An Exploratory Data Analysis Approach for Tax Revenue Systems

Abstract

Tax collation is an important part of any company's revenue system. Usually, over time, the process becomes more daunting, and the ability to monitor tax trends and revenue streams decreases. Not to mention gaining useful insights that can aid in decision-making and company transactions. That is why a tax data analysis system is able to continuously monitor tax information, pointing out anomalies, trends, and providing useful data visualizations. The tax analysis system would also enhance transparency and accountability in tax collection, improve efficiency, and reduce the need for audits, hence underlining its potential. Tax data is an important collection of information; however, many businesses fail to take advantage of this by not digging deeper into that collection. The aim of this research is to explore tax and sales data in an attempt to gain valuable insights and provide clearer information to the user. The methodology adopted is Exploratory Data Analysis (EDA) using Python as the main tool. The dataset used consists of 5,200 transactional tax records obtained from small and medium-scale enterprise (SME) sales reports spanning a 24-month period (Jan 2022 – Dec 2023). All data contained fields were pre-processed and stored in an SQLite database. Using Python libraries like Pandas, Matplotlib, Plotly, descriptive statistics, and visualization analyses showed that corporate tax contributions accounted for 47.8% of total tax revenue, while sales tax trends fluctuated seasonally, peaking in Q2 and Q4 of each fiscal year. The analysis demonstrated a 12% improvement in tax insight accuracy compared to manual spreadsheet tracking. The results show that with the approach, tax data can provide insights that can inform business decisions through charts and graphs. In conclusion, the platform can be a great tool in business decision-making and breaking down large datasets to give meaningful information.