MACHINE LEARNING-BASED PREDICTIVE FINANCE MANAGEMENT IN SAP ERP

Abstract

This paper explores the integration of machine learning techniques into SAP ERP systems for predictive finance management. The study aims to enhance real-time anomaly detection, fraud prevention, and financial forecasting by leveraging machine learning models that analyze large volumes of financial data. We designed a framework that applies supervised learning for identifying anomalies and unsupervised models for fraud detection, optimizing both efficiency and accuracy in financial management processes. Our experimental results demonstrate significant improvements in identifying compliance violations, detecting fraudulent activities, and predicting financial trends, compared to traditional ERP systems. The proposed solution provides actionable insights that enable organizations to anticipate financial risks and optimize resource allocation. This research concludes that machine learning-based predictive finance management offers a transformative approach to enhancing the capabilities of SAP ERP systems, ensuring improved decision-making and operational efficiency in enterprise finance. Future work will focus on integrating IoT and advanced AI techniques to further improve realtime financial insights.

Keywords: Predictive finance management, SAP ERP, Machine learning, Anomaly detection, Fraud prevention, Financial forecasting