Abstract

T he advent of synthetic data generation is redefining the testing ecosystem in the Life Insurance sector by mitigating key challenges such as data privacy vulnerabilities, suboptimal test coverage, and scalability constraints. This paper provides a granular analysis of synthetic data, positioning it as a next generation solution through a comparative evaluation with conventional data constructs, including production data, anonymized datasets, and process simulated data. An exhaustive comparison matrix underscores the unique value proposition of synthetic data in driving end-to-end test coverage while ensuring alignment with stringent regulatory frameworks and data governance protocols. Furthermore, the paper explores cutting-edge methodologies, toolchains, operational applications, and associated challenges, while charting a forward-looking perspective on its transformative impact on quality engineering and assurance frameworks in Life Insurance.