An Industrial-Scale Insurance LLM Achieving Verifiable Domain Mastery and Hallucination Control without Competence Trade-offs

1. Problem Statement

Adapting Large Language Models (LLMs) to the insurance sector presents unique challenges that generic or even general financial models fail to address adequately:

• Competency Trade-off: Existing vertical models often sacrifice general intelligence to gain domain expertise, leading to "catastrophic forgetting" where the model loses its ability to reason generally.
• Hallucination Risks: Insurance decisions require strict adherence to regulations and auditable reasoning paths. Current models often hallucinate policy terms or fabricate cases, creating severe legal and reputational liabilities.
• Shallow Adaptation: Most solutions rely on superficial Retrieval-Augmented Generation (RAG) or naive fine-tuning, failing to internalize the complex, multi-step reasoning required for actuarial pricing, underwriting, and claims adjudication.
• Lack of Standardized Evaluation: There is no comprehensive benchmark to evaluate insurance-specific capabilities, hindering iterative optimization.

2. Methodology

The authors propose INS-S1, a family of insurance-specific LLMs trained via a novel end-to-end alignment paradigm. The methodology consists of three core pillars:

A. Verifiable Data Synthesis System

Instead of simple knowledge injection, the authors constructed a hierarchical dataset synthesis pipeline:

• Knowledge Injection (No-Query): Processed multi-modal corpora (textbooks, regulations) using "Instruction Isolation" with Empty Think Tags (<thought></thought>) to prevent over-reliance on reasoning for rote memorization, improving theory benchmark accuracy by 3.11%.
• Cognitive Alignment: Introduced "Business-Trap Distractors" (adversarial options with subtle semantic/numerical changes) to enforce precise discrimination. Answer generation follows SOP-CoT (Standard Operating Procedure Chain of Thought), enforcing a verification pipeline: Entity → Attribute → Compliance.
• Complex Reasoning (With-Query):
  • Actuarial Math: Replaced noisy free-form CoT with Standardized Reasoning (linear, step-by-step derivations similar to Program-of-Thought) and an Answer Correction Loop (Generate-Verify-Reflect-Rewrite).
  • Underwriting & Claims: Utilized a Prompt Correction Loop (inspired by APE) to iteratively refine prompts based on validation errors and enforced structured logic paths (Info Extraction → Risk ID → Rule Detection → Conclusion).
• Hallucination Mitigation: Decomposed RAG into four atomic tasks (Knowledge Boundary ID, Selection, Summarization, Self-Check) and used a phased approach to migrate models to the domain using low-hallucination QA and refusal samples.

B. Progressive SFT-RL Curriculum Framework

The training follows a two-stage curriculum learning approach to balance stability and plasticity:

• SFT Stage (Breadth to Depth):
  • Stage 1 (Foundation): Mixes self-distilled general data with domain data to prevent catastrophic forgetting.
  • Stage 2 (Domain-Adaptive Annealing): Introduces Dynamic Data Annealing, up-sampling complex reasoning and hallucination mitigation tasks while reducing general data. This shifts focus from general patterns to conquering core domain challenges.
• RL Stage (Dual Alignment):
  • RLVR (Verified Reasoning): Uses Process Supervision for tasks with ground truth (e.g., actuarial formulas), employing rule-based matching and semantic equivalence checks.
  • RLAIF (AI Feedback): Uses a strong model (Qwen3-30B) to evaluate open-ended tasks on Factuality, Professionalism, and Expression.
  • Algorithm Selection: Uses GRPO for dense models (32B) and GSPO for MoE models (235B) to optimize routing stability.
  • Reward Shaping: Implements a "Principle of Parsimony" with length rewards to penalize verbose, valueless CoT and negative constraints to curb reward hacking (e.g., verbatim copying).

C. INSEva Benchmark

The authors released INSEva, the most comprehensive insurance benchmark to date:

• Scale: 39,166 samples across 9 core business dimensions (e.g., Actuarial Science, Underwriting, Compliance) and 50+ task types.
• Taxonomy: Stratified by Business Area, Format (MC, QA, Dialogue), Difficulty, and Cognition (Remembering to Evaluating).
• Evaluation: Uses an automated "LLM-as-a-Judge" pipeline for open-ended generation and standard accuracy for deterministic tasks.

3. Key Results

Extensive experiments demonstrate that INS-S1 achieves State-of-the-Art (SOTA) performance without compromising general capabilities:

• Domain Mastery:

  • INS-S1-235B scored 90.14 on INSEva, significantly outperforming DeepSeek-R1 (82.47) and Gemini-2.5-Pro (84.23).
  • INS-S1-32B (88.42) surpassed the larger Qwen3-235B (83.53) and DeepSeek-R1, proving high-quality instruction sets can overcome "competence dilution."
  • It led in 7 out of 8 sub-dimensions, with massive gains in Actuarial Reasoning (ILR: 83.61 vs. 54.25 for Qwen3-80B) and Safety/Compliance (ISC: 91.39).

• Hallucination Control:

  • Achieved a record-low 0.6% hallucination rate on the HHEM benchmark (compared to 2.8% for the baseline Qwen3-32B), while maintaining high factuality in real-world scenarios.

• General Capability Preservation (No Trade-off):

  • Contrary to the "zero-sum" trade-off, INS-S1 retained top-tier general reasoning.
  • INS-S1-32B outperformed its backbone (Qwen3-32B) on general benchmarks (80.08 vs. 79.22 average).
  • Strong performance on AIME25 (Math) and Codeforces (Coding), indicating positive transfer where rigorous insurance logic enhances general mathematical and logical reasoning.

4. Significance and Contributions

• Solving the Trade-off: The paper demonstrates that rigorous domain specialization does not require sacrificing general intelligence. Through Self-Distillation and Curriculum Learning, the model internalizes domain logic without forgetting general reasoning.
• Verifiable Reasoning: By moving beyond "black box" generation to SOP-CoT and Process Supervision, the model provides auditable reasoning paths essential for regulated industries.
• Industrial Scalability: The framework is validated on a 235B parameter model, proving that these techniques scale effectively for industrial deployment.
• Resource for the Community: The release of INSEva provides a standardized, rigorous evaluation tool that was previously missing, enabling future research in insurance AI.
• Real-World Impact: In double-blind side-by-side evaluations with real business queries, INS-S1-235B achieved a 59.27% win-tie rate against business-fine-tuned general baselines, with significant improvements in professionalism and factuality.

In conclusion, INS-S1 establishes a new paradigm for vertical domain LLMs, proving that with verifiable data synthesis and progressive curriculum alignment, models can achieve deep domain mastery and near-zero hallucination rates while retaining their general intelligence.