Authors: Aarvik Bhatnagar
Abstract: Effective financial governance is critical for ensuring accuracy, transparency, compliance, and risk mitigation in enterprise resource planning (ERP) systems. SAP ERP provides robust financial and control functionalities; however, traditional governance mechanisms largely depend on static rule-based controls and manual audits, which are increasingly insufficient in handling high-volume, complex, and dynamic financial transactions. This paper proposes an intelligent financial governance approach for SAP ERP systems using hybrid machine learning models that combine rule-based logic, statistical methods, and advanced machine learning techniques. The proposed framework integrates seamlessly with SAP financial modules to enable real-time monitoring, anomaly detection, predictive risk assessment, and continuous compliance management. By leveraging hybrid model architectures, the approach balances adaptability and learning capability with transparency and regulatory interpretability. Practical use cases, including fraud detection, compliance monitoring, and predictive financial controls, demonstrate the effectiveness of the proposed solution. Experimental evaluation highlights the superiority of hybrid models over traditional rule-based and standalone machine learning approaches in terms of detection accuracy, false-positive reduction, and operational scalability. The findings indicate that hybrid machine learning models can transform financial governance in SAP ERP from a reactive control function into a proactive, intelligent, and strategic capability.
DOI: http://doi.org/10.5281/zenodo.18159759
