AI-Assisted Continuous Controls Monitoring (CCM) in Oracle Cloud ERP: An Intelligent and Adaptive Framework for Enterprise Compliance
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I4P115Keywords:
Continuous Controls Monitoring, Artificial Intelligence, Oracle Cloud ERP, Enterprise Compliance, Governance Risk and Compliance (GRC), Anomaly Detection, Machine Learning, Audit AutomationAbstract
Continuous Controls Monitoring (CCM) has become an important tool of assuring compliance within enterprises, minimization of risks, and transparency of operations in contemporary digital organizations. The customary control monitoring models are much more manual, periodical, and rule-based that they fail to deal with the volume, speed, and sophistication of modern enterprise resource planning (ERP) setups. The growing use of cloud-based ERP systems, especially the Oracle Cloud ERP, has augmented the requirement of intelligent, automated and adaptive solutions of compliance, which can run in real time. The paper will introduce a Continuous Controls Monitoring (CCM) framework on the Oracle Cloud ERP by using AI to boost business adherence by automating knowledge predictively and adapting dynamically to intelligence. The framework incorporates the machine learning, anomaly detection, process mining, and the natural language processing to constantly evaluate the transactional data, master data, user access activities, and configuration settings of the Oracle Cloud ERP modules. As opposed to typical, rule-based CCM systems, the proposed system is dynamic in nature and must change and adapt over time as the business processes and regulatory requirements evolve as well as the patterns of risks. The paper also shows the overall architectural design of the AI-based CCM framework, including data ingestion pipelines, feature engineering techniques, artificial intelligence model selection, control risk scoring system, and feedback-directed model modification. Analytical rigor is achieved with mathematical models of detecting anomalies, scoring risks, and optimization of models. The approach focuses on explainability, auditable-friendly, and regulatory compliant means so that AI-inspired insights are crystal clear and palatable to auditors and regulators. The discussion of the anticipated outcomes includes the aspects of better control performance, a decrease in false positive, achievement of compliance violation faster, and improving the audit preparedness. The framework is contextualized with the literature and industry practices so that the academic relevance and practical feasibility of the framework could be achieved. The study will lead to the developing literature on smart governance, risk, and compliance (GRC) systems and will provide a scalable framework that can be applied in businesses willing to update CCM into the context of the Oracle Cloud ERP.
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