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New benchmark reveals LLM struggles with insurance claim adjudication

By PulseAugur Editorial · [1 sources] ·

A new benchmark called InsClaimBench has been developed to evaluate the performance of large language models in insurance claim adjudication. The benchmark, which includes 3,780 cases across auto, property, and health insurance, assesses models' ability to connect evidence, rules, judgments, and payout calculations. Evaluations of six LLMs showed a decline in reliability along the decision chain, with payout-decision accuracy ranging from 74.23% to 80.19%, and joint decision-amount accuracy dropping to 47.54% to 73.15%. The study highlights that while models may perform well on individual rules, they struggle with consistent propagation of information across the entire adjudication process. AI

IMPACT Highlights limitations of current LLMs in complex, multi-step decision-making tasks, indicating a need for improved reasoning and consistency.

RANK_REASON The item is a research paper introducing a new benchmark for evaluating LLMs on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New benchmark reveals LLM struggles with insurance claim adjudication

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The item is a research paper introducing a new benchmark for evaluating LLMs on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Linqi Zhang, Chong Qi, Yan Cheng, Wanqing Cao, Yu Liu, Chenwei Lin, Xian Xu ·

    InsClaimBench: Benchmarking Insurance Claim Adjudication Across the Decision Chain

    arXiv:2610.09671v1 Announce Type: new Abstract: Recent advances in reasoning-oriented large language models (LLMs) have motivated increasing evaluation of their ability to perform professional decision tasks. Insurance claim adjudication is one such task, requiring models to conn…

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