Application of artificial intelligence in budget management: a systematic review
DOI:
https://doi.org/10.15649/2346030X.4199Keywords:
budget optimization, financial management, genetic algorithms, neural networks, regression modelsAbstract
In budget management, traditional methods have been criticized for their rigidity and inefficiency in dynamic environments. This study systematically reviews the applications of artificial intelligence (AI) in organizational budget management. The specific objectives are: (i) to identify AI functions that impact budget management, (ii) to determine the aspects in which AI improves this management, and (iii) to identify the AI algorithms used. The PRISMA methodology was employed, and searches were conducted in Scopus, ScienceDirect, and Wiley, selecting 14 studies. The findings showed that AI is applied in expenditure distribution, financial risk prediction, internal control evaluation, cost budget control, and budget performance evaluation. It improves decision-making, expense control, operational efficiency, and budgetary accuracy. The most commonly used algorithms include Convolutional Neural Networks (CNN), Backpropagation Neural Networks (BPNN), Genetic Algorithms, Multilayer Perceptron, Dynamic Distributed Clustering Algorithm, and Multiple Linear and Logistic Regression models. The research concludes that AI shows evident utility and promising results in budget management. However, it is recommended to delve deeper into its applications in specific budget management functions and explore less studied but relevant areas in financial administration.
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