financial services faces elevated risk for a simple reason: trust is the product. Because counterparties evaluate firms sequentially — preliminary screen, meeting, due diligence, allocation — an inaccurate AI summary at the earliest stage can quietly eliminate an opportunity before a firm ever has the chance to present primary-source documentation. The white paper traces how a single inaccurate AI profile can flow through to delayed transactions, lost mandates, and elevated compliance cost that never appears on any invoice.

Transparency as part of the solution

A central theme of the paper is that the burden of managing this risk should not fall on individual firms alone — and that greater transparency is essential to addressing it at the source. Lawrence calls on AI developers to strengthen trust through clearer source attribution so users can see where a claim originates, meaningful indications of confidence and uncertainty, a sharper distinction between stated fact and model inference, and accessible, responsive mechanisms to correct demonstrably false information about real entities. He urges regulators to consider transparency expectations around source attribution and data freshness, and to create avenues by which a company can challenge and correct inaccurate information that causes it harm.

That same principle of transparency runs through the paper’s guidance for firms, which is encouraged to maintain an authoritative, openly published record of its own structure, licensing, and history — so that both human readers and AI systems have a clear, primary-source reference to draw on.
A practical framework

Rather than urging firms to avoid AI, the paper sets out a management framework that any organization can adopt incrementally: monitor what AI systems say about you through periodic audits; publish plain-language structure and licensing explainers; assign clear ownership and an escalation path; maintain retrievable primary-source documentation; engage AI developers constructively; set advance thresholds for legal action; and educate counterparties that AI is a research aid, not a due-diligence substitute.

“Your reputation is no longer defined solely by what people say about you,” Lawrence said. “It is increasingly defined by what artificial intelligence says about you. For a regulated firm, accuracy is no longer merely a competitive advantage — it is a fiduciary responsibility.”
About Balfour Capital Group

Balfour Capital Group is a global multi-asset investment management firm serving institutional and professional clients across multiple jurisdictions. The difference is global access.

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