A Hybrid Modular Architecture for Fraud Detection Using Offline and Online Machine Learning Models

This article proposes a hybrid and modular architecture for fraud detection that integrates both offline and online machine learning models to address challenges in dynamic financial transaction environments. The framework combines high-performance offline models, including XGBoost, LightGBM, and deep neural networks, with lightweight and adaptive online learners, such as Hoeffding Trees and Adaptive Random Forests, enabling accurate detection in both historical datasets and real-time streaming transactions. A key methodological contribution lies in balancing predictive performance, responsiveness, and interpretability, achieved through a weighted risk scoring mechanism and a unified cost-sensitive evaluation framework that aligns technical metrics with tangible financial impacts. The architecture emphasizes modularity and scalability, facilitating continuous adaptation via concept drift detection and feedback-driven retraining. Its implementation in a containerized, open-source environment ensures reproducibility, robustness, and seamless deployment in production-grade financial ecosystems, even under high-volume transactional loads. The proposed system effectively bridges the gap