Detecting Tax Evaders Using TrustRank and Spectral Clustering

Mehta, Priya; Mathews, Jithin; Bisht, Dikshant; Suryamukhi, K.; Kumar, Kapil; Babu, Ch. Sobhan

doi:10.1007/978-3-030-53337-3_13

Cited by 2 publications

(4 citation statements)

References 15 publications

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“…Researchers also analyzed the tax return data of a group of commercial sellers in Telangana (India) based on graph clustering [41]. In graph clustering, the top-down method is used, and each sample is assigned to a cluster closer to the samples.…”

Section: Review Of the Literaturementioning

confidence: 99%

“…e closest Euclidean distance for clustering was used to identify similar samples. e results showed that clustering affected the tax samples, and suspicious samples were detected by clustering [41]. Another study used the machine learning classification approach to detect fraudulent samples of government-linked companies in Malaysia [42].…”

Section: Review Of the Literaturementioning

confidence: 99%

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Towards Tax Evasion Detection Using Improved Particle Swarm Optimization Algorithm

Mojahedi

Sangar

Màsdàrí

2022

Mathematical Problems in Engineering

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This paper employs machine learning algorithms to detect tax evasion and analyzes tax data. With the development of commercial businesses, traditional algorithms are not appropriate for solving the tax evasion detection problem. Hence, other algorithms with acceptable speed, precision, analysis, and data decisions must be used. In the case of assets and tax assessment, the integration of machine learning models with meta-heuristic algorithms increases accuracy due to optimal parameters. In this paper, intelligent machine learning algorithms are used to solve tax evasion detection. This research uses an improved particle swarm optimization (IPSO) algorithm to improve the multilayer perceptron neural network by finding the optimal weight and improving support vector machine (SVM) classifiers with optimal parameters. The IPSO-MLP and IPSO-SVM models using the IPSO algorithm are used as new models for tax evasion detection. Our proposed system applies the dataset collected from the general administration of tax affairs of West Azerbaijan province of Iran with 1500 samples for the tax evasion detection problem. The evaluations show that the IPSO-MLP model has a higher accuracy rate than the IPSO-SVM model and logistic regression. Moreover, the IPSO-MLP model has higher accuracy than SVM, Naive Bayes, k-nearest neighbor, C5.0 decision tree, and AdaBoost. The accuracy of IPSO-MLP and IPSO-SVM models is 93.68% and 92.24%, respectively.

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Section: Review Of the Literaturementioning

confidence: 99%

Section: Review Of the Literaturementioning

confidence: 99%

Towards Tax Evasion Detection Using Improved Particle Swarm Optimization Algorithm

Mojahedi

Sangar

Màsdàrí

2022

Mathematical Problems in Engineering

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“…The method is experimentally evaluated on a dataset encompassing tax declarations of building projects in Bogota, Columbia. Spectral clustering to detect tax evaders is also explored by Mehta et al (2020). Besides features derived from individual tax returns, in the clustering procedure, a feature derived from a graph showing business interactions among taxpayers is also included.…”

Section: Related Work and Our Contributionsmentioning

confidence: 99%

“…The application of unsupervised learning is mostly relying on various anomaly detection algorithms. These include various supervised, semi-supervised and unsupervised machine learning methods, including decision trees, spectral clustering methods, neural networks, graph-based methods, etc, for example see Wu et al (2012), Matos et al (2015), Bonchi et al (1999), (Basta et al, 2009), (da Silva et al, 2016), Castellón González and Velásquez (2013), Tian et al (2016), de Roux et al (2018), Mehta et al (2020). However, most existing anomaly detection studies focus on devising accurate detection models only, ignoring the capability of providing explanation of the identified anoma-lies (Pang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Tax Evasion Risk Management Using a Hybrid Unsupervised Outlier Detection Method

Savić¹,

Atanasijević²,

Jakovetić³

et al. 2021

Preprint

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Big data methods are becoming an important tool for tax fraud detection around the world. Unsupervised learning approach is the dominant framework due to the lack of label and ground truth in corresponding data sets although these methods suffer from low interpretability. HUNOD, a novel hybrid unsupervised outlier detection method for tax evasion risk management, is presented in this paper. In contrast to previous methods proposed in the literature, the HUNOD method combines two outlier detection approaches based on two different machine learning designs (i.e, clustering and representational learning) to detect and internally validate outliers in a given tax dataset. The HUNOD method allows its users to incorporate relevant domain knowledge into both constituent outlier detection approaches in order to detect outliers relevant for a given economic context. The interpretability of obtained outliers is achieved by training explainable-by-design surrogate models over results of unsupervised outlier detection methods. The experimental evaluation of the HUNOD method is conducted on two datasets derived from the database on individual personal income tax declarations collected by the Tax Administration of Serbia. The obtained results show that the method indicates between 90% and 98% internally validated outliers depending on the clustering configuration and employed regularization mechanisms for representational learning.

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Detecting Tax Evaders Using TrustRank and Spectral Clustering

Cited by 2 publications

References 15 publications

Towards Tax Evasion Detection Using Improved Particle Swarm Optimization Algorithm

Towards Tax Evasion Detection Using Improved Particle Swarm Optimization Algorithm

Tax Evasion Risk Management Using a Hybrid Unsupervised Outlier Detection Method

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