The Danish Business Authority uses graph data science to predict and prevent fraud, leveraging Neo4j for storing detailed data about every decision in a graph database for later review. The team has expanded their infrastructure to incorporate machine learning, event-driven architecture, and metadata strategy, providing an explanation traceability to their models. By analyzing the connections between companies and individuals in a graph, they can identify potential fraudsters more effectively than traditional methods. The project aims to improve explainability to ensure that decisions are transparent and understandable by humans, and the team is now looking into implementing this approach for future anti-fraud models. To introduce graph technology to management, it's recommended to start with a small part of data and demonstrate how it can connect any point in the data, making it easy to understand and deliver value.