Browsing by Author "CHEN, MIGUEL HUANG"
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- Financial reporting with GenAIPublication . CHEN, MIGUEL HUANG; Santos, Joaquim Filipe Peixoto dosFinancial reporting is a critical but time-consuming activity in the banking sector, traditionally requiring analysts to manually extract data, validate figures, and draft lengthy reports. This thesis investigates the use of Generative AI, specifically a GPT-based API, to automate the reporting workflow. A key contribution lies in the design of structured prompt engineering strategies that constrain outputs, ensure numeric accuracy, and enforce corporate formatting requirements. The proposed framework integrates three components: (i) a data extraction tool for structured retrieval of financial indicators, (ii) a Python-based orchestration layer that preprocesses data, builds prompts, and manages interaction with the Generative AI API, and (iii) a report assembly module that converts the AI’s HTML output into fully formattedWord documents. A Streamlit-based interface centralizes usage, enabling analysts to configure parameters, trigger generation, and download reports seamlessly. Evaluation followed an iterative approach with weekly user feedback cycles. Results show a reduction of over 90% in preparation time compared to the manual workflow, alongside improved consistency and reduced operational risk. The framework also demonstrated adaptability across different use cases, from quarterly statements to daily transaction-level reports. While limitations remain—such as dependence on data quality and model fragility—the study demonstrates that LLM-driven prompt engineering can deliver scalable, auditable, and efficient financial reporting automation.