Improving Tax Audit Efficiency Using Machine Learning: The Role of Taxpayer’s Network Data in Fraud Detection

Baghdasaryan, Vardan; Davtyan, Hrant; Sarikyan, Arsine; Navasardyan, Zaruhi

doi:10.1080/08839514.2021.2012002

Cited by 17 publications

(9 citation statements)

References 13 publications

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“…The authors in [17] utilize taxpayer-specific features to detect tax fraud. They study the effect of the taxpayer's network data in fraud detection.…”

Section: Literature Reviewmentioning

confidence: 99%

A Multi-Module Machine Learning Approach to Detect Tax Fraud

Alsadhan¹

2023

Computer Systems Science and Engineering

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Tax fraud is one of the substantial issues affecting governments around the world. It is defined as the intentional alteration of information provided on a tax return to reduce someone's tax liability. This is done by either reducing sales or increasing purchases. According to recent studies, governments lose over $500 billion annually due to tax fraud. A loss of this magnitude motivates tax authorities worldwide to implement efficient fraud detection strategies. Most of the work done in tax fraud using machine learning is centered on supervised models. A significant drawback of this approach is that it requires tax returns that have been previously audited, which constitutes a small percentage of the data. Other strategies focus on using unsupervised models that utilize the whole data when they search for patterns, though ignore whether the tax returns are fraudulent or not. Therefore, unsupervised models are limited in their usefulness if they are used independently to detect tax fraud. The work done in this paper focuses on addressing such limitations by proposing a fraud detection framework that utilizes supervised and unsupervised models to exploit the entire set of tax returns. The framework consists of four modules: A supervised module, which utilizes a tree-based model to extract knowledge from the data; an unsupervised module, which calculates anomaly scores; a behavioral module, which assigns a compliance score for each taxpayer; and a prediction module, which utilizes the output of the previous modules to output a probability of fraud for each tax return. We demonstrate the effectiveness of our framework by testing it on existent tax returns provided by the Saudi tax authority.

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“…The authors in [17] utilize taxpayer-specific features to detect tax fraud. They study the effect of the taxpayer's network data in fraud detection.…”

Section: Literature Reviewmentioning

confidence: 99%

A Multi-Module Machine Learning Approach to Detect Tax Fraud

Alsadhan¹

2023

Computer Systems Science and Engineering

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“…The works in [11,12] introduced fraud-focused advanced data analytics and machine learning. Although they were not focused on tax fraud detection, multiple studies [13,14] adapted supervised and unsupervised machine learning techniques. One should note that more contributions relied on unsupervised machine learning due to the scarcity of labeled data.…”

Section: Related Workmentioning

confidence: 99%

“…As outlined above, neither rule-based solutions [10] nor unsupervised-learning-based approaches [13,14] yielded satisfactory achievements when used to address tax fraud detection. The reported results were typically constrained by the expensive maintenance and update cost of knowledge-based rules as well as the np-hardness of the clustering problem.…”

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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“…For example, Oliveira et al [12] confirm machine learning techniques in the search of tax defaults evidence. Baghdasaryan et al [13] combine information contained in the supplier and buyer network of the taxpayer with machine learning models to identify tax fraud probability. Savić et al [14] analyze the tax risk management by using a hybrid method which mainly focus on outlier detection approach and unsupervised machine learning algorithms.…”

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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Improving Tax Audit Efficiency Using Machine Learning: The Role of Taxpayer’s Network Data in Fraud Detection

Cited by 17 publications

References 13 publications

A Multi-Module Machine Learning Approach to Detect Tax Fraud

A Multi-Module Machine Learning Approach to Detect Tax Fraud

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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