Beru Learning

Insurance contracts outline general coverage on the initial declaration pages while placing major conditions, limits, and exclusions across dozens of subsequent pages. Policyholders often only learn about these restrictions when a claim is reduced or denied.

Using structured prompts, parsing insurance policies with LLMs helps consumers locate specific coverage caps, identify explicit exclusion clauses, and compare policy terms before issues arise.

Extracting the Core Limits from Declaration Pages

The declaration page contains the high-level financial terms of an insurance policy: premiums, liability limits, and base deductibles. However, it does not detail how these numbers apply during specific losses.

Convert the policy's declaration page and definitions section to text, then instruct the model to organize the baseline numbers:

Extract the following from this policy text: 1) Policy effective dates. 2) Per-incident vs aggregate liability limits. 3) Separate deductibles for specific peril categories (such as wind, hail, or collision). 4) List all attached endorsement form numbers. Format the output as a clear key-value list.

This step establishes the baseline limits needed to evaluate subsequent exclusions and endorsements.

Targeted Prompts for Finding Exclusions and Sub-Limits

Property policies routinely place restrictive sub-limits on specific categories, such as electronics, jewelry, or water backup, even when the overall personal property limit is high.

Run the policy's exclusions and endorsements sections through the model using this prompt structure:

Sub-Limits: Locate specific dollar caps on individual categories of property.
Excluded Perils: List events explicitly barred from coverage, such as flood, earth movement, or sewer backup.
Filing Conditions: Identify required timeframes for reporting damage or providing proofs of loss.
Valuation Methods: Determine whether claims are settled at Actual Cash Value (ACV) or Replacement Cost Value (RCV).

Cross-Referencing Endorsement Riders Against Base Policies

Insurers modify base contracts using endorsement riders. An endorsement can alter or remove protections established in the original contract text.

Provide both the original policy clause and the endorsement rider to the model with this comparison prompt:

Compare Clause A from the base policy with Rider B. State clearly how Rider B changes the insurer's liability. Does Rider B expand coverage, add exclusions, or increase the policyholder's deductible? Answer in two direct paragraphs.

This comparison identifies what rights or coverage terms the rider modifies, giving you specific points to review with an insurance agent.

Frequently Asked Questions

Can an LLM guarantee whether a claim will be paid?

No. Claim approvals depend on specific claim facts, adjuster assessments, and local insurance laws. A language model can help interpret contract language, but it cannot determine claim outcomes.

What is the difference between Actual Cash Value and Replacement Cost?

Actual Cash Value pays for damaged property minus depreciation. Replacement Cost covers the expense of purchasing a new, equivalent item at current prices without a depreciation deduction.

Why do endorsements often confuse policyholders?

Endorsements are written as standalone modifications that reference earlier policy sections. Without reading both documents together, it is difficult to identify which terms have been superseded.

Key Takeaways

Declaration pages summarize high-level limits but do not list specific policy exclusions.
Sub-limits often restrict coverage on valuable items like jewelry and electronics.
Targeted prompts can locate excluded perils and mandatory claim reporting deadlines.
Comparing original contract clauses with endorsement riders shows how coverage has shifted.
Use model outputs to identify questions for a licensed insurance agent.

Using LLMs to Parse Insurance Policy Declarations and Exclusions