A. A batch processing pipeline that aggregates data daily and trains a machine learning model to identify fraudulent transactions.

B. A real-time stream processing architecture that uses a combination of rule-based systems and machine learning models to detect fraudulent transactions as they occur.

C. A hybrid approach that combines real-time stream processing for initial detection and batch processing for periodic model retraining and refinement.

D. Train three seperate models for each data set, transaction history, customer profile, and network activitiy.

E. An architecture based solely on rule-based systems to minimize latency, ignoring the potential for machine learning to detect complex fraud patterns.