Percorrer por autor "Cardoso, Duarte"
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- Automated extraction of insurance product characteristicsPublication . Oliveira, Francisco; Sousa, Paulo Gandra de; Faria, Luiz; Faria, Luiz; Cardoso, Duarte; Teixeira, JoãoThe 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.
- Simplifying complex insurance product management with AIPublication . Teixeira, João; Sousa, Paulo Gandra de; Cardoso, Duarte; Faria, Luiz; Oliveira, FranciscoThe 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.