Why Standard Model Risk Management Cannot Validate LLMs in P&C Claims

Standard model risk management frameworks cannot validate large language models running in P&C claims because those frameworks assume deterministic, repeatable outputs, and LLMs are stochastic by design: the same FNOL narrative fed through the same model twice can yield two different coverage conclusions or reserve estimates, a property that has no analog in GLM or gradient-boosted tree validation, and one that regulators begin probing directly at the NAIC's actuarial panel on July 22, 2026.

What Makes LLM Validation Different From GLM and Gradient-Boosted Tree Validation

Actuaries have spent two decades building validation muscle around generalized linear models and, more recently, gradient-boosted trees: backtest the model against holdout data, monitor a Gini coefficient or lift curve for drift, set a performance threshold, and re-certify on a schedule. That muscle assumes three things hold. The model is deterministic, meaning identical inputs produce identical outputs every time. Performance is measurable against a labeled ground truth, meaning a loss ratio or a fraud flag can be scored right or wrong. And degradation is gradual, meaning a model that scored well last quarter will not suddenly and unpredictably fail this quarter absent a real shift in the underlying data.

None of those three assumptions holds for a large language model processing an unstructured claims narrative. Run the identical FNOL description through the identical model twice at any temperature setting above zero, and the two outputs can diverge, sometimes only in phrasing, sometimes in the actual coverage determination or reserve figure recommended. Ask the same coverage question with slightly different word order, and the model's conclusion can shift, a property researchers call context-dependence and that has no equivalent in a scorecard model where feature order is irrelevant by construction. And the model can produce a confident, well-formatted, entirely wrong answer, a hallucination, with no statistical signal in the output itself that distinguishes it from a correct one. A GLM that misfires produces an implausible number an actuary can flag on inspection. An LLM that misfires produces a plausible-sounding paragraph that reads exactly like a correct one.

SR 26-2 Replaced SR 11-7 in April 2026, and Still Left Generative AI Out of Scope

Most P&C carriers built their internal model governance function around the Federal Reserve's SR 11-7 guidance, technically a banking supervisory letter but adopted informally across insurance as the industry benchmark for model risk management since its 2011 issuance. In April 2026, the Federal Reserve, the OCC, and the FDIC issued SR 26-2, the first wholesale revision of that guidance in over a decade, updating supervisory expectations for a modeling landscape that has moved well beyond the regression-era assumptions baked into the original letter. The revision explicitly carves generative AI and agentic AI out of its formal scope, on the stated basis that the technology is too novel and too fast-moving to fit the existing validation architecture.

That is not a minor footnote. It means the single most widely adopted model governance framework in financial services, the one most claims and actuarial departments already point to when a regulator or internal audit team asks how a model is validated, does not claim to cover the exact technology carriers are now deploying in claims intake, coverage interpretation, and reserve estimation. Carriers cannot simply extend their SR 11-7-style documentation to LLMs and call the gap closed, because the regulator that wrote the standard has said, in the same 2026 revision, that the standard was not built for this.

MRM Assumption	GLM / Gradient-Boosted Tree	Large Language Model
Deterministic output	Yes; identical inputs always reproduce identical outputs	No; stochastic sampling produces different outputs from identical prompts
Measurable against ground truth	Yes; loss ratio, fraud flag, or claim outcome scores as right or wrong	Partial; coverage conclusions and reserve narratives often lack a single correct answer to score against
Stable over time absent data shift	Yes; performance degrades predictably as population drifts	No; a prompt-template change, vendor model update, or fine-tune can silently shift behavior with no data drift at all
Interpretable failure mode	Yes; an implausible score is visible on inspection	No; a hallucinated but fluent answer is often indistinguishable from a correct one without independent verification

Specific Failure Modes Already Showing Up in Claims Workflows

These are not hypothetical concerns. Carriers running LLMs in production claims workflows report three recurring failure patterns. The first is coverage misinterpretation at intake, where an LLM summarizing a first notice of loss narrative drops or misstates a policy exclusion, particularly on claims involving overlapping coverages or endorsements that require reading the declarations page against the narrative rather than pattern-matching on keywords. The second is inconsistent reserve estimation across FNOL narratives that describe functionally similar losses, where phrasing differences alone, not underlying severity differences, produce materially different initial reserve recommendations. The third is regulatory language drift, where an LLM trained or fine-tuned on a national claims corpus applies a coverage interpretation standard that is correct in one state's case law but wrong in another's, a risk that scales directly with how many states a carrier writes in and how uniformly its claims LLM was trained.

Each of these failure modes shares a structural feature: none produces an error large enough to trip a conventional performance-threshold alarm, because there is no stable baseline threshold to trip against in the first place. A GLM pricing model that starts overpredicting loss ratios by 15 points sets off a monitoring dashboard. An LLM that misreads a flood exclusion on one claim in four hundred, correctly on the rest, produces no aggregate signal a quarterly stability report would catch, yet each individual miss is a live coverage decision with a policyholder on the other end of it.

What the NAIC AI Systems Evaluation Tool Pilot Is Testing For

The NAIC's AI Systems Evaluation Tool pilot, running across 12 states from March through September 2026, was not written with LLMs specifically in mind, but its four exhibits, covering AI inventory, governance framework, high-risk model detail, and data lineage, are the closest thing regulators currently have to a structured way of asking carriers how a generative model is validated. Now in its ninth month, the pilot is expected to surface concrete deficiencies at the NAIC Big Data and Artificial Intelligence Working Group's July 22, 2026 meeting, which pairs an actuarial panel on AI governance trends with a pilot progress update.

