Privacy-Enhanced Deep Learning Architecture for AI-Cloud-Based Financial Forecasting in SAP and SQL-Driven Business Systems
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Abstract
This paper proposes a Privacy-Enhanced Deep Learning Architecture for AI-cloud-based financial forecasting within SAP and SQL-driven Business Management Systems (BMS). The framework integrates cloud computing, artificial intelligence, and deep learning models to deliver secure, scalable, and data-driven financial insights for enterprise operations. Leveraging SQL-based data pipelines, the system enables efficient data preprocessing, feature extraction, and predictive modeling while maintaining compliance with privacy-preserving protocols. The proposed architecture employs federated and encrypted learning mechanisms to safeguard sensitive financial information during model training and inference in distributed cloud environments. Through experimental analysis, the framework demonstrates improved prediction accuracy, reduced latency, and enhanced data protection compared to traditional models. This study contributes to the development of trustworthy AI ecosystems, promoting responsible, privacy-aware financial intelligence and decision-making in SAP-integrated enterprise infrastructures.
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