Software built and proven for insurance platforms where a rating error compounds across every policy.
Quoting and rating engines, underwriting workflows, claims processing and policy administration, built and tested for accuracy that survives an actuarial review and a regulator.
What building for Insurance actually requires.
Insurance software is judged on arithmetic before experience. A rating engine that is subtly wrong does not throw an error; it quietly misprices every policy it touches until someone reconciles the book. That makes calculation correctness, not interface polish, the first quality objective.
The domain is rule-dense and the rules change. Rate tables, state or territory variations, endorsements and regulatory filings all shift on their own schedule, so the architecture has to treat rules as versioned data rather than as code that gets edited in place.
Every figure eventually needs explaining. A policyholder disputes a premium, a regulator samples a decision, an auditor traces a claim payment. If the system cannot reconstruct why it produced a number on a given date, the answer becomes a manual investigation.
Where it breaks without care.
Rating logic is tested with a handful of happy-path quotes rather than against a full matrix of ages, territories, coverages and endorsements, so an error surfaces only when a book of business is reconciled.
Rate versions are deployed as code changes, so reproducing what the engine would have quoted six months ago becomes impossible exactly when a dispute requires it.
Claims workflows are tested forward but not backward: reversals, partial payments, reopened claims and recoveries are where the state machine actually breaks.
Third-party data calls, credit, motor history, property risk, are mocked in testing and never exercised against timeout and partial-response behaviour, which is how a quote flow hangs in production.
How we deliver it.
Rating and premium calculations verified against a generated matrix covering the full parameter space, not a sample, with expected values agreed with your actuarial team before the build.
Rate tables and rules versioned as data with effective dates, so any historical quote or decision can be reproduced exactly on demand.
Claims state machines tested against reversal, reopening and partial-settlement paths, since those are the transitions that corrupt balances.
External data providers exercised under timeout, partial response and outage, with defined degradation rather than a hung quote screen.
An audit trail designed at the architecture stage that links every premium, decision and payment to the inputs, rule version and person or system that produced it.
Industry focus areas.
Quoting & rating engines
Underwriting workflow
Claims processing
Policy administration
Broker & agent portals
Regulatory reporting
Evaluating a build partner?
A two-week scoping engagement covering architecture review, risk mapping specific to insurance & insurtech, and a 90-day delivery plan across build and quality engineering. You keep the plan whether or not you continue with us.
Insurance questions, answered.
What does QA for an insurance platform focus on?
Calculation correctness first: rating, premium, commission and claim amounts verified across the full parameter space. Then state integrity in claims and policy lifecycle, then the audit trail that lets you explain any figure later.
How do you test a rating engine properly?
By generating the full matrix of rating parameters rather than sampling a few quotes, and comparing against expected values agreed with your actuarial team. Spot-checking a rating engine reliably misses systematic errors.
Can you work with our existing policy administration system?
Yes. Most engagements integrate with an incumbent PAS or core system rather than replacing it, which makes contract testing at those boundaries the highest-value coverage we can add.
Built for Insurance,proven before launch.
Senior engineers who know where this sector breaks, and the quality engineering to prove your build does not.