Application of machine learning in tax prediction: A review with practical approaches
1 Independent Researcher, New York, USA.
2 Independent Researcher, St Louis, Missouri, USA.
3 Department of Business Administration, Skinner School of Business, Trevecca Nazarene University, USA.
4 Independent Researcher, Nashville, Tennessee, USA.
5 Independent Researcher, Nigeria.
Review Article
Global Journal of Engineering and Technology Advances, 2024, 18(02), 102–117.
Article DOI: 10.30574/gjeta.2024.18.2.0028
Publication history:
Received on 02 January 2024; revised on 12 February 2024; accepted on 14 February 2024
Abstract:
This scholarly exploration delves into the dynamic intersection of machine learning (ML) and tax prediction, a realm where the fusion of advanced computational techniques and fiscal analytics heralds a new epoch in financial forecasting. The study's purpose was to meticulously dissect the efficacy of ML in tax prediction, navigating through the complexities and potentialities embedded within this innovative domain. The scope of the paper encompasses a comprehensive review of the evolution of tax prediction models, the significance of accurate tax forecasting in economic planning, and a critical analysis of previous studies, thereby laying a robust foundation for understanding the current state and future trajectory of ML in taxation.
Employing a methodical approach, the study conducted a systematic review of peer-reviewed literature, focusing on the comparative analysis of various ML models, the impact of data quality on predictions, and innovative approaches in ML for enhanced tax prediction. This rigorous methodology ensured a holistic understanding of the subject matter, providing insights into both the theoretical underpinnings and practical applications of ML in tax forecasting.
The main findings reveal that while ML presents unparalleled opportunities in tax prediction, it is not devoid of challenges such as data complexity, model interpretability, and ethical considerations. The study concludes that the integration of ML in tax prediction can significantly enhance accuracy and efficiency, provided these challenges are meticulously addressed. Recommendations include the adoption of appropriate ML models, emphasis on high-quality data, continuous model evaluation, and adherence to ethical practices in ML modeling.
In summary, this paper offers a comprehensive and nuanced perspective on the role of ML in revolutionizing tax prediction. It serves as a guiding framework for policymakers, tax authorities, and researchers, advocating for a harmonious blend of technological innovation and ethical responsibility in the pursuit of advanced tax forecasting methods.
Keywords:
Machine Learning; Tax Prediction; Financial Forecasting; Data Quality; Model Interpretation; Ethical Considerations.
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