How to choose AI solutions for insurance

Where does AI pay off first in insurance, and why?

Insurance runs on documents: submissions, schedules of values, loss runs, applications, medical records, first notices of loss, estimates, demand letters, bordereaux and treaty wordings. Most of the cost and delay sits in people reading, keying and cross-checking these before anyone makes a decision. AI that reads those documents and checks them against the policy, the guideline or the binder pays off first, because it removes the waiting without moving the decision away from the underwriter or adjuster.

The second payoff is coverage of the whole book instead of a sample. Audits of delegated claims, estimate reviews, legal bill reviews and closed-claim compliance checks are usually sampled because there are not enough people to read everything. When software reads every file, the sample becomes the full population, and the reviewers spend their time on the exceptions it finds.

The money at stake is large. The Coalition Against Insurance Fraud estimated in 2022 that insurance fraud costs the United States about $308.6 billion a year, which is why fraud referral, document testing and subrogation are early candidates in claims.

Which processes should we start with?

Start where the volume is high, the documents are messy, the rule being checked is written down, and a person already signs the outcome. Those four conditions make a process easy to measure and safe to automate in steps.

Avoid starting with a process whose rules live only in one expert's head, or one where the AI would make the final decision about a customer. Those can come later, once you trust the evidence trail.

• Underwriting: commercial submission intake and clearance, loss run analysis, schedule of values checks, life underwriting medical record summaries.
• Claims: first notice of loss triage, coverage verification against the forms in force on the date of loss, bodily injury demand review, fraud referral.
• Recovery: subrogation identification on new and closed claims, so recoveries are not lost to a statute deadline.
• Distribution and servicing: policy checking against quote and binder, endorsement processing, certificate requests.
• Delegated authority and reinsurance: bordereaux mapping and checks, binder compliance, treaty wording comparison.

What should we demand on accuracy, citations and human sign-off?

Demand that every extracted value and every finding opens to the exact passage it came from: the page of the loss run, the clause of the endorsement, the line of the estimate. An answer without its source cannot be checked by an underwriter, an auditor or a regulator, and so it cannot be relied on.

Demand that a named, qualified person makes and signs every decision that matters: to quote, decline, pay, deny, refer to the SIU or settle. The software should read, check and draft; it should never bind, deny or pay on its own. Ask to see where the sign-off happens and what is recorded when it does.

Measure accuracy on your own documents, not on a vendor's demo set. Agree the fields and findings that matter, have your staff mark the right answers on a sample of real files, and score the system against them before go-live and again after.

What about security, deployment, data and regulation?

Claim files and life applications carry health, financial and personal data. Ask where the data will live, who can see it, whether it is encrypted in transit and at rest, and whether your inputs and outputs are used to train the vendor's models. Botminds runs as multi-tenant SaaS, in a dedicated cloud environment, or fully on-premises, with tenant isolation, AES-256 encryption at rest, role-based access, immutable audit logs, SOC 2 Type II and ISO 27001, and your inputs and outputs are not used to train models.

Regulators now expect insurers to govern the AI they use, including AI bought from vendors. The NAIC adopted its Model Bulletin on the Use of Artificial Intelligence Systems by Insurers on 4 December 2023; by 8 October 2026, the NAIC's own tracker listed 29 jurisdictions that had adopted it, with California, Colorado, New York and Texas listed as having their own insurance-specific rules or guidance. The bulletin expects each insurer to keep a written AI program covering governance, risk controls, testing and the diligence it applies to vendors. New York's Circular Letter No. 7 (11 July 2024) covers AI and external consumer data in underwriting and pricing, and Colorado's Regulation 10-1-1, amended with effect from 15 October 2025, requires life, private passenger auto and health benefit plan insurers that use external consumer data to run a governance and risk management framework and test for unfair discrimination by race.

In the EU, the AI Act lists AI used for risk assessment and pricing of natural persons in life and health insurance as high-risk. Under the Digital Omnibus on AI that EU legislators agreed in May 2026, those obligations apply from 2 December 2027 instead of 2 August 2026. Ask any vendor how its system supports your model inventory, testing and documentation for those rules.

How do we run a pilot and measure it?

Pick one process, one team and one baseline. Before the pilot, record how long the work takes today, how many files a person handles, how often errors reach the customer or the regulator, and how much is sampled rather than fully reviewed.

Run the pilot on live work, not on a cleaned-up test set, with the same people who will use it afterwards. Keep the human decision in place and compare: time to a ready file, share of files fully reviewed, accuracy against the marked sample, and how often staff overrode the system and why.

Set the success line in writing before you start, and decide in advance what you will do if it is met. A pilot without a decision rule tends to run forever.

