International Journal of Science, Engineering and TechnologyAuthors: Yashvik Pai
Abstract: In the volatile landscape of modern corporate finance, traditional spreadsheet-based liquidity management often fails to provide the real-time precision required for strategic decision-making. This review article evaluates the integration of machine learning (ML) methodologies within SAP financial modules specifically SAP S/4HANA Finance and SAP Treasury and Risk Management to optimize cash flow and liquidity. We examine how the transition to a unified data model, the Universal Journal, provides a high-fidelity training environment for predictive algorithms. The review categorizes ML approaches into three primary functional areas: time-series forecasting for predicting liquidity trends (utilizing models such as ARIMA, Prophet, and LSTMs), classification models for analyzing customer payment behavior to optimize Accounts Receivable, and Natural Language Processing (NLP) for automating bank-to-ledger reconciliation. Furthermore, we analyze the architectural synergy between SAP’s "One Exposure from Operations" framework and embedded AI, which allows for the continuous refinement of cash position forecasts. The article also addresses significant implementation hurdles, including the challenge of data fragmentation in hybrid SAP landscapes, the necessity for model interpretability in audited financial environments, and the shift toward "Autonomous Treasury" operations. By synthesizing current literature and technical documentation, this review provides a roadmap for CFOs and treasury professionals to leverage ML for reducing idle cash, mitigating foreign exchange risks, and enhancing organizational resilience through data-driven liquidity planning.
DOI: https://doi.org/10.5281/zenodo.18228239
