FNOL and claims triage support
Structuring the notification, checking cover against the policy wording and assembling the case, so a handler starts from a prepared file - with the coverage decision left to them.
Industries
Insurance AI where the customer outcome is the test
Claims and underwriting run on documents arriving in every format imaginable, and most of the cost sits in reading them. That is genuine AI territory - provided the decisions that affect a customer stay with a person who can be asked to justify them.
The pressure right now
What we hear from insurance
- Claims volumes and cycle-time expectations rising while experienced handlers retire
- Submissions arriving as email, spreadsheet, PDF and scan, and being rekeyed by hand
- Consumer Duty requiring evidence of fair value and good outcomes across the book
- Fraud referral quality varying by handler, with inconsistent evidence
- Complaints and root-cause analysis absorbing senior time
Where it pays off
Six places AI earns its keep here
Not everything on this list will apply to you. Most organisations start with one and extend once it has been measured.
Submission and document ingestion
Extracting risk data from broker submissions in whatever format they arrive, with the source shown next to every extracted field for verification.
Claims correspondence
Consistent, plain-English customer letters drafted from the case record, in your approved wording, reviewed before sending.
Fraud referral evidence
Assembling a consistent evidence pack for the counter-fraud team so referrals arrive complete - the decision to refer, and to decline, stays human.
Complaints and root cause
Summarising cases, classifying causes consistently and surfacing the patterns that a manual sample would take a quarter to find.
Portfolio and QA review
Reviewing a far larger share of claims and policies against your quality criteria, routing only the exceptions to reviewers.
Where we would start
The first three moves
1
Take the document bottleneck
Ingestion and case assembly deliver the clearest, least contentious gain, and they do not touch a customer decision.
2
Instrument fairness from the start
Before rollout, test outcomes across customer groups. Retrofitting that analysis after a complaint is a much worse position to be in.
3
Keep the decision points visible
Map exactly where a human decides, and design the system to make that decision faster and better evidenced.
Risk and regulation
The part most suppliers skip
Where the risk sits
- Consumer Duty: fair value and good outcomes must be evidenced, especially for vulnerable customers
- Pricing and underwriting decisions influenced by AI attract fairness and discrimination scrutiny
- Claims declinature or reduction based on an unexplained model output
- Vulnerability indicators missed by automated routing
- Delegated authority and broker data flowing into third-party AI processing
How we handle it
Declinature, reduction and pricing outcomes are never automated. The system prepares and evidences; a person decides and can explain the decision in the customer's own terms.
Vulnerability signals route to a human immediately and are tested as an explicit scenario before go-live, not treated as an edge case.
Outcome testing across customer groups is part of delivery, with the results written down - so the fairness question has an answer before anyone asks it.
Where this fits
Where to go next
Questions
Questions from insurance
Technically yes; we do not recommend it and we do not build it without an explicit human authority step. The reputational and regulatory downside of a wrong automated declinature dwarfs the handling-cost saving.
As third-party data with its own contractual constraints. The data flow map and processing terms are part of the design, and where the agreement does not permit external processing, the deployment is private.
The operational workflows are much the same; the accountability chain is not. Delegated authority arrangements mean your capacity provider will have views on AI in the process, so we cover that in the governance work.
Start with an audit of what you already run
Two to four weeks to an evidenced picture of your AI use, spend and risk - and a ranked list of what to do first.