Insurance
Most insurance teams use a copy of production data to test realistically, and every copy opens another leak surface. We generate synthetic data that carries the behaviour of production. Scenario variety increases, and personal data never enters the test environment.
Premium, coverage and claims calculation run on hundreds of rules layered over the years, and no team can test all of them by hand. We turn those rules into verifiable scenarios and order them by risk. The most critical calculation paths run automatically on every release.
In older policy systems the biggest obstacle is not technical debt but fear: if we touch this, what breaks? Automated regression makes that question measurable. Because the side effect of a change is visible immediately, modernisation does not stall.
The agency screen, the web and mobile are usually tested by separate teams with separate priorities, and a flow that works in one channel breaks in another. We bring all three under one test strategy and one reporting layer.
RabbitQA is a multi-agentic AI platform that manages the quality process from requirements validation through test data generation. Its most critical capability in insurance is synthetic data: it analyses the statistical distribution and business rule variety of production data and generates artificial data that behaves the same way. Age distribution, coverage combinations and claim frequency stay close to reality, and no record belongs to a real person. In the Quick Sigorta project, this method delivered zero data leakage.
AutoRunner is an AI-powered test automation product that plans, executes and reports test cases from one place. It does two things in insurance: it turns policy and claims rules into scenarios and runs them in order of risk, and it flags the affected scenarios automatically when a rule changes. In legacy systems the danger usually lies not where you made the change but in what depends on it. AutoRunner reports the broken flow together with its source.
Loadmance is a cloud-based, AI-powered performance testing product that measures how a system behaves under heavy traffic. In insurance the critical moments are known: renewal periods, campaign launches, events that create a surge in claims. Loadmance generates that load in advance and shows where the system slows, so the capacity decision rests on measurement rather than estimate.
Your digital channels are tested against WCAG 2.2 criteria. This meets the compliance obligation and prevents lost conversion at the same time: a form that cannot be accessed is a form that does not get filled in.
Yes. Testing is carried out with synthetic data that carries the statistical distribution and business rule variety of production. This widens scenario coverage and removes personal data from the test environment altogether. In the Quick Sigorta project, the method delivered zero data leakage.
Business rules are first converted into verifiable requirements, then into scenario suites. Risk-based prioritisation means the most critical calculation paths run automatically on every release. When a rule changes, the affected scenarios are flagged, so you do not have to review the entire suite.
Yes. The usual starting point is bringing the riskiest transaction flows into regression scope, which requires no change to the system. As coverage widens, modernisation steps become safer, because the side effect of every change becomes measurable.
Yes. Web, mobile and agency interfaces are managed under one test strategy, and results are collected in a shared reporting layer. A flow that works in one channel but breaks in another is caught before release.
What is being tested in insurance is above all calculation accuracy: premium, coverage scope and claim amount. Those calculations are layered with business rules accumulated over years, and errors are often noticed months later. That is why a rule-based approach with high data variety is required.
Complete the form; let’s discuss your policy issuance and claims processes, number of integrations, and peak renewal periods. Together, we’ll identify where the end-to-end flow still depends on manual validation.
