Cross-verification of revenue data using third-party data

There is currently limited use of geographic information system (GIS) and management information system (MIS) data available from third parties to help the government grow its tax revenue base. This leads to inefficient, random revenue targets and financial planning as well as incorrect data and analysis. This also leads to a lack of transparency with regards to spending and resourcing, leaving opportunities for corruption in revenue collection. This project aims to reduce the unreliability of the current property tax data by using third-party data providers.

Urban tax is often considered the most growth-friendly levy across countries, this project aims to increase efficiency of the Excise and Taxation Department (ET) and increase property tax revenue. We propose integration of third-party GIS and MIS data within the government’s own GIS and MIS data to identify inconsistencies such as missing properties. This project will develop new machine learning technology that generates maps to help identify regions and locations where land usage has changed. These changes will help to update tax calculations and register new locations which aren’t in the MIS.

The pilot will be based in regions in the Khyber Pakhtunkhwa (KP) province and will extend to other parts of the country. We will conduct a change analysis via satellite imagery of the last 3 years, highlighting regions for a physical survey and showing the government which areas have changed. These findings will feed into the second part where we combine MIS data with public GIS data, building tools to process and analyse, developing a solution to identify properties missing in the MIS. Finally, we will create a software for the ET to track taxation cases, allowing users to open and close cases as well as assign surveyors and monitor progress.