Machine Learning–Driven Financial and Marketing Analytics with Cybersecurity Intelligence in SAP Cloud Platforms
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Abstract
The rapid adoption of SAP-based cloud platforms in financial and marketing domains has significantly increased data-driven decision-making while simultaneously exposing enterprises to sophisticated cyber threats. This paper proposes a machine learning–driven analytical framework that integrates financial and marketing intelligence with cybersecurity analytics in SAP cloud environments. By leveraging advanced data analytics and generative AI techniques, the proposed approach enables real-time anomaly detection, predictive risk assessment, and adaptive threat mitigation across transactional and customer engagement data. Machine learning models are employed to calibrate financial forecasts, optimize marketing performance, and identify cyber attack patterns using behavioral and network-level indicators. The framework supports scalable cloud deployment and seamless integration with SAP systems, ensuring enhanced data security, operational resilience, and business agility. Experimental observations indicate improved accuracy in fraud detection, marketing insight generation, and cyber risk identification compared to conventional rule-based systems. The study demonstrates that unified analytics and cybersecurity intelligence can significantly strengthen financial and marketing operations in SAP cloud platforms while maintaining compliance and performance efficiency.
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