ETD

• data governance

This thesis addresses the challenge of data governance and proactive data quality control in the fintech domain, focusing on the Italian platform MoneyViz. The main objective is to design and implement an end-to-end framework capable of extracting, normalizing, and integrating heterogeneous financial data from sources such as blockchains, exchanges, brokers, and third-party APIs. These raw and fragmented inputs are transformed into a centralized and reliable information asset, easily accessible through Business Intelligence tools. Interactive dashboards and automated reports were developed to continuously monitor data quality and detect anomalies, while predictive machine learning models (Random Forest, XGBoost) were implemented to classify and prevent errors in user portfolios. The results demonstrate that combining governance practices, BI solutions, and AI techniques significantly enhances reliability, reduces errors, and provides a scalable and transparent approach to financial data management.