LLM-Driven Optimization of Insurance Policy Engine Testing: How AI Reshapes Insurance Core System Testing

This article focuses on the application of Large Language Models (LLM) in insurance policy engine testing, aiming to address pain points of traditional testing such as reliance on manual experience, incomplete boundary coverage, and high regression costs. By analyzing the technical paths and practical experiences of automated test generation, intelligent boundary case identification, and test coverage optimization, it provides feasible solutions for LLM-driven testing optimization in the insurance technology field.

The insurance policy engine is the neural center of core business systems, handling key processes such as policy rules and premium calculation. Traditional testing faces four major challenges:

1. Business complexity: Multi-domain rules (life insurance/property insurance/health insurance) and dependencies;
2. Boundary diversity: Difficult to manually cover variable combinations like age and sum insured;
3. Frequent rule changes: Product iterations and regulatory adjustments require a large number of regression tests;
4. Complex data preparation: High cost to simulate real business data. LLM's code understanding and logical reasoning capabilities provide new possibilities for testing optimization.

Technical Architecture

It includes three core modules:

• Test requirement understanding: Extract test points from requirement documents via Few-shot prompting;
• Test case generation: Analyze code/rule configurations to generate equivalence class and boundary value test cases;
• Test execution optimization: Intelligently sort cases to prioritize coverage of high-risk scenarios.

Key Innovations

1. Intelligent boundary identification: Discover critical states with overlapping multiple conditions that are missed by humans;
2. Fuzz testing generation: Simulate abnormal inputs to detect system robustness;
3. Test data synthesis: Generate compliant and desensitized business data.

It is recommended to implement in three phases:

1. Data preparation and model selection: Collect rule documents and historical cases, select models like GPT-4/Claude;
2. Prompt engineering optimization: Design insurance domain Prompt templates to guide the model in understanding professional logic;
3. Result verification and optimization: Manually review cases and provide continuous feedback to improve generation accuracy.

Application effects are significant:

• Test case design efficiency increased by over 60%;
• Boundary condition coverage improved by 40%;
• Regression testing time reduced by 30%. Value embodiment: Early detection of potential defects, reducing business losses and compliance risks; long-term reduction of testing costs and acceleration of business response.

Challenges

1. Interpretability: Difficult to trace the logic of generated cases;
2. Depth of domain knowledge: General models have insufficient understanding of insurance terminology;
3. Case maintenance: Large number of automatically generated cases;
4. Security and compliance: Sensitive data risks.

Countermeasures

• Require the model to output design reasons;
• Adopt RAG technology to access insurance knowledge bases;
• Establish case classification and lifecycle management;
• Ensure security through data desensitization + local deployment.

Future trends include:

1. Multimodal applications: Support complex inputs like charts/voice;
2. Autonomous agents: Automatically analyze system changes and dynamically adjust testing strategies;
3. DevOps integration: Embed into CI/CD pipelines to achieve fully automated testing.

LLM brings revolutionary changes to insurance policy engine testing, improving efficiency and quality while reducing risk costs. Although facing challenges such as interpretability and domain knowledge, technological progress and practical accumulation will make it a key support for digital transformation. Insurance IT teams should layout early to seize competitive advantages.