Client Context
A leading health insurance provider launched a digital transformation to automate its client onboarding journey. This involved validating microservices spanning policy data management (HAM), orchestration (HAO), and third-party integrations for identity and risk profiling. Given the complex architecture and asynchronous workflows, the client required a scalable framework to ensure consistent, end-to-end testing across APIs, databases, and event pipelines with minimal manual effort.
Challenges
The engagement posed several key challenges that needed to be addressed for successful delivery:
Lack of an existing automation framework, with heavy reliance on manual validation
Complex request and response payloads with deeply nested structures and high-volume enums
Asynchronous messaging architecture requiring validation of Kafka topic events
Distributed data model with over 30 interlinked PostgreSQL tables
Integration with LexisNexis, demanding validation of identity enrichment and external decisioning
Need to support dual validation across legacy and modernized components during transition phases
Solution Delivered
A domain-aligned automation framework was implemented to provide comprehensive validation across critical insurance workflows. The framework was designed to support end-to-end testing with a focus on modularity, scalability, and reusability.
- Automated validation of Create, Submit, and Retrieve health application processes
- Established database connectivity and entity mapping using Entity Framework Core with PostgreSQL
- Integrated Kafka event validation for asynchronous workflows triggered post-submission
- Dynamic data handling strategy enabling broad test coverage with minimal duplication
- Scenario-driven structure to support varied input combinations and edge cases
- Seamless alignment with ongoing development cycles for continuous quality assurance
Value Delivered
Driving Quality at Speed Through Test Automation in Insurance
- Over 1000 automated scenarios covering core onboarding flows
- 91% average test success rate across multiple releases
- 60% reduction in testing cycle time per release
- Early identification of 200+ critical defects during testing phases
- 250+ permutations of application scenarios validated with dynamic data combinations
- Improved accuracy, speed, and consistency across testing lifecycles
Impact Highlights
The solution brought about measurable improvements in delivery efficiency, collaboration, and test coverage:
Established a standardized, reusable QA framework adopted across squads
Enabled end-to-end testing of both synchronous API flows and asynchronous Kafka-driven processes
Improved accuracy and traceability of test results across application layers
Enhanced collaboration between QA, development, and DevOps teams
Set the foundation for automation expansion into additional microservices within the onboarding domain
