## 1 Introduction  
Each year, the Saudi Zakat Tax and Customs Authority (ZATCA) [1] receives millions of tax declarations from individuals, government agencies, financial institutions, companies, and various other entities. Those electronic forms include financial data relevant to taxpayer actvities. Additionally, ZATCA collects third-party data from several government agencies to consolidate taxpayer profiles and cross-checks their financial information. Typically, ZATCA relies on big data for its tax enforcement activities. In particular, Zakat under-reporting represents a major risk faced by the Saudi tax authority. In fact, the Zakat base represents the net worth of the entity as calculated for Zakat purposes [2]. Then, Zakat is charged on the company's Zakat base at 2.5%. To bridge the widening Zakat gap, ZATCA governments enforce various legal penalties and regulations [2]. In fact, Zakat non-compliance is perceived as a delinquent act. Thus, taxpayers are required to prove their compliance in order to avoid legal consequences. The earliest solutions considered to improve tax compliance relied exclusively on auditor efforts. However, this strategy is costly and constrained by the large number of taxpayers in addition to the reduced audit capacity of the tax administration. Moreover, big data being collected by tax authorities and stored in their databases are not efficiently exploited to enhance the detection rate of tax under-reporting. In other words, most of the case selection strategies rely on the intuition, domain knowledge, and experience of auditors with no intelligent mining of the existing data [3]. Further, taxpayers have been continuously developing new techniques of tax evasion that are relatively difficult to detect, requiring the deployment of advanced robust fraud detection methods [4; 5]. The recent advances in Artificial Intelligence and its application to data science promoted the interest of tax administrations around the world to design intelligent solutions to support their conventional approaches to determine fraudulent behavior and optimize the management of the avilable auditing resources and data collection capabilities. In particular, the rich tax data collected by tax administrations triggered the development of advanced analytics models intended to investigate and mine tax fraud patterns [6]. Namely, machine learning (ML) techniques have been adapted and associated with a large amount of tax datasets to improve risk description and detection performance [7]. Particularly, Value Added Tax (VAT) under-reporting detection has been formulated as a supervised learning task. In other words, historical VAT data have been used to train classification models able to map unseen VAT declarations into one of the predefined classes: under-reporting or actual declaration [8]. Despite the promising solutions introduced to address VAT under-reporting risk [9], to the best of our knowledge, no intelligent approaches have been proposed to alleviate Zakat fraud concerns. Moreover, although existing supervised machine learning techniques [10] solve the categorization problem, the challenge for tax administration remains the prioritization of cases to be audited. For instance, of 100,000 declarations positively categorized using a classification model as under-reporting risks, and given the audit capacity of a tax administration, only a subset of the flagged samples will be selected and sent to the operations department for audit. Naturally, the classification confidence or probability is used to determine the most risky instances. However, this priority measure does not take into consideration the revenue at risk, which is among the main key performance indicators for tax administrations. In other words, a highly accurate supervised learning model may not yield more tax income for the government.