Securing Multi-Cloud Architectures with AI-Enabled Fraud Detection: Causal Trace Miner Analytics and ERP-Integrated Fraud Prevention Techniques
DOI:
https://doi.org/10.15662/IJEETR.2021.0306006Keywords:
Multi-cloud security, AI-enabled fraud detection, causal trace miner, ERP integration, fraud prevention, machine learning, multivariate threat classification, real-time monitoring, risk management, enterprise cloud securityAbstract
The proliferation of multi-cloud environments has increased the complexity of managing security and detecting fraud across enterprise systems. This study proposes an AI-enabled framework that integrates causal trace miner analytics with ERP systems to enhance fraud detection and prevention. By leveraging advanced machine learning models, multivariate threat classification, and real-time monitoring, the framework identifies fraudulent activities in financial transactions with high accuracy. The ERP integration allows seamless coordination between operational and security data, providing a holistic approach to risk management. Experimental results demonstrate significant improvements in fraud detection rates and reduced response times, highlighting the potential of AI and ERP-based approaches in securing multi-cloud infrastructures.
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