How to Compare Insurance Policy Wordings Reliably with AI

A useful policy comparison answers a precise question: what changed between two complete policy states, where is each change written, and which findings require professional review? It does not ask a language model to summarize two PDFs and compare the summaries. Summaries routinely compress qualifications, exceptions, and dependencies—the language that often matters most.

Start by naming the comparison and its purpose. A renewal review compares the expiring policy with the proposed or bound renewal. A placement review compares competing quotations or wordings against a required coverage specification. A policy check compares the issued policy with the binder, quote, instructions, and agreed endorsements. These are different evidence problems; one generic prompt should not stand in for all three.

The output should also distinguish text difference from coverage significance. A changed heading may be immaterial. A one-word exception, revised definition, missing endorsement, or lower sublimit may be important. The system can surface and organise those candidates, but it should label consequences as questions for review rather than issue a legal or claims conclusion.

This boundary is supported by contract-analysis research. ContractNLI frames review as entailment, contradiction, or not-mentioned classification with supporting evidence spans, and reports that negation by exception makes the task difficult. That is a better mental model than free-form similarity: every conclusion needs an explicit state and inspectable evidence.

A policy rarely lives in one file. The operative state may combine the schedule, standard wording, proposal or declarations, clauses, add-ons, warranties, and endorsements. An endorsement can add, delete, or modify a base term; a schedule can supply the insured locations, limits, sublimits, deductibles, and selected covers that generic wording leaves open.

An actual SBI General Bharat Sookshma Udyam Suraksha wording illustrates the structure. It describes the schedule as carrying details of property, sums insured, limits, locations, purchased covers, and add-ons; defines an endorsement as an amendment that may change the policy’s terms or scope; and states that the schedule, proposal, declarations, and endorsements form part of the contract. Treat this as an example of document relationships, not a universal precedence rule for every product.

Register every component with a stable policy ID, document role, insurer, product and UIN where available, policy period, effective date, issue date, version, and source file. Record whether a document is proposed, quoted, bound, superseded, or cancelled. Reject the comparison if a referenced wording or endorsement is missing instead of silently assuming it matches the other side.

Keep the concise customer-facing summary separate from the complete contract. The IRDAI Master Circular on General Insurance Business dated 11 June 2024 provides for a Customer Information Sheet for retail policies. That sheet helps orient a reader, but a reliable comparison must still trace findings to the operative schedule, wording, and endorsements.

Parse each package into addressable units before comparing it. Preserve page and bounding-box coordinates, section hierarchy, clause number, heading, list nesting, tables, and cross-references. Create typed records for insured interest, coverage grant, definition, exclusion, condition, warranty, limit, sublimit, deductible, waiting period, territory, notification duty, claims requirement, extension, and endorsement.

Do not flatten a definition into every clause that uses it. Link the clause to the defined term, then record which version of the definition applies. Do the same for schedules and endorsements. A graph is useful here because one endorsement may replace a clause, another may apply only to one location, and a schedule value may parameterise several covers. The reviewer needs to see both the changed node and the affected neighbours.

Give every extracted unit an exact source locator and confidence. For tables, keep row and column headers attached to each value. For scans, retain the page image beside OCR text. The CUAD contract-review dataset treats salient clause spans as the objects a human should inspect; its results also show substantial room for improvement. Extraction should therefore be reviewable and correctable, not buried inside a model response.

Resolve the document order explicitly. Apply only precedence rules supported by the policy package or the review team’s protocol. Do not assume that the newest file, the most specific-sounding paragraph, or an endorsement filename automatically controls. Record unresolved conflicts as findings.

First, compare the document inventory. Are the expected schedules, wordings, add-ons, and endorsements present on both sides? A missing document is not the same as a removed clause. It means the system cannot complete that part of the comparison.

Second, align clause families using identifiers, headings, defined terms, references, and semantic retrieval. Allow one clause to align with several clauses when language has been split or consolidated. Keep unmatched clauses visible. Force-fitting every paragraph to a counterpart hides genuine additions and removals.

