Identifying Tax Evasion in Mexico with Tools from Network Science and Machine Learning

Zumaya, Martin; Guerrero, Rita; Islas, Eduardo; Pineda, Omar K.; Gershenson, Carlos; Iñiguez, Gerardo; Pineda, Carlos

doi:10.1007/978-3-030-81484-7_6

Cited by 6 publications

(5 citation statements)

References 18 publications

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“…Some were originally conformed to remain legal for the purpose of camouflaging the operations while others to remain illegal to hide the beneficiary ownership and be dissolved when public funds are transferred. The latter is because shell companies are often used for tax avoidance in Mexico [Zumaya et al, 2021]. In the procurement context, they are dissolved after turning the diverted money into non-traceable cash.…”

Section: Discussionmentioning

confidence: 99%

“…Finally, we argue on the practical implications of our findings to the definition of economic organized crime forms, the measurement of emerging patterns, and the prevention and punishment insights that can possibly impact legal frameworks. Network analysis has the potential to identify criminal typologies that indicate higher economic and political risks [FATF and Egmont-Group, 2018, Zumaya et al, 2021, Falcón-Cortés et al, 2022, as well as to find gaps in law enforcement or misconceived problems in legislation that hinder the control of criminal networks. investigation aimed to evaluate the state's situation regarding simulated operations and to track public funding in 33 government agencies and 138 shell-companies for an amount of 23 million US dollars from 2014 to 2019.…”

Section: Introductionmentioning

confidence: 99%

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Organized crime behavior of shell-company networks in procurement: prevention insights for policy and reform

Nicolás-Carlock¹,

Luna-Pla²

2023

Trends Organ Crim

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In recent years, the analysis of economic crime and corruption in procurement has benefited from integrative studies that acknowledge the interconnected nature of the procurement ecosystem. Following this line of research, we present a networks approach for the analysis of shell-companies operations in procurement that makes use of contracting and ownership data under one framework to gain knowledge about the organized crime behavior that emerges in this setting. In this approach, ownership and management data are used to identify connected components in shell-company networks that, together with the contracting data, allows to develop an alternative representation of the traditional buyer-supplier network: the module-component bipartite network, where the modules are groups of buyers and the connected components are groups of suppliers. This is applied to two documented cases of procurement corruption in Mexico characterized by the involvement of large groups of shell-companies in the misappropriation of millions of dollars across many sectors. We quantify the economic impact of single versus connected shell-companies operations. In addition, we incorporate metrics for the diversity of operations and favoritism levels. This paper builds into the quantitative organized crime in the private sector studies and contributes by proposing a networks approach for preventing fraud and understanding the need for legal reforms.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Organized crime behavior of shell-company networks in procurement: prevention insights for policy and reform

Nicolás-Carlock¹,

Luna-Pla²

2023

Trends Organ Crim

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“…The transfer learning approach is then conducted by fine-tuning the trained model using five tax datasets collected in five Chinese regions. In [33], a large-scale dataset of electronic records of taxable transactions collected in Mexico was analyzed. The authors concluded that the interaction patterns of evaders differ from those corresponding to typical taxpayer behavior.…”

Section: Related Workmentioning

confidence: 99%

Simultaneous Classification and Regression for Zakat Under-Reporting Detection

Ismail

Alsadhan

2023

Applied Sciences

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Tax revenue represents an essential budget source for most countries around the world. Accordingly, the modernization of relevant technological infrastructure has become a key factor of tax administration strategy for improving tax collection efficiency. In particular, the fiscal consolidation of the Kingdom of Saudi Arabia has been supported by considerable development in tax policy and administration, aimed at raising more taxes from non-oil activities. In fact, non-Saudi investors are liable for income tax in Saudi Arabia. On the other hand, Saudi citizen investors (and citizens of the GCC countries) are liable for Zakat, an Islamic assessment. Typically, taxpayers are in charge of preparing and accurately reporting their Zakat declaration. This allows tax authorities to overview and audit their business activities. However, despite administration efforts to increase taxpayer compliance, considerable revenue remains at under-reporting risk. In this paper, we introduce a novel intelligent approach to support tax authority efforts in detecting under-reporting among Zakat payer declarations. In particular, the proposed solution aims at improving detection accuracy and determining the fraud cases that correspond to a higher revenue at risk. Specifically, we formulate Zakat under-reporting detection as a supervised machine learning task through the design of a deep neural network that performs simultaneous classification and regression tasks. In particular, the proposed network contains an input layer, five hidden layers, and two output layers for classification and regression. Zakat declarations are mapped into the predefined “under-reporting” or “actual declaration” classes. Moreover, the revenue at risk caused by the predicted fraud cases is learned by the designed model. This allows the proposed approach to prioritize the auditing of specific Zakat payers based on the corresponding predicted revenue at risk. A real dataset including 51,919 Zakat declarations was used to validate and assess the designed model. Further, the Synthetic Minority Oversampling Technique (SMOTE) boosted the proposed model performance in terms of classification and prioritization.

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“…The application of complex machine learning algorithms shows the suitability to process tax data and also the ability to produce higher accuracy in tax risk identification. Other research also provides certain evidence for better result of machine learning application in tax risk management, reduce tax risk and tax loss [8], [9], [10], [11]. Besides, the combination of machine learning approaches and data processing methods shows potential in enhancing the prediction power for tax data.…”

Section: Empirical Studiesmentioning

confidence: 99%

Forecasting Tax Risk by Machine Learning: Case of Firms in Ho Chi Minh City

Phong

Tam

Cuong

2022

Frontiers in Artificial Intelligence and Applications

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Tax is the main source of income for the State. However, managing tax collection effectively and limiting the tax risks is a challenge for state tax authorities. This study applies machine learning to assess and predict firms with tax risks using logistic regression algorithm. The data set includes 872 observations of firms in Vietnam market. The machine learning approach is used to classifies the firms into 2 categories which has tax risk or not based on 6 main factors: (i) revenue and other income; (ii) expenses; (iii) liquidity; (iv) asset; (v) liabilities; and (vi) equity. The results show that the machine learning method is effective and accurate in identifying and predicting risks in tax declaration. The authors recommend that the tax agencies could apply machine learning methods and go further with big data and artificial intelligence approach to identify and classify enterprises.

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Identifying Tax Evasion in Mexico with Tools from Network Science and Machine Learning

Cited by 6 publications

References 18 publications

Organized crime behavior of shell-company networks in procurement: prevention insights for policy and reform

Organized crime behavior of shell-company networks in procurement: prevention insights for policy and reform

Simultaneous Classification and Regression for Zakat Under-Reporting Detection

Forecasting Tax Risk by Machine Learning: Case of Firms in Ho Chi Minh City

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