AI-Driven Cloud Framework for Real-Time Financial Threat Detection in Digital Banking and SAP Environments

Abstract

The increasing adoption of digital banking platforms and SAP-based enterprise systems has significantly improved operational efficiency and financial service delivery, but it has also expanded the cyber threat landscape. Real-time detection of financial threats such as fraud, unauthorized access, and data breaches remains a critical challenge in highly interconnected cloud environments. This paper proposes an AI-driven cloud framework for real-time financial threat detection in digital banking and SAP environments, designed to enhance security, scalability, and responsiveness.The proposed framework integrates cloud-native architectures with machine learning and artificial intelligence techniques to continuously monitor transactional data, system logs, and user behavior across banking applications and SAP systems. Advanced analytics and anomaly detection models enable early identification of suspicious activities, while automated response mechanisms support rapid threat mitigation. Security controls such as encryption, identity and access management, and continuous compliance monitoring ensure adherence to financial and data protection regulations. Experimental evaluation demonstrates improved detection accuracy, reduced latency in threat response, and enhanced resilience against evolving cyber threats. The framework provides a practical and scalable solution for securing modern financial systems operating in cloud-based digital banking and SAP ecosystems.