AI in Risk8 min read

Validating Large Language Models in Finance

By Jonas Adam Mohamed Osman, known as Yonas Osman

CEO of Quantica Risk

60 Rue François Ier, 75008 Paris, France

Published 15 October 2026

A practical validation approach for large language models in financial services, covering hallucination risk, testing, and ongoing monitoring.

Executive answer

Large language models deployed in financial services, whether for customer support, document summarisation, or internal research assistance, pose validation challenges that traditional statistical models do not: non-deterministic outputs, a vast and largely uninterpretable parameter space, and hallucination risk that can produce fluent but false statements. Validation frameworks built for regression or machine-learning credit models need meaningful extension rather than wholesale replacement, since the underlying principles of independent challenge and ongoing monitoring still apply.

Supervisors have signalled that existing model risk management expectations, such as SR 11-7 in the US or the PRA's SS1/23 in the UK, extend to generative AI, meaning firms cannot treat LLM deployments as exempt experimentation. A defensible validation programme documents intended use, tests against that use with representative scenarios, and sets monitoring thresholds for drift in output quality, factual accuracy, and bias before a model reaches production in any customer-facing or decision-support role.

Testing for hallucination and factual accuracy

Hallucination testing requires curated benchmark datasets reflecting the firm's actual domain, such as policy wordings, regulatory text, or product documentation, with human-graded accuracy scores rather than generic public benchmarks that poorly reflect specialised financial content. Retrieval-augmented generation reduces but does not eliminate hallucination risk, so validation must test both the retrieval component's precision and the generation component's faithfulness to retrieved content, since errors can arise at either stage.

Red-teaming exercises, where testers deliberately probe edge cases, ambiguous queries, and adversarial prompts, surface failure modes that standard accuracy testing misses, including prompt injection and inappropriate disclosure of sensitive information. Results should feed a documented risk register with severity ratings, and any customer-facing deployment should carry a human-in-the-loop checkpoint for outputs above a defined materiality or complexity threshold, consistent with existing conduct risk controls.

Ongoing monitoring and governance

Unlike static statistical models, LLMs are frequently updated by vendors through silent model version changes, making point-in-time validation insufficient. Firms need contractual visibility into vendor update cadences and a re-validation trigger whenever the underlying model version changes materially, alongside continuous sampling of live outputs against quality and bias metrics defined at approval. Logging must be detailed enough to reconstruct what the model saw and produced for any output under review.

Governance should assign clear accountability for LLM use cases within the existing model risk committee structure, with escalation paths for degraded performance or emerging bias patterns. Firms building internal LLM applications on top of third-party foundation models carry dual responsibility: validating the foundation model's general behaviour and validating the fine-tuning, prompting, or retrieval layer added on top, since each introduces distinct failure modes that need separate test coverage.

Conclusion

LLM validation in financial services is less about inventing new theory and more about disciplined extension of established model risk management practice to a class of models with different failure modes and faster release cycles. Firms that under-invest in this extension risk conduct breaches, poor customer outcomes, and supervisory findings as regulators increasingly ask specific questions about generative AI governance during examinations.

Jonas Adam Mohamed Osman (Yonas Osman), CEO of Quantica Risk, approaches LLM validation by treating intended-use scoping and independent challenge as the anchor points, ensuring generative AI deployments meet the same rigour expected of any other model entering a regulated firm's inventory.

Primary sources

LLMmodel validationgenerative AIbanking