Third, run deterministic checks over structured fields. Compare currency, limits, sublimits, deductibles, percentages, waiting periods, dates, locations, named insureds, covered property, and form editions with typed values and units. Language models can help locate the values; ordinary code should decide whether ₹10 lakh differs from ₹1 crore or whether 72 hours differs from 7 days.

Fourth, test the meaning of aligned clauses. Use a controlled finding set: equivalent, wording changed with no identified effect, broader candidate, narrower candidate, added, removed, conflicting, or not comparable. Ask whether each policy entails, contradicts, or does not mention a review hypothesis, and attach the evidence from both sides. Avoid a single similarity score: highly similar clauses can differ at one exception, and different wording can express the same obligation.

The recent CLAUSE discrepancy benchmark stress-tests models with subtle flaws in contracts and reports difficulty both detecting and justifying them. That makes a useful design warning: semantic comparison needs adversarial tests and evidence review, not confidence theatre.

Present one finding per row or card. Include the topic, change category, concise explanation, source A passage, source B passage, page or clause locators, affected definitions or endorsements, confidence, and reviewer status. Let the reviewer open the original page rather than relying on extracted text alone.

Separate observed change from possible consequence. ‘Flood sublimit changed from X to Y’ is an observed difference. ‘The renewal may leave a gap for location Z’ is an interpretation that depends on the insured assets, applicable clauses, and professional judgment. Keeping the two fields separate prevents a plausible explanation from becoming an untraceable fact.

Generate an exception summary after the detailed findings, not before. Group material candidates by coverage, exclusion, condition, limit, and document completeness. Include a section for unchanged items only when they were explicitly tested. Silence should never be presented as confirmation.

Make approvals durable. Record who accepted, rejected, or edited each finding, the evidence they saw, and the policy versions involved. When a revised quote or endorsement arrives, invalidate only the affected findings and show what must be reviewed again.

Policy packages can contain personal, commercial, and claims-sensitive information. Enforce tenant and matter-level permissions before retrieval, encrypt files and derived text, minimise model inputs, and define retention for uploads, prompts, outputs, and review logs. A public consumer model should not receive policy documents merely because its interface is convenient.

Keep the workflow aligned with current obligations and internal advice rules. The Government of India’s Department of Financial Services lists the IRDAI Protection of Policyholder’s Interests, Operations and Allied Matters of Insurers Regulations, 2024. The existence of a comparison tool does not change who may advise, approve wording, communicate coverage, or handle a policyholder’s data.

Display the source package date, comparison run time, model and parser versions, unresolved missing documents, and reviewer identity. Disable finalisation when required evidence is absent. The safest interface makes incompleteness conspicuous rather than rewarding the system for always producing a polished answer.

Build a test set from de-identified policy packages reviewed by people who understand the product and use case. Label the authoritative components, clause alignments, structured values, expected findings, evidence spans, and items that genuinely need interpretation. Split tests by product, insurer, document quality, and renewal pattern so repeated templates do not inflate performance.

Then create controlled mutations: remove an endorsement, lower one sublimit, change a currency unit, insert an exception, move a definition, reverse a negation, split one clause into two, replace a form edition, and make schedule and wording conflict. Include scanned pages, broken tables, handwritten endorsements, duplicate documents, and an incomplete package. The system should detect the change or stop with a specific evidence gap.

Measure package-completeness recall, clause-alignment recall, finding precision and recall, numeric exactness, evidence-span accuracy, false equivalence rate, and reviewer correction rate. Weight missed exclusions, limits, and endorsements according to the workflow’s risk, but retain the unweighted counts so a favourable aggregate does not hide a serious failure class.

Pilot in decision-support mode. Compare the tool’s findings with an independent human review, investigate every disagreement, and update tests before expanding scope. Do not generalise performance from one product wording to another without evidence; insurance vocabulary, clause structure, and precedence conventions vary.

If you have a real, de-identified policy comparison where schedules, endorsements, or definitions make the result difficult, share the document shape and review question at sahil@granveo.com. The useful starting point is the evidence problem—not a promise to automate the judgment.