LLM driven Test optimization for Insurance Management Policy Engines

The swift development of Large Language Models (LLMs) is changing software engineering models, especially those that need to process many rules, as insurance policy management systems do. The current paper proposes an original LLM-based test optimization model designed to fit the Insurance Management Policy Engines, which can resolve the long-standing issues of high quality, effective and scalable testing. The insurance systems that use traditional test strategies tend to encounter problems of an exponential increase in the number of the combinations of policy rules which result in overlapping test cases and poor risk coverage. We introduce an AI-assisted style of testing, based on the idea of using generative LLMs to reason about policy requirements, test artifacts, and pattern-based system behaviour in order to use the reasoning to formulate an optimized test suite. Through the application of risk-based testing concepts and adaptive prioritization, the framework is dynamic in the way it finds high impact test scenarios, minimizes test redundancy, and improves automation in regression cycles. Our solution works with the current test automation systems, and allows flawless coordination of test generation, execution and assessment. The experimental outcomes of industry-grade insurance engines show that the detection rates of defects have become much better, the testing effort has decreased and the release cycles have been significantly faster. This study gives prominence to the potential of LLMs to revolutionize the quality engineering process in mission-critical insurance platforms, which creates the AI testing paradigm balancing comprehensive coverage with operational effectiveness.