Plaid's Signal platform uses a mix of real-time and batch-computed features to predict financial transaction risk, including ACH transactions, with the help of machine learning models such as XGBoost. To manage this complex data and ensure high accuracy, Plaid relies on Tecton's feature platform to store, serve, and manage their features for online inference and offline training. This includes using Stream Ingest API to handle mutable bank transaction data efficiently, generating training data with custom time-snapshotted datasets, and utilizing On-Demand Feature Views for low-latency feature serving. Plaid also leverages Tecton's declarative configuration and MLOps best practices such as feature documentation, CI/CD, and consolidated infrastructure to streamline their ML operations, and advises other teams considering a feature platform to select one if they have an ML infrastructure team of size and are working with structured data.