SingleStore is a database platform used by financial services institutions to power real-time analytics and fraud detection applications. A major US bank created a new streaming data architecture with SingleStore at its core, enabling them to move from batch fraud detection to real-time detection using machine learning models. This case study presents a reference architecture that can be used for similar use cases in financial services and beyond, highlighting the benefits of SingleStore's high-performing data platform, speed, scale, and SQL capabilities. The platform allows customers to add new features to their model scores using standard SQL, enabling agile updates without re-engineering or lengthy change management processes. This results in significant cost savings and improved customer experience, with potential tens of millions of dollars in lost fraud events avoided. SingleStore's distributed scale-out architecture, lock-free ingestion technology, and flexible data types make it an attractive option for financial services institutions looking to power their real-time analytics and fraud detection applications.