Authors :
Patrick Mazimpaka; Sanja Michael
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/mwbprabv
Scribd :
https://tinyurl.com/yzkkbyrk
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAR1503
Abstract :
This paper explores the application of
predictive analysis in assessing the impact of Electronic
Billing Machines (EBMs) on Value Added Tax (VAT)
revenue growth in Rwanda. EBMs represent a significant
innovation in VAT revenue collection, yet negative
taxpayer perceptions and a lack of understanding impede
their full potential. Without predictive analysis,
accurately projecting the growth in VAT revenues due to
EBM implementation remains a challenge for the
Rwanda Revenue Authority.
Using machine learning techniques and historical
data obtained from Rwanda Revenue Authority Annual
reports, this research aims to develop a predictive system
capable of forecasting the influence of EBMs on VAT
revenue growth. Employing a descriptive survey design
approach, secondary data analysis was conducted to
gather information on VAT taxpayers, EBM users, VAT
revenues, and other relevant variables.
The study reveals a notable increase in EBM
adoption among VAT payers over a six-year period,
indicating successful governmental efforts. Time series
analysis demonstrates a positive correlation between
EBM usage and significant increases in both VAT and
total tax revenues. The Seasonal Autoregressive
Integrated Moving Average (SARIMA) model,
specifically an ARIMA (1, 0, 0) model, is identified as
suitable for forecasting VAT revenue growth due to its
balance between accuracy and simplicity. The developed
predictive system provides highly accurate forecasts of
VAT revenue growth, facilitating informed fiscal
policymaking and enhancing financial stability and
revenue collection in Rwanda.
Keywords :
Electronic Billing Machine (EBM), Value Added Tax Revenue (VAT), Revenue Growth, Forecasting, Predictive Model, Tax Payers, Rwanda Revenue Authority.
This paper explores the application of
predictive analysis in assessing the impact of Electronic
Billing Machines (EBMs) on Value Added Tax (VAT)
revenue growth in Rwanda. EBMs represent a significant
innovation in VAT revenue collection, yet negative
taxpayer perceptions and a lack of understanding impede
their full potential. Without predictive analysis,
accurately projecting the growth in VAT revenues due to
EBM implementation remains a challenge for the
Rwanda Revenue Authority.
Using machine learning techniques and historical
data obtained from Rwanda Revenue Authority Annual
reports, this research aims to develop a predictive system
capable of forecasting the influence of EBMs on VAT
revenue growth. Employing a descriptive survey design
approach, secondary data analysis was conducted to
gather information on VAT taxpayers, EBM users, VAT
revenues, and other relevant variables.
The study reveals a notable increase in EBM
adoption among VAT payers over a six-year period,
indicating successful governmental efforts. Time series
analysis demonstrates a positive correlation between
EBM usage and significant increases in both VAT and
total tax revenues. The Seasonal Autoregressive
Integrated Moving Average (SARIMA) model,
specifically an ARIMA (1, 0, 0) model, is identified as
suitable for forecasting VAT revenue growth due to its
balance between accuracy and simplicity. The developed
predictive system provides highly accurate forecasts of
VAT revenue growth, facilitating informed fiscal
policymaking and enhancing financial stability and
revenue collection in Rwanda.
Keywords :
Electronic Billing Machine (EBM), Value Added Tax Revenue (VAT), Revenue Growth, Forecasting, Predictive Model, Tax Payers, Rwanda Revenue Authority.
