An Innovative Real-Time Multilayer Framework for Intelligent Big Data Fraud Detection in Digital Ecosystems

The digital transformation has exponentially increased online transactions. This amplifies the risks of fraudulent activities across different sectors such as financial, healthcare, and insurance. Massive complex data volumes and rapidly evolving fraudulent techniques challenge traditional fraud detection methods. These methods are primarily based on static rule-based systems or conventional machine learning (ML) techniques. They face significant limitations in adapting to the rapidly evolving landscape of fraudulent activities and the complex data characteristics of modern digital systems. This research conducts a comparative analysis based upon an extensive literature review of existing fraud detection solutions. The primary objective of this research is to develop an innovative, intelligent, and real-time big data fraud detection framework. This framework harnesses advanced ML algorithms to enable precise, low-latency fraud detection. This paper aims to provide a robust framework capable of effectively detecting and mitigating fraudulent activities in increasingly complex and dynamic 1 digital ecosystems by bridging critical gaps in current solutions. The research proposes a sophisticated five-layer framework integrating secure data ingestion, real-time detection, ML training, scalable storage, and an interactive user interface. The proposed framework has been validated using Credit Card Fraud, KDD Cup Network Intrusion and Insurance Claims data. The implemented framework achieved remarkable results, with ML models consistently performing above 0.95 in accuracy and rapidly processing huge amounts of transactions efficiently. The proposed framework combines big data technologies, rule-based evaluations, and ML models offering a flexible and adaptive solution for detecting traditional and emerging fraud patterns across financial, healthcare and insurance sectors. Subsequently, the newly presented framework strengthens fraud detection and facilitates future innovations.