Claims AI: Questions Insurance Leaders Should Ask
Claims AI is software that assists or automates steps in intake, triage, investigation support, valuation suggestions, fraud flags, or customer updates inside the claims lifecycle. Before investing, insurance leaders should ask practical questions about data, workflow fit, oversight, integration, and rollout risk—not only about demo accuracy. This checklist helps IT, claims, and vendor management stay aligned. It does not rank vendors or invent industry benchmarks.
Start with the job, not the model
Answer: The first question is which claims step should improve, for which lines, and how you will know. “Add AI to claims” is not a scope. “Reduce rework on low-complexity auto glass FNOL triage while keeping human approval on edge cases” is a scope. Write the target step, the exception path, and the metric owners before you score vendors.
Eight questions that keep investments honest
1. What decision is the system allowed to make?
List actions that are assist-only versus actions that can execute without a person. Payment changes, coverage interpretations, and fraud locks need clearer authority than summarization. If the vendor cannot draw that line in writing, pause.
2. What data would we need to supply—and is it fit?
Ask for the minimum viable data set, known failure modes when fields are missing, and whether the model was tuned on your line mix. Garbage in still produces confident garbage out. Include unstructured notes and image quality realities, not only clean policy tables.
3. How does this sit inside our claims system of record?
Prefer architectures that write back through controlled APIs and preserve audit trails in the claims platform. Side databases that drift from the claim file create legal and operational risk. Demand a sequence diagram for create, update, and override events.
4. Who is accountable when the suggestion is wrong?
Name the human role that reviews exceptions and the SLAs for that queue. “The model is usually right” is not an operating model. Train adjusters on when to trust, when to challenge, and how to document disagreement.
5. Can we explain outcomes to customers, regulators, and reinsurers?
You need reviewable traces: inputs used, rules or model versions, and overrides. Exact techniques vary, but “black box with no replay” fails enterprise review. Ask how version pins work when you re-open an old claim.
6. What unfair outcomes will we monitor?
Define monitoring for segments where errors would be harmful or uneven. Build reporting before full rollout. Ethics work here is operational: measurement, escalation, and fix paths—not a poster.
7. What does the security and vendor package include?
Cover data residency, subprocessors, training-use restrictions on your claims data, penetration testing cadence, and exit assistance. Confirm whether prompts, documents, or model telemetry leave your control boundary.
8. How will we roll out without freezing the floor?
Pilot on a bounded claim type, measure rework and cycle time with a control group when feasible, and stage expand only after exception queues are staffed. A flashy pilot that floods adjusters with low-value alerts is a net loss.
How IT and claims leadership should share the review
Claims owns process truth and customer impact. IT owns identity, integration, logging, and vendor security. Compliance and legal should see the decision rights table early. A joint scorecard beats three separate slide decks that disagree in the steering meeting.
Illustrative example: a carrier might pilot AI-assisted document classification for a single personal-lines FNOL path, keep payment authority human, and expand only after override reasons are coded consistently for thirty days. That path teaches governance muscle without betting the entire claims org on day one.
Red flags in vendor conversations
- Accuracy claims with no definition of the task or the evaluation set.
- Pressure to connect production claims data before a controlled pilot design exists.
- Unwillingness to discuss override workflows and audit exports.
- Roadmaps that assume you will replace the core claims system to “unlock AI.”
Build a one-page investment brief
Before procurement deepens, compress the eight questions into a one-page brief: target claim step, decision rights, data dependencies, integration sketch, oversight owner, pilot design, and exit criteria. Leaders can disagree on the brief in one meeting instead of discovering misalignment after contracts are signed.
Refresh the brief when scope changes. A vendor that looked perfect for document intake may be the wrong partner for settlement recommendations. Keep the paper trail so future audits can see why you expanded—or why you stopped.
How Hexagon helps
Hexagon IT Solutions helps insurers and claims technology teams turn AI ideas into governed delivery: decision rights, integration design, and evaluation plans that survive scrutiny. If you are preparing a claims AI investment review, start with a systems audit or schedule a call with our Draper, Utah team.
FAQ
Do we need a data science team to buy claims AI?
Not always, but you need owners who can define tasks, evaluate errors, and maintain monitoring. Vendor models do not remove the need for internal accountability.
Is generative AI required for claims modernization?
No. Many high-value steps are classification, extraction, routing, or rules-assisted valuation. Choose the technique that fits the job.
How long should a pilot run?
Long enough to see exception patterns and seasonal noise in your chosen claim type—not only a two-week demo honeymoon. Set exit criteria in advance.
What should we measure besides speed?
Track rework, leakage indicators you already trust, customer complaint themes, override rates, and adjuster time spent fighting the tool.
