A Multi-Cloud Security Framework for Financial Systems: AI-Based Fraud Detection, Causal Trace Miner Insights, and ERP-Driven Prevention Strategies
DOI:
https://doi.org/10.15662/IJARCST.2021.0406014Keywords:
Multi-cloud security, AI-based fraud detection, causal trace miner, ERP integration, fraud prevention strategies, machine learning, multivariate classification, real-time monitoring, financial cloud systems, risk managementAbstract
The rapid adoption of multi-cloud infrastructures in financial systems has introduced increased complexity in managing security and fraud prevention. This study presents a comprehensive framework that combines AI-based fraud detection with causal trace miner analytics and ERP-driven prevention strategies. By integrating machine learning and multivariate threat classification, the framework identifies anomalous patterns in real time, enhancing the detection of fraudulent activities across distributed cloud environments. ERP integration enables seamless coordination between operational and security data, supporting proactive risk mitigation and improving overall system resilience. Experimental results indicate substantial improvements in fraud detection accuracy and reduction in response times, demonstrating the effectiveness of AI and ERP-integrated multi-cloud security solutions.
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