Gains:
- Ability to produce policy summary, coverage table, offer specifications and internal procedure documents quickly and consistently with artificial intelligence
- Ability to verify guarantees, exceptions and legal statements in each document produced with general conditions and legislation
- Ability to understand that legally binding statements in the contract text require legal/compliance approval.
Insurance is a paper (or today digital document) intensive business. A policy process; It requires producing offer specifications, coverage table, policy summary, addendum (subsequent change document in the policy), internal procedure document and many standard letters. These documents must be consistent, accurate and in compliance with legislation; because an error in a guarantee table or an incorrect statement in a specification has direct legal consequences. Artificial intelligence (software that generates structured, consistent text and tables) greatly alleviates this documentation burden: drafts bid specifications, prepares coverage table, produces policy summary, writes internal procedure. But the critical distinction here is this: AI produces the format and outline of the document; Legally binding statements are determined by general conditions, legislation and legal/compliance approval. In this unit, you will learn how to produce documentation quickly and consistently with artificial intelligence, how to verify every guarantee and legal statement, and why contract language requires legal approval.
What does artificial intelligence do in documentation
AI is strong in these document tasks:
- Bid specification: Drafting the required guarantees, limits and conditions into a structured specification draft.
- Coverage table: Turning scattered coverage/limit/exemption information into a readable, consistent table.
- Policy summary: Summarizing the main coverages, exclusions and important terms of a long policy.
- Internal procedure/workflow document: Putting the steps, responsibilities and control points of a process in writing.
Most of this work is about consistency and form; Here, artificial intelligence is both fast and reduces human error (such as forgetting to write a guarantee in the table). However, there are two types of statements in every document: (1) informational/formal statements, (2) legally binding contractual statements. The first is the yellow zone, the second is the red zone.
Attention: The scope of coverage, exclusions, responsibilities and rights statements in a contract text are legally binding. These cannot be published without being confirmed with the general conditions and legislation and without obtaining legal/compliance approval.
Consistency and verification
The most common problem in the documentation is inconsistency: the limit written as 500,000 TL in the offer turns out to be 450,000 TL in the policy, and a guarantee that is in the coverage table is not in the text. AI can both produce and capture these inconsistencies. Therefore, it is used in two ways: to produce the document and to check the consistency by comparing the produced document with the source. But the final confirmation always belongs to man; In particular, numbers (limit, exemption, premium) and legal statements are verified individually from the source.
Tip: After generating the collateral table, tell the AI "compare this table with the source text, list missing/excess/conflicting collaterals." Then confirm the numbers one by one from the source. Double checking greatly reduces documentation error.
Step by step: AI-powered documentation
- Gather the resource. Relevant guarantees, limits, exemptions, general/special conditions and legislative references.
- Define the template and format. Corporate template, headers, table structure.
- Have the draft produced. Request a draft specification/table/summary in a structured format.
- Have a consistency check done. Compare the produced document with the source and mark any contradictions.
- Verify numbers and legal statements by hand. Verify limits, exemptions and binding statements from the source.
- Obtain legal/compliance approval. Approval for contract language and legally binding wording; then publish and version.
three mini cases
Case 1 — Limit discrepancy. Artificial intelligence produced a coverage table for a workplace package policy. The expert compared the table with the source offer: The limit of the "business interruption" coverage was 750,000 TL in the offer, and 570,000 TL in the table; The artificial intelligence had quoted the number incorrectly. Manual confirmation caught this error; Issuing a policy with incorrect limits was prevented.
Case 2 — Made-up legislative reference. In an internal procedure document, the AI wrote a precise reference: "In accordance with SEDDK Circular 2021/14..." The compliance specialist checked: such a circular number was not available in the available source; He made up the artificial intelligence reference. The reference has been replaced by the actual and current regulation. A fabricated regulatory reference would leave the document vulnerable to audit.
