Authors: Nandini Bhalla
Abstract: Machine learning–driven risk management has gained significant attention as organizations increasingly rely on SAP-based financial and enterprise information systems to support critical business operations. Traditional risk management approaches in SAP environments are predominantly rule-based and reactive, limiting their effectiveness in detecting complex, evolving, and previously unknown risks. With the growing volume, velocity, and complexity of enterprise data, machine learning techniques offer advanced capabilities for predictive risk assessment, anomaly detection, and continuous monitoring. This review paper presents a comprehensive analysis of machine learning–driven risk management models applied to SAP-based financial and enterprise information systems. It systematically examines SAP system architectures, risk management frameworks, and the integration of supervised, unsupervised, and hybrid machine learning techniques for managing financial, operational, compliance, and access control risks. The paper also reviews SAP-specific data sources, data preprocessing requirements, and evaluation metrics used to assess model performance, with particular attention to challenges such as data quality, model interpretability, regulatory compliance, and system integration. Furthermore, the review identifies key research gaps and emerging trends, including explainable artificial intelligence, federated learning, and real-time continuous auditing within SAP environments. By synthesizing existing literature and highlighting practical and research implications, this paper provides valuable insights for researchers, practitioners, and organizations seeking to design and implement intelligent, scalable, and compliant risk management solutions in SAP-based enterprise systems.
DOI: http://doi.org/10.5281/zenodo.18170299
