AI-Driven Treasury Forecasting in SAP S/4HANA: A Comparative Analysis of ML Models

Abstract

The article focuses on how the effort is to be put to improve the treasury forecasting using the Artificial Intelligence (AI), and the Machine Learning (ML) models in SAP S/4HANA applications. To learn the accuracy and reliability of the forecasting, the compared models in the research discussed by Linear Regression, ARIMA, LSTM, and Hybrid ML are applied. The study is conducted using the qualitative research method with the review of applications and cases enterprise systems. As it is observed, the results of the trained AI models especially, Hybrid ML are highly accurate and less prone to forecasting error compared to the traditional models. They as well increase the improved control of the liquidity and reduction of the manual effort in the operations. The results of the research indicate that the use of AI together with the implementation of the SAP enhances both financial accuracy and helps to make sound judgments. The current paper can be useful to organizations that choose to bring the modernisation of treasury forecasting by leveraging AI-based strategies and solutions.