Every millisecond counts when it comes to fraud. A fraudulent transaction approved in 200ms costs real money. A legitimate transaction declined in 200ms costs a customer. Getting this balance right — at scale — is one of the hardest engineering problems in financial services. This is a deep dive into the architectural decisions, trade-offs, and hard lessons from building a production-grade credit card fraud detection system. No toy datasets. No Jupyter notebooks. Real architecture, real constraints. The Problem Is Not What You Think Most tutorials frame fraud detection as a machine learning problem. Pick the right model, tune your F1 score, ship it. In production, it's an engineering and systems problem with ML embedded inside it.…