Introducing Axlerod: An LLM-based Chatbot for Assisting Independent Insurance Agents
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| Format: | Preprint |
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2025
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| _version_ | 1866918303459442688 |
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| author | Bradley, Adam Hastings, John Ahmed, Khandaker Mamun |
| author_facet | Bradley, Adam Hastings, John Ahmed, Khandaker Mamun |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_09715 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Introducing Axlerod: An LLM-based Chatbot for Assisting Independent Insurance Agents Bradley, Adam Hastings, John Ahmed, Khandaker Mamun Computation and Language Artificial Intelligence Human-Computer Interaction Information Retrieval I.2.7; H.3.3; H.5.2 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. |
| title | Introducing Axlerod: An LLM-based Chatbot for Assisting Independent Insurance Agents |
| topic | Computation and Language Artificial Intelligence Human-Computer Interaction Information Retrieval I.2.7; H.3.3; H.5.2 |
| url | https://arxiv.org/abs/2601.09715 |