Machine learning in budget forecasting for corporate finance: A conceptual model for improving financial planning
1 Etihuku Pty Ltd, Midrand, Gauteng, South Africa.
2 Department of Business Administration, Texas A&M University Commerce, Texas, USA.
Review
Open Access Research Journal of Multidisciplinary Studies, 2024, 08(02), 032–040.
Article DOI: 10.53022/oarjms.2024.8.2.0061
Publication history:
Received on 23 September 2024; revised on 01 November 2024; accepted on 04 November 2024
Abstract:
This paper explores the transformative potential of machine learning (ML) in budget forecasting within corporate finance. It begins with an examination of the importance of accurate budget forecasting for financial planning and the limitations of traditional methods. Theoretical foundations of ML are then discussed, highlighting relevant techniques such as regression, time series analysis, and neural networks. A conceptual model for ML-driven budget forecasting is proposed, detailing its components, architecture, and integration into financial planning processes. The paper also addresses the expected benefits, including improved accuracy and efficiency, alongside the challenges such as data quality, ethical considerations, and organizational barriers. By navigating these challenges through robust data governance, ethical practices, and strategic implementation, organizations can significantly enhance their budget forecasting processes, leading to better financial outcomes and strategic decision-making.
Keywords:
Machine Learning; Budget Forecasting; Corporate Finance; Financial Planning; Data Quality; Ethical Considerations
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0