Enhancing Financial Fraud Detection in SAP Systems with Machine Learning Algorithms

Authors

Surya Sai Ram Parimi

Abstract

Financial fraud detection in SAP systems is a pressing concern for organizations seeking to protect their financial integrity and operational stability. Traditional rule-based approaches and manual audits are increasingly insufficient to address the sophisticated and evolving tactics of modern fraudsters. This survey paper provides a comprehensive overview of state-of-the-art machine learning algorithms and techniques to enhance fraud detection capabilities within SAP environments. The novelty of this work lies in its holistic examination of advanced machine learning methods, including decision trees, neural networks, support vector machines, and ensemble methods, specifically tailored for SAP systems. Additionally, this paper offers practical implementation strategies, emphasizing real-time data processing, online learning, and robust evaluation metrics. We also address common challenges such as data quality, scalability, and system integration, proposing effective solutions. Our contributions include a detailed analysis of the latest machine learning approaches, insights into their practical deployment in SAP systems, and the identification of future research directions, such as the integration of deep learning and blockchain technology for enhanced fraud detection. This survey aims to guide researchers and practitioners in developing more robust and adaptive fraud detection systems, ultimately improving the security and efficiency of financial operations in SAP environments

Keywords

Financial fraud detection, SAP systems, machine learning algorithms, anomaly detection, deep learning integration

Enhancing Financial Fraud Detection in SAP Systems with Machine Learning Algorithms. Surya Sai Ram Parimi. 2015. IJIRCT, Volume 1, Issue 2. Pages 1-11. https://www.ijirct.org/viewPaper.php?paperId=2407071