Bank fraud is a significant issue affecting financial institutions worldwide, with organized criminal groups being the primary perpetrators. Fraud differs from anti-money laundering (AML) as it involves criminals misrepresenting their identity to steal money, while AML focuses on monitoring and reporting money movement to prevent illicit funds. Bank fraud can be categorized into two main types: loan fraud and credit card fraud. Detecting fraud is crucial during the application process and at the time of the fraud itself. Fraud detection teams focus on identifying suspicious accounts and activity through automated means, often utilizing machine learning algorithms. TigerGraph's advanced methods for consolidating information, assessing account connections, and monitoring behavioral changes contribute to effective bank fraud detection and prevention.