The structural mismatch shows up fastest in Exhibit C, which asks for testing evidence and human-in-the-loop protocols for high-risk models. Most carriers can produce that evidence for a claims triage scoring model built on gradient-boosted trees: a validation report with a stability metric, a bias test, a performance threshold. Far fewer can produce an equivalent report for an LLM summarizing claims narratives, because the underlying validation science, output variance across repeated runs, adversarial boundary testing, semantic consistency over time, is not yet standardized the way GLM backtesting is. The NAIC's March 2026 AI Issue Brief states plainly that "existing state insurance laws apply regardless of whether" a decision involves a human or an algorithm, which means the absence of an LLM-specific validation standard is not a defense against an examiner's question. It is the gap the examiner is there to find.

Validation Techniques That Actually Transfer

A handful of emerging approaches address the stochastic and context-dependent properties directly, rather than trying to force an LLM through a validation process built for a deterministic model. Ensemble sampling runs the identical prompt against the identical model dozens of times and measures the variance across outputs; a coverage determination that comes back the same way ninety-eight times out of a hundred is validated differently than one that splits sixty-forty, and that variance statistic, not a single point estimate, becomes the thing a validator certifies. Boundary condition testing takes the place of the adversarial test set a fraud model would run: instead of feeding the model unusual claims data, it feeds the model deliberately ambiguous or edge-case coverage language, concurrent-causation losses, stacked endorsements, conflicting policy language, and checks whether the model's confidence matches its actual accuracy on cases the model itself should recognize as hard. Semantic drift monitoring replaces the population-stability index a GLM validator would track: instead of comparing a score distribution across months, it compares the semantic content of the model's outputs against a fixed baseline set of responses, flagging a shift in how the model interprets a standard coverage question even when no traditional data drift has occurred.

None of these three techniques is a drop-in replacement for backtesting, and none produces the single clean performance number that satisfied a GLM validation sign-off. That is the actual shape of the problem: validating an LLM means reporting a distribution and a confidence interval where the old framework expected a point estimate, and most carrier governance documentation still asks for the point estimate.

Who Signs Off: The Actuarial Role in LLM Governance

A governance question follows directly from the technical one. When a technology team builds an LLM-based claims tool and a claims operations team deploys it, the validation responsibility often falls into a gap between the two: the technology team validates for uptime and latency, not coverage accuracy, and the claims team lacks the statistical background to design a variance test. That gap is exactly where state examiners are starting to look, and it is exactly where a credentialed actuary's involvement changes the answer a carrier can give. An actuary who has signed off on a reserve estimation LLM's output variance, boundary test results, and semantic drift monitoring plan gives an examiner a named, credentialed party who can speak to model behavior in the same way an actuary already speaks to a reserve indication or a rate filing. Absent that sign-off, the carrier's answer to "who validated this model" is a technology vendor's internal QA process, which is not the same standard of accountability an examiner applies to a pricing or reserving model, and increasingly not the standard examiners are willing to accept for a model that touches coverage and payment decisions.

What Carriers Should Fix Before Formal Examination Begins

Three gaps show up repeatedly when governance documentation is compared against what carriers can actually produce on request. First, most claims LLM deployments lack a documented output-variance baseline, meaning nobody has run the ensemble-sampling test described above and recorded what normal variance looks like for that specific model and prompt template, so there is nothing to compare a suspicious result against later. Second, few carriers have assigned a named, credentialed owner for LLM validation specifically, as distinct from the model's technical owner or the claims department that deploys it. Third, almost none have a semantic drift monitoring process running continuously in production, which means a vendor's silent model update, a common occurrence given how frequently underlying LLM providers ship new versions, can change claims behavior with no internal alert until a pattern of complaints or an examiner's sample review surfaces it.

Carriers that close these three gaps before the evaluation tool moves from pilot to standard practice will be answering an examiner's Exhibit C question with an actual validation report. Carriers that wait will be explaining, after the fact, why a model that behaves differently from one query to the next was never tested for how differently it behaves.

Why This Matters for Reserving and Claims Actuaries

The broader legal profession, from which claims-department document review tools often inherit their underlying architecture, already has a visible paper trail on what happens when this validation gap goes unaddressed. Court sanctions for AI-generated fabrications in legal filings totaled at least $145,000 industry-wide in the first quarter of 2026 alone, the highest quarterly total on record, and a 2026 follow-on study grading 3,000 legal AI answers found that 24% cited or misapplied law that did not actually support the claim, with every model tested fabricating or misapplying at least one citation. Insurance claims LLMs performing coverage interpretation are doing structurally similar work: reading a document, applying a legal or contractual standard, and producing a conclusion that looks authoritative whether or not it is correct. Full-scale AI adoption across the insurance industry grew from 8% to 34% of carriers between 2024 and 2025, and Travelers' early-2026 agentic voice service for auto damage FNOL now makes more than half of eligible claims candidates for fully automated, straight-through processing. Scale and validation maturity are moving in opposite directions, and the reserving implication is direct: any development pattern built on claims where an LLM influenced the intake narrative, the coverage read, or the initial reserve figure needs to be understood as data from a source whose error behavior has not been statistically characterized in the way a GLM's has. An actuary selecting loss development factors from a period of LLM-influenced settlement activity is selecting factors from a data-generating process that has not been validated to the standard the rest of the reserving methodology assumes.