Utilizing Machine Learning to Enhance Cash Flow Management in SAP Finance

Authors: Surya Sai Ram Parimi

DOI: https://doi.org/https://doi.org/10.5281/zenodo.12805591

Short DOI: https://doi.org/gt442w

Country: India

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Abstract: Effective cash flow management is critical for organizational stability and growth, particularly within SAP Finance systems, where accurate forecasting and efficient resource allocation are paramount. This survey explores the application of machine learning techniques to enhance cash flow management in SAP Finance. Machine learning offers capabilities such as predictive modeling, anomaly detection, and opti-mization algorithms that enable organizations to improve cash flow forecasting accuracy, detect financial anomalies proactively, and optimize cash allocation strategies. However, integrating machine learning into SAP Finance presents challenges including data integration complexities, model interpretability is-sues, and regulatory compliance concerns. This paper reviews current literature, identifies key challenges, and discusses operational considerations for successful implementation. Future research directions in-clude enhancing model interpretability, integrating real-time data processing capabilities, and advancing resilient risk management strategies. By leveraging machine learning, organizations can navigate financial complexities more effectively, drive operational efficiency, and achieve sustainable financial perfor-mance in SAP Finance environments.

Keywords: Cash flow Management, SAP Financial, Machine Learning

Paper Id: 230799

Published On: 2024-04-01

Published In: Volume 12, Issue 2, March-April 2024

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