Explainable Artificial Intelligence Architecture for Risk-Sensitive Data Analytics in Cloud and SAP-Based Platforms
Abstract
Cloud computing and enterprise resource planning (ERP) systems, particularly SAP-based platforms, have become central to modern enterprise operations, handling vast volumes of sensitive financial, operational, and customer data. While AI-driven analytics can extract valuable insights from these data, the increasing reliance on black-box machine learning models raises concerns regarding transparency, regulatory compliance, and risk management. This research proposes an Explainable Artificial Intelligence (XAI) architecture designed for risk-sensitive data analytics in cloud and SAP-based platforms. The proposed framework integrates advanced AI models with interpretability techniques, enabling stakeholders to understand, validate, and trust predictive outcomes. Key components of the architecture include data ingestion from SAP modules and cloud sources, risk-based feature selection, model training with interpretable machine learning algorithms, and explanation generation for decision support. The framework emphasizes compliance with regulations such as GDPR, SOX, and HIPAA by ensuring transparency in data processing and decision-making. Experimental evaluations on enterprise datasets demonstrate that the XAI framework not only maintains high predictive accuracy but also enhances model interpretability, risk assessment, and anomaly detection capabilities. This research contributes to bridging the gap between AI-driven analytics and enterprise risk management, offering a secure, transparent, and compliant approach for decision-making in SAP and cloud-integrated environments.
Article Information
Journal |
International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
|---|---|
Volume (Issue) |
Vol. 8 No. 5 (2025): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
DOI |
|
Pages |
17245-17254 |
Published |
October 8, 2025 |
| Copyright | |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Forrest Duncan (2025). Explainable Artificial Intelligence Architecture for Risk-Sensitive Data Analytics in Cloud and SAP-Based Platforms. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 8 No. 5 (2025): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 17245-17254. https://doi.org/10.15662/IJAESIT.2025.0805007 |
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