Mining Suspicious Tax Evasion Groups in Big Data

Abstract

There is evidence that an increasing number of enterprises plot together to evade tax in an unperceived way. At the same time, the taxation information related data is a classic kind of big data. The issues challenge the effectiveness of traditional data mining-based tax evasion detection methods. To address this problem, we first investigate the classic tax evasion cases, and employ a graph-based method to characterize their property that describes two suspicious relationship trails with a same antecedent node behind an Interest Affiliated Transaction (IAT). Next, we propose a colored network-based model (CNBM) for characterizing economic behaviors, social relationships and the IATs between taxpayers, and generating a Taxpayer Interest Interacted Network (TPIIN). To accomplish the tax evasion detection task by discovering suspicious groups in a TPIIN, methods for building a patterns tree and matching component patterns are introduced and the completeness of the methods based on graph theory is presented. Then, we describe an experiment based on real data and a simulated network. The experimental results show that our proposed method greatly improves the efficiency of tax evasion detection, as well as provides a clear explanation of the tax evasion behaviors of taxpayer groups.

IRTED-TL: An Inter-Region Tax Evasion Detection Method Based on Transfer Learning

“…Third, as presented by Tian et al [1], the results of machine learning-based methods are not explainable and counterintuitive. Almost all machine learning models and transfer learning methods are black box models due to the feature mapping operation, which are vulnerable to security attacks [3] [4] [5].…”

“…Tax evasion causes a large revenue loss in China. The Chinese government reported that the rate of tax revenue loss in China was more than 22 percent [1]. Especially in recent years, tax evasion measures have become more diverse and covert in China.…”

“…National governments have taken a series of measures to detect tax evasion. Three means for tax evasion detection have been adopted by tax authorities in their daily operation: manual case selection, computer-based case selection and whistleblowing-based selection [1]. The computer-based case selection is primarily based on machine learning techniques, which extract evasion-related features from historical data for training and obtain a model that can be used in tax audit.…”

Data mining, fuzzy AHP and TOPSIS for optimizing taxpayer supervision

“…The researchers [3], conducted a study using a hybrid intelligence system to detect tax evasion from corporate taxpayers, namely taxpayers who do business beverages and textile in Iran. The researchers [4], conducted a study investigating classic tax evasion cases using several methods aimed at classifying tax evasion behavior based on the network that has been simulated with real data. The researchers [5], conducted a study by applying parallelism techniques that aim to improve the performance of fraud detection algorithms.…”

Privacy-Preserving Outsourced Auditing Scheme for Dynamic Data Storage in Cloud

“…As one of the vital services of cloud computing, cloud storage offers many attractive advantages, including the location-independent resources, ubiquitous network access, and on-demand storage space [2], motivating more and more enterprises and individuals to outsource their own data to cloud. Benefiting from the big data that is gathered together into the cloud, all kinds of datadriven techniques, such as data mining [3,4] and data signal processing [5,6], can be deployed upon the cloud storage environment to play their effective roles for creating more information wealth.…”