Real-Time Financial Risk Intelligence Using Secure-by-Design AI in SAP-Enabled Cloud Digital Banking
DOI:
https://doi.org/10.15680/IJCTECE.2024.0706017Keywords:
Digital Banking, Financial Risk Prediction, Secure-by-Design Architecture, SAP Integration, Cloud-Native Machine Learning, AI-Driven Risk Analytics, Fraud Detection, Credit Risk Assessment, Zero Trust Security, Real-Time AnalyticsAbstract
The increasing complexity of digital financial transactions and regulatory requirements has made real-time risk intelligence and end-to-end security critical for modern banking platforms. This paper proposes a secure-by-design, AI-enabled digital banking architecture for real-time financial risk prediction through seamless integration with SAP-based core banking systems and cloud-native machine learning frameworks. Security is embedded across the architecture using zero-trust principles, secure identity and access management, encrypted data streams, and continuous monitoring to ensure data confidentiality, integrity, and regulatory compliance. Transactional and operational data from SAP systems are ingested through secure event-driven pipelines and analyzed using low-latency predictive models for fraud detection, credit risk assessment, and anomaly identification. Cloud-native machine learning services, containerized microservices, and automated MLOps pipelines enable scalable model training, deployment, and continuous learning. The proposed architecture supports hybrid and multi-cloud environments, ensuring high availability, resilience, and fault tolerance. Experimental analysis demonstrates improved risk prediction accuracy, reduced decision latency, and enhanced security posture compared to traditional centralized banking systems. The framework serves as a reference model for next-generation digital banking solutions requiring secure, intelligent, and real-time financial risk management.
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