Where AI pays off first for carriers
| Workflow | What AI does | What stays with people |
|---|---|---|
| FNOL and claims intake | Reads emails, forms, photos and documents; extracts loss details; opens or updates the claim | Coverage decisions and customer-sensitive conversations |
| Claims triage and routing | Scores complexity and severity, flags potential fraud signals and routes to the right adjuster or fast-track path | Investigation, reserving and settlement authority |
| Underwriting submission intake | Extracts data from broker submissions, schedules and loss runs; checks appetite and missing information | Risk selection, pricing and final terms |
| Document intelligence | Summarises medical records, repair estimates, police reports and legal filings with citations | Interpretation in disputed or litigated claims |
| Policy servicing | Handles endorsements, certificate requests and billing questions across email and chat, and escalates exceptions | Non-standard changes and complaints |
| Subrogation and recovery | Identifies claims with recovery potential from the claim file and routes them for review | Pursuit decisions |
Illustrative workflow: commercial property FNOL to adjuster assignment
- 01
Intake
A broker emails a loss notice with photos, a contractor estimate and the insured's statement. An intake agent classifies the email, extracts the policy number, date and cause of loss, and pulls the policy from the core system.
- 02
Coverage context
The agent checks the policy is in force for the loss date, lists relevant coverages, limits and deductibles, and flags anything that needs a coverage review. It doesn't make the coverage decision.
- 03
Claim creation
Validated data is written to the claims system through its API under a dedicated integration identity. Missing information triggers a drafted request to the broker for a person to approve.
- 04
Triage
A triage model scores severity and complexity and highlights fraud indicators with reasons, such as inconsistent dates or prior losses at the same location, for the special investigations unit (SIU) to review.
- 05
Assignment
The claim is routed to the right desk or fast-track queue with a one-page summary and links to the source documents, so the adjuster starts with the file already organised.
- 06
Audit
Every step is logged with inputs, model version, outputs and reasons, keyed to the claim number for audit and regulatory review.
In a real engagement we baseline intake time, assignment time and rework before go-live and report against them after launch, rather than projecting generic industry figures.
Core system integration
AI output has to appear where adjusters and underwriters already work. We integrate with Guidewire ClaimCenter and PolicyCenter, Duck Creek, Majesco and in-house policy and claims administration systems through their APIs and integration frameworks, and with industry data sources such as ISO ClaimSearch and LexisNexis where carriers license them.
Explainability, fairness and audit
- Every model score shows the factors behind it, so adjusters and underwriters can see why a recommendation was made.
- Bias and fairness testing on protected characteristics and proxies before and after launch.
- Documentation to support model governance reviews, rate filings and state insurance department enquiries.
- Human decision rights on coverage, reserves, pricing and denials, with overrides recorded.
- Model monitoring for drift, with retraining triggered by performance changes, for example after catastrophe events.
How an engagement runs
- 01
Line-of-business assessment (2 weeks)
Map the target workflow, its volumes, handling times and rework, and assess data quality and system access.
- 02
Prototype on your data (3–5 weeks)
Build the workflow against historical claims or submissions and review outputs with your adjusters or underwriters.
- 03
Production (around 60 days)
Integrate with the core system, run in shadow mode, then go live with approval gates and monitoring.
You own the code and models. Work is scoped as fixed-price milestones before it begins.
Frequently asked questions
What does an AI consultancy do for an insurance carrier?
It identifies the claims, underwriting and servicing workflows where AI will pay off, then designs, builds and integrates the systems into the carrier's core platforms with the governance regulators expect. Solnix delivers working software in production, not only a strategy or a vendor selection.
Which insurance workflow should a carrier start with?
Usually FNOL and claims intake or underwriting submission intake. Both are document-heavy, have clear baselines such as handling time and rework, and keep final decisions with people, which makes them lower risk to automate first.
Do you work with Guidewire and Duck Creek?
Yes. We integrate with Guidewire, Duck Creek, Majesco and in-house systems through their APIs and integration frameworks, so outputs appear inside existing adjuster and underwriter workbenches.
Will AI make claim or underwriting decisions on its own?
Not for coverage, reserves, pricing or denials. AI prepares the file, recommends and explains; licensed staff decide. Routine, low-risk steps such as data entry and routing can be automated within limits you set.
How do you handle explainability for regulators?
Every model output records the factors behind it, and we test for bias before and after launch. We document models to support governance reviews, rate filings and state insurance department enquiries.
How long until a carrier sees something working?
A prototype on your own data typically takes 3 to 5 weeks, and a first production workflow around 60 days, depending on system access and approvals.