What Is an LLM Hallucination, and Why Does It Occur?
According to an IBM blog post, an LLM hallucination occurs when the model “perceives patterns or objects that are nonexistent or imperceptible to human observers, creating outputs that are nonsensical or altogether inaccurate.”
Huzaifa Sidhpurwala, senior principal product security engineer at Red Hat, explains that LLMs “excel at mimicking humanlike communication and producing contextually relevant responses,” but hallucinations remain a “critical limitation.”
This matters greatly in financial services, where customers may turn to AI-driven chatbots for sensitive information on mortgages, investment accounts or insurance claims. If the system fabricates data, the institution could inadvertently mislead customers or regulators.
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What Are the Risks for Financial Institutions?
While hallucinations pose challenges across industries, financial services face unique and heightened risks:
- Reputational damage: A bank or insurer relying on an LLM that provides a plausible but false statement could lose customer trust, which is particularly fragile in finance.
- Regulatory and compliance risk: The financial sector operates under strict regulations via the U.S. Securities and Exchange Commission and other agencies. If an AI hallucination produces inaccurate disclosures, guidance or advice, it could result in noncompliance and trigger penalties.
- Financial exposure: Erroneous outputs could lead to poor investment decisions, incorrect underwriting calculations or mishandled fraud detection alerts, creating direct financial losses.
- Operational inefficiency: If AI-generated code for risk modeling or reporting includes errors, IT teams may spend more time fixing issues than they would building solutions from scratch, driving up costs.
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Real-World Lessons: AI Hallucinations in Action
Examples from other industries highlight the consequences of AI hallucinations:
- In 2023, a judge sanctioned two attorneys who used ChatGPT to write a legal brief that cited six nonexistent cases.
- Air Canada was ordered to compensate a passenger after its chatbot provided false refund policy information, creating both reputational and legal fallout.
Imagine a similar scenario, in which a credit union’s chatbot provides the wrong information on loan eligibility, or a bank’s AI tool misstates interest rates. Such issues could quickly escalate into litigation or enforcement actions.
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Best Practices To Mitigate LLM Hallucinations in Financial Services
To minimize risks, financial institutions should adopt the pillars of responsible AI — transparency, explainability, inclusivity and sustainability. Beyond these foundations, IT leaders should consider:
- Small language models: Train models on domain-specific financial data rather than the open internet, reducing exposure to irrelevant or misleading information.
- Retrieval-augmented generation: Ground outputs in the institution’s verified, up-to-date business data to ensure accuracy in customer-facing applications.
- Data governance: Establish strict provenance and quality controls so only trusted data informs AI systems.
- Human oversight: Ensure AI-generated decisions — whether for investment analysis, loan approvals or claims processing — are reviewed by qualified staff before execution.
“Above all else, using trustworthy data with a strong provenance is vital,” Sidhpurwala says.
