Authors: Mihir Saxena
Abstract: Modern enterprises operating within SAP environments face increasing pressure to provide transparent and compliant financial reporting amidst growing data complexity and stringent global regulations. This review article evaluates the integration of machine learning (ML) techniques including supervised classification, unsupervised anomaly detection, and natural language processing to enhance the integrity of SAP financial systems. We analyze a layered architecture utilizing the SAP Business Technology Platform (BTP) and SAP HANA to orchestrate real-time data ingestion and intelligent document extraction. The study specifically addresses the role of machine learning in automated account reconciliation, real-time fraud detection, and continuous control monitoring (CCM) to ensure adherence to frameworks such as SOX and IFRS. Furthermore, we investigate the necessity of Explainable AI (XAI) using SHAP and LIME methodologies to maintain auditability and overcome the black box challenge in financial decision-making. By synthesizing current implementation hurdles, such as data quality and the multidisciplinary skills gap, with future trends like agentic AI and quantum-accelerated reporting, this article provides a strategic roadmap for the digital transformation of enterprise finance. Ultimately, we demonstrate that machine learning is a critical enabler of "autonomous finance," transforming SAP from a transactional system into a proactive, self-auditing governance framework that ensures institutional transparency and market trust.
DOI: http://doi.org/10.5281/zenodo.18231355
