Feature Engineering and ML Pipelines for Cloud-Based Fraud Detection

Financial technology platforms process massive volumes of digital transactions across mobile banking, payment gateways, online commerce systems, and automated financial services. Detecting fraudulent activity within these high-velocity environments requires scalable data processing pipelines, robust feature engineering strategies, and machine learning models capable of making real-time decisions. This paper presents a cloud-based framework for fraud detection that emphasizes the design of feature engineering workflows and end-to-end machine learning pipelines. The proposed approach integrates streaming data ingestion, distributed feature computation, and scalable model inference using modern cloud infrastructure. Key feature categories including behavioral aggregates, temporal velocity metrics, contextual device attributes, and historical transaction profiles are analyzed to improve model performance in highly imbalanced fraud detection scenarios. Using benchmark-style experiments and representative financial transaction datasets, the study evaluates multiple machine learning models including Random Forest, gradient boosting (XGBoost), and deep neural networks. Experimental results show that feature engineering quality plays a critical role in improving detection accuracy, while gradient boosting models provide an effective balance between predictive performance and inference latency. The paper also discusses architectural considerations for feature stores, trainingserving consistency, and scalable deployment on cloud platforms. The findings highlight how well-designed feature pipelines and ML workflows can significantly enhance the reliability and scalability of fraud detection systems in modern FinTech environments.