Claim Automation using Large Language Model

• URL: http://arxiv.org/abs/2602.16836v1
• Date: Wed, 18 Feb 2026 20:01:12 GMT
• Title: Claim Automation using Large Language Model
• Authors: Zhengda Mo, Zhiyu Quan, Eli O'Donohue, Kaiwen Zhong,
• Abstract summary: Large Language Models (LLMs) have achieved strong performance on general-purpose language tasks, but their deployment in regulated and data-sensitive domains, including insurance, remains limited.<n>We propose a locally deployed governance-aware language modeling component that generates structured corrective-action recommendations from unstructured claim narratives.<n>We fine-tune pretrained LLMs using Low-Rank Adaptation (LoRA), scoping the model to an initial decision module within the claim processing pipeline to speed up claim adjusters' decisions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While Large Language Models (LLMs) have achieved strong performance on general-purpose language tasks, their deployment in regulated and data-sensitive domains, including insurance, remains limited. Leveraging millions of historical warranty claims, we propose a locally deployed governance-aware language modeling component that generates structured corrective-action recommendations from unstructured claim narratives. We fine-tune pretrained LLMs using Low-Rank Adaptation (LoRA), scoping the model to an initial decision module within the claim processing pipeline to speed up claim adjusters' decisions. We assess this module using a multi-dimensional evaluation framework that combines automated semantic similarity metrics with human evaluation, enabling a rigorous examination of both practical utility and predictive accuracy. Our results show that domain-specific fine-tuning substantially outperforms commercial general-purpose and prompt-based LLMs, with approximately 80% of the evaluated cases achieving near-identical matches to ground-truth corrective actions. Overall, this study provides both theoretical and empirical evidence to prove that domain-adaptive fine-tuning can align model output distributions more closely with real-world operational data, demonstrating its promise as a reliable and governable building block for insurance applications.

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