Tax Fraud Detection through Neural Networks: An Application Using a Sample of Personal Income Taxpayers

Abstract

The goal of the present research is to contribute to the detection of tax fraud concerning personal income tax returns (IRPF, in Spanish) filed in Spain, through the use of Machine Learning advanced predictive tools, by applying Multilayer Perceptron neural network (MLP) models. The possibilities springing from these techniques have been applied to a broad range of personal income return data supplied by the Institute of Fiscal Studies (IEF). The use of the neural networks enabled taxpayer segmentation as well as calculation of the probability concerning an individual taxpayer’s propensity to attempt to evade taxes. The results showed that the selected model has an efficiency rate of 84.3%, implying an improvement in relation to other models utilized in tax fraud detection. The proposal can be generalized to quantify an individual’s propensity to commit fraud with regards to other kinds of taxes. These models will support tax offices to help them arrive at the best decisions regarding action plans to combat tax fraud.

Explainable and transparent artificial intelligence for public policymaking

“…More recently, the use of ML has been proposed and explored for the analysis and mining of public policy-related information, as part of evidence-based policy approaches (Androutsopoulou and Charalabidis, 2018). Specifically, ML techniques for public policy-related applications have been explored in areas like taxation (López et al, 2019), public security and counterterrorism (Huamaní et al, 2020), public work design (Eggers et al, 2017), and healthcare (Qian and Medaglia, 2019). These systems have provided insights on the benefits and challenges of ML-based policymaking.…”

Simultaneous Classification and Regression for Zakat Under-Reporting Detection

“…In [29], the researchers introduced a multilayer perceptron neural network model to detect fraud in personal income tax forms. The reported findings show that the multilayer perceptron can be considered as an efficient classifier to predict fraudulent taxpayers, and estimate the taxpayer's likelihood of cheating tax.…”

Machine learning in Governments: Benefits, Challenges and Future Directions

“…The potential of machine learning in the public sphere is grounded in the tremendous data availability and policy prediction needs of this field. Machine learning has already demonstrated considerable potential to enhance the effectiveness and accuracy of many decisions-making scenarios ranging from medical diagnosis, granting mortgages, tax evasion, and terrorist activities identification (Kononenko, 2001;Nowshath et al, 2019;Rodríguez et al, 2019;Mantari et al, 2020). Machine Learning is an umbrella term encompassing a wide range of algorithms for fields such as natural language processing, data mining, image processing, and predictive analytics.…”