Operational Benchmarking of ML Models for Fraud Detection: A Comparative Study on AWS EC2 and ECS

Credit card fraud detection increasingly depends not just on model accuracy but on real-time deployment performance and scalability. While many studies benchmark algorithmic improvements in classification precision, this work shifts focus toward the operational deployment layer, a critical yet underexplored aspect of machine learning (ML) systems engineering. We present a comparative benchmark of traditional ML models (logistic regression, XGBoost, LightGBM), deep learning architectures (convolutional and recurrent neural networks, Transformer models), and stacking ensembles across three AWS-based deployment configurations: EC2 virtual machines, single-task ECS Fargate containers, and autoscaled ECS Fargate with load balancing. Rather than measuring predictive accuracy, our experiments systematically evaluate inference latency, throughput (requests per second), reliability, and scalability under load, using containerized Flask APIs exposed via REST endpoints. Load tests were executed with varying user concurrency to simulate production behavior. Results show that traditional ML models offer the lowest latencies, whereas deep learning and stacked models demonstrate superior scalability under ECS autoscaling, achieving latency reductions up to 65% and throughput improvements over 100%. We also analyze runtime (Python 3.9 vs. 3.12) and estimate AWS cost-performance trade-offs. This work provides a reproducible framework for evaluating model-serving infrastructure and offers practical deployment guidance for practitioners building real-time fraud detection systems on AWS.