Preventing failed trades is crucial for broker-dealers managing high-volume transactions. This article explores using a machine learning model with scalable infrastructure to predict trade failures in real time. By proactively identifying risks, broker-dealers can safeguard revenue, enhance efficiency, and ensure smoother trade settlements using a robust MLOps-driven approach.