Adaptive Machine Learning for Dynamic Fraud Detection in Cloud-Native Environments
Keywords:
Adaptive Machine Learning, Fraud Detection, Cloud-Native Systems, Concept Drift, Anomaly Detection, Explainable AIAbstract
The rapid digital transformation of financial and enterprise systems has significantly increased the scale, complexity, and sophistication of fraudulent activities. Traditional rule-based fraud detection systems are increasingly ineffective in cloud-native ecosystems characterized by high-velocity data streams, distributed architectures, and evolving threat patterns. This paper presents a comprehensive analysis of adaptive machine learning (AML) approaches for dynamic fraud detection in cloud-native environments. It synthesizes contemporary research on online learning, anomaly detection, reinforcement learning, and hybrid architectures, and examines their integration within cloud-native infrastructures such as microservices and containerized platforms. The study further explores challenges including concept drift, adversarial attacks, data imbalance, and explainability, proposing architectural and algorithmic solutions. The paper concludes with future research directions focusing on federated learning, explainable AI, and real-time streaming analytics.
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