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Automated extraction of insurance product characteristics

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Orientador(es)

Resumo(s)

The increasing complexity and diversity of insurance products have highlighted the need for efficient methods to interpret and manage the detailed information present in regulatory documents. This project explores the application of Natural Language Processing (NLP) and Large Language Models (LLMs) in the automatic extraction of relevant characteristics of these products, addressing challenges such as structuring technical texts and accurately identifying rules, conditions, and variations. The research focuses on analyzing the state of the art in technologies such as vector databases, LLMs, knowledge graphs, and agentic workflows, as well as evaluating NLP tools and methodologies. The text goes on to explore the primary challenges encountered during the interpretation of insurance documentation, as well as the transformation of unstructured data into organized formats that are compatible with modelling systems. The solution developed responded satisfactorily to the objectives established, enabling the structured and consistent extraction of product characteristics from regulatory documents. To this end, AI agent-based workflows were used, supported by LLMs and validation schemes, ensuring the quality and consistency of the results.

Descrição

Palavras-chave

Document extraction Named Entity Recognition Large Language Models AI Agentic Workflows Insurance Product

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

Projetos de investigação

Unidades organizacionais

Fascículo

Editora

Instituto Superior de Engenharia do Porto (ISEP) – P.Porto