AI in Insurance
Submission intake, claims triage and policy analysis on your own infrastructure.
Insurance runs on documents that arrive in no fixed format: submissions, loss runs, policy forms, medical records and adjuster notes. Most carriers and brokers have already automated the structured parts and stalled on the unstructured ones. That is precisely where language models are strongest, and precisely where regulators care most about explainability.
Book a free consultationWhat makes this hard
- Submissions arrive as email attachments in dozens of formats, and rekeying them is the bottleneck in the quoting process
- Claims triage quality varies by adjuster, and the variance is expensive in both leakage and litigation
- State insurance regulators increasingly require explanation of any model affecting a rate or a claim decision
- Loss run and policy comparison is slow manual work that directly delays quoting
Where AI earns its place
Submission Intake
Extract risk data from submissions, ACORD forms and loss runs into the rating system regardless of the format they arrive in.
Quote turnaround measured in hoursClaims Triage and Summarisation
Summarise a claim file into a consistent structure and flag severity and complexity indicators, so the routing decision stops depending on who opened it.
Consistent triage across adjustersPolicy and Coverage Comparison
Compare forms and endorsements across carriers and surface the substantive coverage differences rather than the wording differences.
Coverage gaps surfaced before bindingFraud Signal Surfacing
Surface patterns for investigator review, presented as evidence to examine rather than as a score that decides anything.
Investigator time on genuine signalsFigures are drawn from Senteras engagements and are illustrative of typical results. Outcomes vary by data quality, infrastructure and scope.
The rules that shape the build
These are the constraints that decide the architecture, usually before anyone has picked a model. This is general information about the regulatory landscape, not legal advice on your obligations.
NAIC Model Bulletin on AI use
Adopted in most states. It expects a documented AI governance programme covering testing, validation and vendor oversight, and it applies whether or not the AI is customer-facing.
State unfair discrimination statutes
A model that produces disparate outcomes across protected classes is a violation regardless of intent, and proxy variables are still a problem even when protected attributes are excluded.
HIPAA (health and disability lines)
Medical records in a claim file carry the same protections as in a clinical setting.
Nothing that touches a rate decides anything
Senteras builds insurance AI on a hard split: models handle extraction, summarisation and surfacing, and never make or score a rating or claims decision. That is not caution for its own sake. State regulators are actively examining algorithmic decisioning, and a system architected so the model is demonstrably outside the decision path is far easier to defend in a market conduct exam than one where it sits inside with a human rubber-stamping it.
Where this applies
The same core systems, with the differences that matter in each setting.
- Carriers
- Claims triage and submission intake at volume; governance obligations under the NAIC bulletin fall on you directly.
- Brokers and agencies
- Submission preparation and policy comparison, where speed to quote wins the account.
- MGAs and programme managers
- Binding authority makes underwriting consistency an existential issue rather than an efficiency one.
- Third-party administrators
- Claims summarisation across multiple client carriers, with tenant isolation as the hard requirement.
Common questions
Does the NAIC model bulletin apply to us?
Most states have adopted it, and it applies to AI use generally rather than only to customer-facing systems. It expects a documented governance programme covering testing, validation and vendor oversight, which is easier to satisfy when the model is inside your own environment.
Can a model decline a claim or set a rate?
Not in our builds. We keep models out of rating and claims decisions and use them for extraction, summarisation and surfacing. With regulators actively examining algorithmic decisioning, a system demonstrably outside the decision path is far easier to defend than one inside it with a human rubber-stamping.
What is the fastest thing to deploy?
Submission intake for carriers and brokers. It is high volume, the input formats are uncontrolled, and quote turnaround is directly tied to win rate, so the value is measurable within weeks.
How do you avoid unfair discrimination exposure?
Testing for disparate outcomes across protected classes, including proxies, is part of the build rather than a review at the end. Excluding protected attributes alone does not address it.
How we build it
Local & On-Prem LLM Deployment
The most powerful AI models, running entirely on your hardware.
Custom AI Agents & Automation
AI that doesn't just answer questions. It gets things done.
Model Fine-Tuning & Integration
Models that speak your industry's language, trained on your data.
Document Processing
Extraction, classification and validation across formats you do not control.
Contract Review
Flags deviations from your playbook, not from a generic standard.
Fraud Detection
Fewer false positives, and a reason attached to every flag.
Find out what this looks like for your organisation
A 30-minute call. We will tell you plainly whether AI is the right tool for the problem you have, including when it is not.
Book a free consultation