Machine Learning-based Predictive Analytics for Financial Planning and Budgeting in ERP Systems

Abstract

A financial budget is a crucial component of managing accounts and a crucial aspect of organization administration. The financial accounts section provides the foundation for the budget, and the invoices, accounts, and additional data found in the financial accounts section serve as the foundation of the financial planning and budgeting section's implementation data. Artificial Intelligence (AI)-based Enterprise Resource Planning (ERP) approaches are revolutionizing business processes through the incorporation of AI features into conventional ERP systems. This connection offers businesses remarkable efficiency benefits and improved user interfaces while revolutionizing accessibility and development. Hence this research examines the machine learning (ML) based predictive analysis for financial planning and budgeting in ERP systems. In this research, the PSO-SVM technique was analyzed using the 3 classification requirements of MAE, MRE, and RMSE. It was additionally evaluated under various evaluation periods of the BP and GA-SVM techniques throughout the precise timeframe. The PSO-SVM technique suggested in this study has a comparative time complexity of 0.392, according to the test data. In terms of relative time-based difficulty, it operates better. The empirical findings demonstrate the substantial advantage of the ML prediction analysis in terms of prediction stability and accuracy. The suggested approach can assist businesses in allocating their materials more scientifically, maximizing their advantages, and organizing their finite materials.

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