SAP-Based Digital Banking Architecture Using Azure AI and Deep Learning for Real-Time Healthcare Predictive Analytics
DOI:
https://doi.org/10.21590/ijtmh.10.02.11Keywords:
Secure digital banking, SAP integration, predictive analytics, AI, real-time analytics, fraud detection, enterprise systems, risk management, machineAbstract
The digital transformation of banking has led to increasingly complex operational environments where security, real-time insight, and enterprise system integration are paramount. This research proposes a Secure Digital Banking Framework that tightly integrates core SAP systems with real-time AI-driven predictive analytics to enhance risk management, fraud detection, customer personalization, and operational efficiency. Leveraging SAP’s enterprise capabilities (e.g., SAP S/4HANA, SAP Fiori, SAP Cloud Platform) combined with streaming data platforms and machine learning models, the framework supports real-time data ingestion, contextual analytics, secure access control, and regulatory compliance. The architecture incorporates role-based security, end-to-end encryption, audit trails, and continuous monitoring, ensuring robust protections for sensitive financial data. Predictive analytics modules use advanced machine learning and deep learning techniques to forecast credit risk, detect anomalous transactions, and model customer lifetime value. A prototype implementation demonstrates that integrating SAP data sources with real-time AI models significantly improves predictive accuracy and response times compared to traditional batch analytics. Results indicate improvements in fraud detection precision, reduced false positive rates, and better operational visibility. The proposed framework provides a blueprint for financial institutions to modernize digital banking infrastructure with secure, integrated, and intelligent features that support compliance, resilience, and customer satisfaction.
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