Claim Automation using Large Language Model

TL;DR

This paper demonstrates that fine-tuning large language models with domain-specific data significantly improves their ability to generate accurate, structured corrective actions for insurance claims, outperforming general-purpose models.

Contribution

It introduces a governance-aware, locally deployed LLM component fine-tuned with LoRA for insurance claim processing, showing improved accuracy and practical utility.

Findings

Approximately 80% of cases matched ground-truth actions.

Domain-specific fine-tuning outperformed general-purpose LLMs.

Model output closely aligned with real-world data.

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.