Insurance
Agentic AI for insurance — claims triage, fraud detection and underwriting support built so that a regulator and a board can both sign off on the same system.
What makes insurance hard for AI
Insurance decisions are consequential, contested, and reviewed after the fact. That raises the bar on evidence well above the industry norm.
Every decision may be challenged
A declined claim can be disputed by the customer, examined by an ombudsman and tested by a supervisor. The system must reconstruct not only what it decided but what it relied on.
Fairness is measured across groups, not in aggregate
Aggregate accuracy conceals disparate outcomes. Evaluation has to be segmented, and the segmentation has to be defensible.
Legacy policy administration constrains integration
Policy data is frequently spread across systems of different vintages with inconsistent product hierarchies. Reconciling that is usually the first real project.
Where the value concentrates
- Claims triage. Routing by complexity and severity, straight-through processing for the simple majority, with reasoning retained for the rest.
- Fraud detection. Pattern recognition across claim, policy and third-party data with explainable indicators rather than an opaque score.
- Underwriting support. Document extraction and risk summarisation that shortens quote turnaround without removing the underwriter.
- Customer correspondence. Drafting and summarising, reviewed before issue.
What the economics look like
Claims handling cost and cycle time are the headline measures, but leakage — payments that should not have been made — is usually where the larger number sits. Triage that surfaces the right cases for scrutiny tends to pay for the programme on its own.
How we build it
We design for the review that comes later: explainable decisions, audit trails, and human oversight routed by consequence rather than applied uniformly.
Oversight as policy
Value thresholds, confidence bands, novel patterns and regulated categories each route differently. Everything else proceeds automatically with sampling — which is what keeps the model honest as volume grows.
Related work
Starting smaller
Smaller carriers and MGAs usually begin with document extraction from claim submissions — narrow, high-volume, and immediately measurable — before extending into triage.
Questions we get from carriers
Will a regulator accept an AI-assisted claims decision?
Supervisors are generally concerned with governance rather than technology: can you evidence how the decision was reached, show that oversight is real, and demonstrate that outcomes have been tested for fairness across groups. Systems designed with those three properties tend to pass; systems that add them afterwards tend not to.
How do we avoid discriminatory outcomes?
Evaluate by segment, not in aggregate. Aggregate accuracy routinely hides disparate performance across groups. Agree the segmentation with your compliance function before launch so the measure is not disputed later.
Does this replace claims handlers?
In practice it changes what they handle. Straight-through processing absorbs the simple majority; handlers concentrate on complex, contested and high-value cases where judgement genuinely matters. Volume per handler rises, and so does the difficulty of the average case.
What about legacy policy administration systems?
They stay authoritative. Agents read and act through governed interfaces rather than requiring replacement — which is what makes the timeline measured in months rather than years.