International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Artificial Intelligence Powered Revenue Forecasting: Investigate The Use of AI To Predict Local Government Revenue

Author(s) Laode Buzyali Fajman Sanbigs
Country Indonesia
Abstract This paper explores the application of Artificial Intelligence (AI) in revenue forecasting for local governments. Revenue forecasting plays a crucial role in effective financial planning and resource allocation, enabling governments to make informed decisions regarding budgeting, expenditure, and policy formulation. Traditional revenue forecasting methods often rely on historical data and statistical models, which may have limitations in capturing complex patterns and dynamic factors that influence the revenue generation. This study aims to investigate the feasibility and effectiveness of using AI techniques to predict local government revenue. By leveraging Machine Learning (ML) algorithms and advanced data analytics, AI has the potential to uncover hidden patterns and correlations within vast and diverse datasets. The research methodology involves collecting historical revenue data from multiple local government entities and also implementing various AI models, including regression based algorithms and neural networks. The models are trained and validated using a comprehensive dataset that includes relevant economic variables, such as Population, GDP, The Unemployment rate, and Hotel Occupancy. The results of the study demonstrate the potential of AI-powered revenue forecasting in local government contexts with high-accuracy prediction. The findings also highlight the significance of incorporating dynamic external factors into the forecasting process, as AI models can capture complex relationships and identify key drivers of revenue generation.
Keywords Artificial Intelligence (AI), Revenue forecasting, Local government, and Machine learning (ML)
Field Business Administration
Published In Volume 5, Issue 6, November-December 2023
Published On 2023-11-25
DOI https://doi.org/10.36948/ijfmr.2023.v05i06.9334
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E-ISSN 2582-2160
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CrossRef DOI prefix of IJFMR is 10.36948/ijfmr

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