Fraud has become increasingly sophisticated, with global fraud-related losses reaching $485.6 billion in 2023 and U.S. internet crime losses surging to $16 billion in 2024, a 33% increase in just one year. Traditional detection systems often miss complex fraud patterns, such as identity theft and account takeovers, which are now coordinated across multiple accounts and devices. Graph technology addresses these challenges by connecting seemingly unrelated data points, revealing patterns of coordinated fraud through graph databases and algorithms. This approach can detect fraudulent activities such as multiple claims tied to a single phone number or bank account, as demonstrated during the U.S. COVID-19 unemployment scam where $6 billion was stolen. Graph analytics, using techniques like graph traversal and community detection, enhance the ability to trace connections and uncover fraud rings, allowing for earlier detection and more effective intervention. The dynamic nature of graph technology provides analysts with the tools to quickly identify and respond to fraud patterns, emphasizing the importance of speed and context in modern fraud detection.