Should we build, buy a point product, or use a platform?

Building in-house gives full control but takes a team that understands both the models and insurance operations, and the result has to be governed, tested and maintained like any other system a regulator can examine. Point products are quick for one task, but a carrier that buys a separate tool for submissions, claims triage, bordereaux and treaty review ends up with four security reviews, four audit trails and four places where the evidence lives.

A platform approach puts every solution on the same controls, audit log and sign-off model. Botminds is built this way: it offers a library of insurance solutions, from submission clearance to treaty wording comparison, and builds new ones from your own requirements on the same governed platform, so the second and third processes start from the first one's controls.

What changes for underwriters, adjusters and their teams?

The work shifts from gathering and keying to reviewing and deciding. An underwriter opens a submission that is already read, cleared and checked against appetite; an adjuster opens a claim with the coverage position drafted and every fact cited. They still decide, and the record shows that they did.

Plan for the change: train staff on how to check a cited finding and how to override it, track overrides as a quality signal rather than a failure, and give supervisors a view of where files are waiting. Teams adopt tools that let them see why an answer was given; they work around tools that do not.

What does it cost to wait?

Every month of manual reading is a month of submissions answered after a competitor quotes, claims that sit before anyone sizes them, recoveries that run past their statute, and reviews done on a sample. Those costs show up as lost premium, higher loss adjustment expense and leakage, not as a line item labeled AI.

The rules are also moving. With 29 jurisdictions on the NAIC bulletin by 8 October 2026 and EU high-risk obligations dated for 2 December 2027, insurers that start now build their AI inventory and testing habits on a small, well-run process instead of rushing to document many at once.

Solutions to look at first

• Commercial Submission Intake & ClearanceAn intake solution for commercial carriers that reads, keys and clears every broker submission.
• Loss Run Analysis AutomationAn underwriting solution that turns prior-carrier loss runs in any layout into one loss view.
• Underwriting Guidelines AssistantAn underwriting knowledge solution that answers eligibility questions from guidelines.
• APS SummarizationA medical review solution for life underwriters that turns APS pages into a cited summary.
• FNOL & Claims Triage AutomationAn intake solution that reads each first notice, matches its policy and routes it by severity.
• Claim Coverage VerificationA coverage analysis solution that says if a loss is covered, quoting the form and endorsement.
• Bodily Injury Demand Package ReviewA demand review solution that summarizes records, totals specials and drafts the reply in time.
• Claims Fraud DetectionA fraud detection solution that reads every new claim and refers the suspect ones to the SIU.
• Subrogation IdentificationA subrogation solution that reads new and closed claims for another party who should pay.
• Insurance Policy CheckingA policy checking solution for agencies that compares each issued policy to quote and binder.
• Policy Endorsement ProcessingA servicing solution that reads mid-term change requests and checks them against the policy.
• Bordereaux ManagementA bordereaux solution for carriers and MGAs that maps and checks each delegate's monthly file.
• Treaty Wording ComparisonA contract review solution that redlines each treaty against last year, the slip and market.
• AI Governance & Model InventoryAn AI governance solution that lists every model and vendor AI tool, its tests and sign-off.

Every insurance solution →

A checklist for any vendor

1. Does every extracted value and finding link to the exact page or clause it came from?A good answer is a live demo on your own documents where any field opens to its source passage, not a confidence score alone.
2. Where does a person decide and sign, and what is recorded when they do?Look for a named sign-off step on every consequential action (quote, decline, pay, deny, refer, settle) and an audit log that cannot be altered.
3. How will you measure accuracy on our files before and after go-live?The vendor should score against a sample your staff marked, report results by field and finding, and keep testing after launch to catch drift.
4. Where will our data live, and is it used to train your models?You should be able to choose SaaS, a dedicated cloud or on-premises, and get a written statement that your inputs and outputs are not used for training.
5. Which independent security attestations do you hold?Expect current reports such as SOC 2 Type II and ISO 27001 that your security team can review, plus regular penetration testing.
6. How does your system support our AI governance under the NAIC bulletin and state rules?A good vendor can supply what your model inventory, testing and vendor-oversight files need, and explain how bias testing is done where it applies.
7. Can it read our real documents: scanned loss runs, handwritten notes, carrier-specific forms?Ask to run your worst files, not their best samples, and see what happens when a document cannot be read.
8. How does it connect to our policy, claims and billing systems?It should work with the systems, mailboxes and portals you already run, without a rip-and-replace project.
9. What happens when we need a second and third process?Look for the same controls, sign-off and audit trail reused across processes, not a new product and a new security review each time.
10. Can we export our documents, extracted data and decisions if we leave?You should keep full access to your own records in usable formats, at any time.

Questions