Improving Income Tax Fraud Detection Accuracy with Logistic Regression
DOI:
https://doi.org/10.52783/jns.v14.3764Keywords:
Income Tax Fraud Detection, Logistic Regression, Decision Tree, Random Forest, Naive Bayes, K-Nearest Neighbors, Feed Forward Neural Network, Android StudioAbstract
Another critical source of revenue for governments involves taxation on income for people and companies, which must be paid under law. Probably the biggest challenge is tax fraud, which is the act of intentionally machining the declaration to avoid taxation. In conjunction with this, our project aims to analyze the financial data of taxpayers and establish a strong machine learning model for flagging such errant behavior. The authors compared six machine learning algorithms, the six types being a Feedforward Neural Network, k nearest Neighbors, Random Forest, Naive Bayes, Decision Tree, and Logistic Regression, for the detection and classification of tax fraud. Logistic regression performed best in the detection of tax fraud. The proposed framework is found to be better, whereby it is able to identify complex patterns better than the previously existing methods since both linear and non-linear correlations are considered in between variables. We trained and ran our model on the OpenML dataset. The results seem promising. Our model also assures that, besides being financially viable, it promotes fruitfulness in policy design with much less tax revenue loss. Using Tensor Flow for deployment of the model on Android Studio also enhanced accessibility. It has also led to the build-up of a simple prediction tool that helped people understand the risks involved in tax fraud.
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