Leveraging Oracle Fusion ERP’s Embedded AI for Predictive Financial Forecasting
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V2I3P108Keywords:
Oracle Fusion ERP, Predictive Forecasting, Artificial Intelligence, Machine Learning, Financial Analytics, Cash Flow Prediction, Expense Variance, Revenue TrendsAbstract
The process of predictive financial forecasting is becoming a different thing, as embedded Artificial Intelligence (AI) emerges into Enterprise Resource Planning (ERP) systems. Oracle Fusion ERP is a cloud-based product that incorporates machine learning models to make precise estimations using historical financial data and other external factors, enabling accurate predictions regarding cash flows, revenue changes, and expense changes. This essay provides an in-depth examination of the workings of embedded AI in Oracle Fusion ERP predictive forecasting, highlighting how Oracle Fusion ERP leverages data science, neural networks, and real-time analytics to support financial planning and decision-making. The results presented in the study outline the methodologies employed, including data preprocessing, model training, and validation techniques, with a focus on the main characteristics of natural language processing (NLP), anomaly detection, and adaptive learning. We consider the implementation of Oracle Fusion ERP AI using real financial data and compare its accuracy level to that of classical models in terms of forecasting. The study highlights the importance of incorporating externally applied macroeconomic factors, market mood, and seasonal performance in narrowing the accuracy of predictions. Comparative analysis indicates enhanced forecast precision, efficiency and agility of the business. The paper also makes contributions by providing an in-depth model that integrates the models on AI introduced by Oracle and financial strategy, in favour of its application in various fields of life and business, enabling data-driven decisions about finances
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