Machine Learning and Deep Learning Approaches for Tax and Public Revenue Forecasting in African Context: A Systematic Review
Keywords:
machine learning; deep learning; tax revenue forecasting; public revenue; AfricaAbstract
Forecasting tax and public revenue is a key component of fiscal planning, budget credibility and macroeconomic stability, especially in developing countries where the impact of forecast errors on public spending is particularly large. Although various machine learning (ML) and deep learning (DL) techniques have been used in the revenue forecasting problem across the globe, there is limited research that has attempted to synthesize the literature within an African context. The review is conducted in accordance with the PRISMA 2020 guideline to identify, screen and synthesize studies using some or all of at least one ML or DL model to forecast tax, customs and general government revenue in an African country. As the review was not done under paid database access, searches were made in freely accessible sources such as Google Scholar indexed publisher pages, institutional repositories, as well as IEEE Xplore. Four studies were identified from an initial pool of around 50 from nine searches and fulfilled all inclusion criteria, from Nigeria, Ghana and South Africa. It was consistently reported in included studies that ML/DL models, specifically artificial neural network (ANN) variations and hybrid models, outperformed the traditional statistical models like the ARIMA, SARIMA and Holt-Winters models. However, none of the four studies included forecasted on a daily-frequency basis, and evaluation metric choice was quite variable across different studies and no study came from the East, North or Francophone Africa region. These results suggest that there is little research on revenue forecasting using ML/DL in African context, mostly concentrated to few geographical areas and methodologically inconsistent, thus a substantive and largely unaddressed research gap.
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