How AI handles insurance endorsements safely

What AI is good at with endorsements

• Recognizing that an email is an endorsement request, and what kind: vehicle, driver, location, limit, named insured, additional insured.
• Matching it to the customer and the policy, and saying how the match was made.
• Pulling details out of the request and attachments: a VIN, a driver's name and date of birth, an address.
• Listing what is missing compared with what the carrier will need.
• Drafting the reply that asks for the missing details in one go.
• Keeping the task on a clock and following up while it waits on the customer or the carrier.

What AI must not decide

• Whether a change is bound. Only the carrier, or an agency with authority, binds.
• What a customer meant when the request is ambiguous.
• Whether a contract's insurance requirements are met.
• Whether a coverage is appropriate for the customer: that is advice.
• Anything about eligibility, underwriting, rating or claims.

The controls to insist on

Before you let any AI tool near endorsement requests, ask the vendor to show you these controls working, not describe them:

• A confidence threshold: when the model is unsure what a request is, it goes to a person instead of being guessed. You should be able to see and set the threshold.
• Matching that stops: if the customer, policy or requester cannot be matched, the automation stops.
• Source on every value: each detail it extracted shows where it came from, so a person can check it in seconds.
• Approval by a second person for anything that leaves the agency, and the preparer can never approve their own work.
• A record of every step: what the model read, what it decided, who approved and when.
• No training on your data, in writing in the vendor's terms and the model provider's.

Keeping customer data safe

Endorsement requests carry personal information: dates of birth, license numbers, addresses, sometimes financial details. Ask what is sent to the AI provider, whether it is retained, where it is processed and who can see it. Ask how personal email that lands in the same mailbox is handled. Under the Gramm-Leach-Bliley Act and state insurance data-security laws, the agency must oversee its service providers, so the answers should be in writing in a data processing addendum.

Measuring whether it is safe

If the correction rate is not falling after the first few weeks, tighten the threshold rather than loosening the approval.

• How many endorsement requests went to a person because the AI was unsure, and whether those were the right ones.
• How many extracted details a person had to correct.
• How many replies were sent back by an approver, and why.
• Whether the time from request to carrier submission went down.

Rolling it out without risk

Start with the endorsement types that are most frequent and least ambiguous, usually vehicle and driver additions, and leave the rest with people. Run the AI in a quiet mode first: it reads and drafts, but nothing is sent, and your team compares its work with their own. Move a change type into normal use only when the correction rate is low and stable, and keep approval on for every reply that leaves the agency.

Tell your E&O carrier what you are doing. Many carriers publish risk-management guidance on technology and procedures, and a documented rollout, with approvals and an audit trail, is far easier to defend than an informal one.

How Shlix IBS does it

Shlix IBS recognizes endorsement requests, matches them, lists what is missing and drafts the reply asking for it. It does not make the policy change: your team works the carrier portal or the AMS, and the task stays on a clock until the carrier confirms. Low-confidence requests go to an Exceptions queue for a person. Our AI use policy sets out the models we use and what they see.

Common mistakes and what they cost

Each of these is a common route to an errors-and-omissions (E&O) problem. None of it is legal advice; follow your carriers' rules and your agency's procedures.

• Letting AI resolve an ambiguous request. The model picks an interpretation; if it is wrong, the policy is changed in a way the customer did not ask for.
• Telling customers a change is done because the AI processed it. Processing a request is not binding. A customer who believes they are covered when they are not is an E&O claim.
• No source shown for extracted values. Reviewers cannot check quickly, so they either approve without checking or redo the work.
• Not asking about training and retention. Customer information may be used or kept in ways the agency's privacy obligations do not allow.

Questions