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The use of Artificial Intelligence (AI) in the insurance industry covers a wide spectrum, including risk analysis, fraud detection, personalized policies, and customer support through chatbots. However, there is still room for improvement, as many techniques still rely heavily on manual processes. In this paper, we discuss both current solutions and a new system aimed at improving decision-making in the insurance field. We introduce a modular decision support system that weaves a Large Language Model (LLM) into crucial stages of the insurance decision-making process. Unlike fully automated systems, our approach embraces a Human-in-the-Loop (HITL) model, prioritizing transparency, user control, and explainability. This prototype allows insurance professionals to interact with data and AI tools using natural language, making it easier to handle tasks like portfolio analysis, customer segmentation, drafting personalized proposals, and querying structured data, without need for technical know-how.

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Human-in-the-Loop Generative AI for Explainable Insurance Decision Support

  • 会议论文

  • 发表日期：

    2026年

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  • Arianna Anniciello,
  • Simona Fioretto,
  • Elio Masciari,
  • Enea Vincenzo Napolitano

摘要

The use of Artificial Intelligence (AI) in the insurance industry covers a wide spectrum, including risk analysis, fraud detection, personalized policies, and customer support through chatbots. However, there is still room for improvement, as many techniques still rely heavily on manual processes. In this paper, we discuss both current solutions and a new system aimed at improving decision-making in the insurance field. We introduce a modular decision support system that weaves a Large Language Model (LLM) into crucial stages of the insurance decision-making process. Unlike fully automated systems, our approach embraces a Human-in-the-Loop (HITL) model, prioritizing transparency, user control, and explainability. This prototype allows insurance professionals to interact with data and AI tools using natural language, making it easier to handle tasks like portfolio analysis, customer segmentation, drafting personalized proposals, and querying structured data, without need for technical know-how.