Product Validation Accelerator Engine - Combining AI with Software Testing Techniques for Configurable Insurance Systems

Abstract

The validation of insurance product configurations is a complex and time-consuming process. Highly configurable insurance systems generate combinatorial spaces that grow exponentially with the number of available options, and the interdependencies between business rules, calculation formulas, and variation conditions make systematic coverage intractable through manual means. This dissertation proposes an approach to accelerate the validation of these configurations by combining Artificial Intelligence with traditional software testing techniques. In the proposed architecture, AI does not act as a generic orchestrator, but rather as the component capable of understanding the subtleties inherent to the domain: interpreting premium calculation formulas, analyzing composition rules and their variations, and identifying boundary conditions and critical scenarios that a purely deterministic pipeline cannot infer. This semantic knowledge is then combined with deterministic methods, including combinatorial generation, Boundary Value Analysis, and Equivalence Class Partitioning, to produce test scenarios that are valid, efficient, and relevant to the business. The proposed solution, named Product Validation Accelerator Engine, has significantly improved the validation time of product configurations by automating, or semi-automating, the three most demanding processes in the workflow: the creation of test artifacts, targeted verifications through unit tests, and mass validation through massive tests. The entire solution is adaptable to the configuration inherent to insurance systems and to the complexity they present.