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Publicação
Simplifying complex insurance product management with AI
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Resumo(s)
The digital transformation of the insurance sector presents significant challenges in internal processes, particularly in the management of product information. These challenges arise from the high complexity and variability of insurance product models, which are frequently updated, and from the technical demands of existing tools that require extensive user expertise to operate effectively. Recent advances in Generative Artificial Intelligence (GenAI) and the growing use of Large Language Models (LLMs) and intelligent agents are opening up new opportunities to automate and streamline processes, enabling organizations to adapt to a rapidly evolving technological landscape. The Product Machine Explorer leverages Generative AI, Large Language Models (LLMs), and AI agents to make exploring insurance product models easier. Integrated with msg Life Iberia’s Product Machine platform, it allows users to interact with complex product data using natural language. By leveraging structured data and advanced query techniques, it can understand user requests and deliver accurate, context-aware responses, improving both efficiency and usability in product model exploration.
Built using the Evaluation-Driven Development (EDD) methodology and supported by software, prompt, and context engineering, the tool was assessed using defined metrics and expert feedback. The results demonstrate significant efficiency improvements, with professionals spending considerably less time on repetitive tasks. Overall, the Product Machine Explorer demonstrates how LLM-powered agents can simplify complex information management and support better decision-making in the insurance sector.
Descrição
Palavras-chave
Insurance Products Model Querying Generative AI Large Language Models AI Agents Agentic Workflows
Contexto Educativo
Citação
Teixeira, J., Sousa, P. G., Faria, L., Cardoso, D. & Oliveira, F. (2025, dezembro 9). Simplifying complex insurance product management with AI. In Sá, C., Oliveira, C., Silva, E., Cardoso, M., Morgado, N., Proença, P., Carvalho, P., Vieira, R., Meireles, R., & Moreira, S. (Eds.). Simpósio de Engenharia Informática 2025. Instituto Superior de Engenharia do Porto ISEP – P.Porto
Editora
Instituto Superior de Engenharia do Porto (ISEP) – P.Porto