Underpricing Fraud Detection using Artificial Intelligence Technology to reduce Tax evasion in Tanzaniaa

This repository includes qualitative data collected through interviews, with responses gathered and analyzed using ATLAS.ti. In addition, videos and magazine notes were also coded in ATLAS.ti. The results of these analyses are presented in the research article submitted for journal publication.

The repository also contains quantitative data involving human participants, including taxpayers, tax officers, and customers (buyers). The questions varied depending on the group of respondents, which explains why some questions have missing values. The quantitative data were analyzed using SPSS (Statistical Package for the Social Sciences).

Moreover, supplementary data such as government and official reports were analyzed using Excel, and the results are included in the research paper. Lastly, the dataset used to train the machine learning model for developing the AI tool is also attached. This AI tool is integrated into a simulated EFD (Electronic Fiscal Device) application to detect underpricing fraud in real time.