Claims that clear themselves, and models you can defend
Most carriers already know which claims should never have needed a human, which submissions should never have reached an underwriter, and which policy system is quietly setting the ceiling on all of it. The work is rarely a matter of buying something new. We build the automation, the document extraction and the integration layer that let the existing estate run faster — and we build the governance around every model that touches a price, because a regulator asking how a rate was set is now a routine event rather than a bad day.
What insurance teams are actually up against
The policy administration system is the real bottleneck
Most carriers are running a policy admin platform written in an earlier decade, with rules encoded in places nobody has read in years. Every product change queues behind it, and replacing it wholesale is a multi-year programme with a poor completion record across the industry. The realistic path is to work around it in stages while it keeps running.
Claims automation stalls at the exceptions
Straight-through processing on simple, well-documented claims is achievable and worth having. The trouble starts with the twenty per cent that are ambiguous, contested or missing evidence, which consume most of the handling cost. Automation that has no thought-through path back to a human adjuster makes those claims worse, not cheaper.
Evidence still arrives as documents
Loss reports, medical records, police reports, engineer surveys, broker submissions and photographs — unstructured, inconsistent, and arriving in every format anyone has ever used. Reading them is where handling time goes, and it is one of the few places where current AI is genuinely, measurably better than the alternative.
Algorithmic pricing is under scrutiny
Regulators in several markets now expect carriers to explain what goes into a rating or underwriting model, to test it for proxy discrimination and to show who signed it off. Models built without documentation, versioning or a reproducible training set are a supervisory problem waiting for a review to find them.
Fraud moves faster than the rules that catch it
Static rule sets catch yesterday's patterns and generate false positives that swallow investigator time. Networked and organised fraud in particular does not look wrong on any single claim — it looks wrong across claims, parties and repairers, which is a graph problem rather than a checklist.
Catastrophe exposure is outrunning the models
Wildfire, flood and severe convective storm losses have moved outside what historical experience predicts, and reinsurance pricing has moved with them. Portfolio steering, exposure aggregation and geospatial risk data are now operational questions for the whole business, not a quarterly exercise for the actuarial team.
How we answer those problems
Claims automation and straight-through processing
Automated intake, validation and triage, with rules for what settles automatically, what routes to which adjuster and what stops immediately. Every decision path is logged and reversible, and the exception route is designed first — it is the part that decides whether the automation survives contact with real claims.
Document and evidence extraction
Structured data pulled out of loss reports, medical records, surveys, invoices and broker submissions, with confidence scores and a review queue for anything the model is unsure of. This is the strongest current AI use case in insurance and the one that pays back fastest, usually inside two quarters.
Underwriting workbench and model governance
Submission triage, appetite matching, automated data enrichment and a decision record for every referral. Models arrive with documentation, versioned training data, monitoring for drift and a reproducible audit trail, so an internal model risk review or an external examination has something to read.
Fraud detection across claims, not one at a time
Network analysis over parties, addresses, repairers and payment destinations alongside per-claim scoring, tuned against your own confirmed fraud history. We tune to investigator capacity rather than to raw detection rate, because alerts nobody works are not detection.
Policy administration integration and modernisation
An API layer over the legacy platform so new products, portals and partner channels stop waiting for it, then incremental migration of functions off it using the strangler pattern. Guidewire, Duck Creek, Sapiens and in-house mainframe estates are all familiar territory.
Customer and broker self-service
Policy documents, claim submission and status, payments and renewals in web and native mobile, built for the availability those journeys need. Every self-service transaction that works is a call that never arrives, which is where the cost case actually lives.
Regulatory context
These regimes shape what can be built and how data moves. We design to them from the first architecture conversation, rather than retrofitting controls once something is already live.
Services we bring to insurance
Software built around how your business actually works — not bent to fit somebody else's product roadmap.
Models that forecast, rank, score and flag — built on your data, monitored for drift, and cheap enough to run on every transaction.
A warehouse, tested transformations and one agreed definition of every metric — so the number in the meeting stops being an argument.
Trained remote teams for collections, customer service, admin and technical support — operational in days rather than months, at a meaningful discount to hiring the same capability locally.
Moving workloads to AWS, Azure or Google Cloud in waves you can stop — with the running cost worked out before you commit, not discovered on the third invoice.
Insurance questions, answered
Last updated: August 31, 2026
Yes, and usually alongside it rather than inside it. Configuration and upgrade work in a packaged policy or claims platform is best done by people certified on that product, and we will say so. What we build is the layer around it — integrations, document processing, portals, analytics and the services that need to move faster than the core release cycle allows. The same approach applies to Sapiens, Majesco and in-house mainframe systems.
Bring us the claim type that costs the most to handle
We will look at the volume, the documents, the exception rate and the systems it touches, and tell you what can be automated now, what needs the policy platform to move first, and what is not worth the effort.
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