A Scalable AI-Driven Cloud Framework for Context-Aware Threat and Fraud Prediction in SAP Financial Systems
DOI:
https://doi.org/10.15662/IJRAI.2024.0704006Keywords:
Artificial Intelligence, Machine Learning, Cloud Security, SAP Financial Systems, Fraud Detection, Threat Prediction, Context-Aware Analytics, Multi-Tenant Cloud, Scalable Framework, Enterprise SecurityAbstract
Financial enterprises running SAP workloads in multi-tenant cloud environments face growing risks from fraud, cyber threats, and anomalous transactions. Traditional security and fraud detection approaches often struggle with the scale, complexity, and dynamic behavior of modern cloud-based systems. This paper proposes a scalable AI-driven cloud framework for context-aware threat and fraud prediction in SAP financial systems.The framework integrates machine learning algorithms with cloud-native processing to analyze transactional, behavioral, and contextual data in real time. By leveraging multi-tenant aware architectures, it ensures secure isolation, high availability, and efficient handling of large-scale financial data. Context-aware modeling enables adaptive risk assessment, predictive threat detection, and proactive fraud prevention. Experimental evaluation demonstrates the framework’s ability to improve detection accuracy, reduce false positives, and support dynamic decision-making across enterprise SAP environments. This study highlights the effectiveness of combining AI, cloud computing, and context-aware analytics for securing large-scale financial systems.
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