AI聊天机器人帮保险经纪人快速找方案，省时又准确。
Introducing Axlerod: An LLM-based Chatbot for Assisting Independent Insurance Agents
- 用自然语言处理+知识库检索，理解经纪人提问意图
- 政策查找准确率达93.18%，平均搜索时间缩短2.42秒
- 专为经纪人设计，适合企业级保险科技场景
保险行业正通过人工智能技术迎来范式转变，尤其在智能对话系统领域。聊天机器人已发展为能自动化复杂流程（如保单推荐、理赔分诊）并实现动态上下文交互的高级AI系统。本文介绍了一款名为Axlerod的AI驱动对话界面的设计、实现与实证评估，旨在提升独立保险经纪人的运营效率。该系统融合自然语言处理（NLP）、检索增强生成（RAG）与领域知识集成，具备解析用户意图、访问结构化保单数据库并实时提供上下文相关回复的能力。实验结果表明，Axlerod在保单检索任务中整体准确率达到93.18%，同时将平均搜索时间减少2.42秒。本研究推动了企业级AI在保险科技中的应用，特别聚焦于代理人辅助而非面向消费者的架构。
原文摘要 · Abstract (English)
The insurance industry is undergoing a paradigm shift through the adoption of artificial intelligence (AI) technologies, particularly in the realm of intelligent conversational agents. Chatbots have evolved into sophisticated AI-driven systems capable of automating complex workflows, including policy recommendation and claims triage, while simultaneously enabling dynamic, context-aware user engagement. This paper presents the design, implementation, and empirical evaluation of Axlerod, an AI-powered conversational interface designed to improve the operational efficiency of independent insurance agents. Leveraging natural language processing (NLP), retrieval-augmented generation (RAG), and domain-specific knowledge integration, Axlerod demonstrates robust capabilities in parsing user intent, accessing structured policy databases, and delivering real-time, contextually relevant responses. Experimental results underscore Axlerod's effectiveness, achieving an overall accuracy of 93.18% in policy retrieval tasks while reducing the average search time by 2.42 seconds. This work contributes to the growing body of research on enterprise-grade AI applications in insurtech, with a particular focus on agent-assistive rather than consumer-facing architectures.
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