Case 3 — Saving time with consistent summary. A team would create a 1-page summary of a 40-page combination policy to be given to the customer. AI has distilled key guarantees, exclusions, and important terms into a coherent summary; The expert confirmed and confirmed that the exclusions were complete and that no guarantees were exaggerated. A half-day job was reduced to an hour, including inspection.
Four copyable prompts
1) Draft bid specifications:
You are an insurance documentation assistant. Produce a structured proposal specification draft from the following coverage, limits and condition information: [coverages, limits, exemptions, special conditions] Headings: Insured information (keep space), Guarantees and limits (table), Exceptions, Exemptions, Special conditions. Do not add any guarantees that are not in the source. Leave a note "LEGAL APPROVAL REQUIRED" for legally binding statements.
2) Coverage table production:
Turn the following scattered collateral information into a coherent table:[info]Columns: Collateral name | Scope (brief) | Limit | Exemption | Note/Exception. Adding a line that is not in the source, skipping a line that is. Quote the numbers verbatim.
3) Document consistency check:
Below is a source text and a document produced from it.Source: [paste] Produced document: [paste]Task: Compare two documents. List missing, excessive or conflicting coverage/limits/exemptions/conditions. Especially check if the numbers are exactly the same.
4) Policy summary (for customer):
The following policy produces a 1-page summary for the customer: [policy text]Content: main guarantees, important exclusions, exemptions, important conditions/periods. Hiding exceptions, exaggerating the coverage, using absolute expressions. This is a summary; Add a note that the binding text is the policy itself.
Weak prompt / Strong prompt
Weak: "Write a specification and coverage table for this policy, and add the legal statements."
Problem: Leaving legally binding statements to AI; lack of source, consistency and legal approval. Risk of fabricated substances and incorrect limits.
Güçlü: "Produce a structured draft specification and table from the following guarantee/limit information; do not add guarantees that are not in the source, quote the numbers verbatim, leave a 'legal approval required' note for legally binding statements."
Why it's good: Form and outline in AI, legal content and approval in human; Consistency can be checked.
comparison chart
document
artificial intelligence
human confirmation
Requires approval
Offer specification
Outline/format
Collateral + number
Legal expressions → law
Collateral table
Layouts
Number one to one
—
Policy summary
draft
Exception completeness
—
internal procedure
draft
Process accuracy
Regulatory reference → compliance
Contract text
draft format
—
Binding phrase → law mandatory
Common mistakes
- Not confirming the numbers. Accepting the limits and exemptions as conveyed by artificial intelligence; The wrong number is transferred to the policy.
- Using made-up legislation reference. Writing the non-existent circular/article number without verifying it.
- Publishing legal statements without legal approval. Not subjecting the binding contract language to expert/legal control.
- Bypassing the consistency check. Not seeing the contradiction between the offer, table and policy.
- Not versioning. Not recording which document was published with which version and approval.
In summary
Artificial intelligence produces specification, coverage table, policy summary and internal procedure drafts quickly and consistently in documentation; but it does not specify legally binding statements. Check each document for consistency with the source; manually confirm numbers (limit, exemption, premium) and regulatory references; Obtain legal/compliance approval for contract language and binding wording. The form and outline are with the AI, the legal content and ultimate responsibility are with the human.
Application task
Generate a coverage table from the (anonymised) coverage information of a policy with prompt number 2. Then, with prompt number 3, compare the table with the source and find inconsistencies; Then confirm the numbers one by one from the source. How many inconsistencies were there? Also, have a draft internal procedure produced and check whether every legislative reference in it is genuine.
checklist
- [ ] I collected the guarantees, limits and conditions from the source.
- [ ] I produced the manuscript using the corporate template and format.
- [ ] I had the AI perform a consistency check.
- [ ] I confirmed all the numbers (limit/exemption/premium) manually.
- [ ] I have verified the authenticity of the regulatory references.
- [ ] I have received legal/compliance approval for legally binding statements.
- [ ] I versioned the document and published it with approval.