## 2 Related Works  
To the best of our knowledge, to date, no research has dealt with Zakat fraud detection using machine learning techniques. Accordingly, this section covers relevant tax fraud detection techniques introduced by researchers and/or adopted by fiscal administrations around the globe. Typically, rule-based systems have been designed to address challenges related to various tax fraud detection. However, the resulting solutions proved to be limited by the millions of taxpayers (individual and business) to be investigated in addition to the subjective intuition and knowledge of auditors when selecting suspicious cases [11]. This alternative exhibits two main drawbacks: (i) expensive maintenance and update costs of knowledge-based approaches, and (ii) dependence on previous experience, which affects its ability to recognize recent fraudulent behavior. On the other hand, fraudsters keep developing tactics to evade paying taxes. This makes auditor intuition and experience insufficient to track them. Recently, data science and Artificial Intelligence emerged as the most promising alternatives for addressing complex analytics challenges. Specifically, they have been used to leverage the machine's ability to learn from available data with no explicit programming. 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. Particularly, the authors in [13] relied on unsupervised learning to group similarly valued tax declarations into homogeneous clusters. Then, they adjusted the resulting probability distribution of each obtained cluster. Finally, the detection of suspicious patterns was achieved based on the quantiles of cluster-adjusted distribution. Despite the reported promising results, the main limitations of the work were data scarcity and the reduced number of features used to represent it. Moreover, the absence of labeled gold data affected the trustworthiness of the performance achieved by the model. The pioneering deployment of supervised machine learning techniques to address fiscal fraud detection was achieved in [12]. Specifically, the authors investigated the C5.0 decision tree algorithm to build a predictive model for tax evasion detection in Italy. In addition, random forests [15], rule-based classification [16] and Bayesian networks were also considered to resolve fraudulent tax behavior detection. The researchers in [17] depicted a Value Added Tax (VAT) screening framework to determine non-compliant VAT declarations. In particular, the Apriori algorithm was employed to mine association rules from historical data of business entities with a confirmed fraudulent behavior. The resulting model was assessed using non-compliant VAT declarations collected in Taiwan from 2003 to 2004. Similarly, the Apriori algorithm was adapted in [18; 19] to mine hidden patterns underlying fraudulent tax behaviors. Specifically, it was associated with Principal Component Analysis (PCA) [20] and Singular Value Decomposition (SVD) [21], as dimensionality reduction techniques, in order to determine the relevant fraud indicators and learn a fraud scale to rank Brazilian taxpayers based on the risk they represent for the tax administration. In [22], unsupervised and supervised machine learning techniques were coupled to detect taxpayers' suspicious behavior. In particular, two unsupervised learning techniques were adopted to discover clusters of business entities that exhibit similar tax-related behavior. Namely, Neural Gas [23] and Self-Organizing Maps [24] were investigated and associated with two datasets including tax declarations of micro to small and medium to large companies collected in Chile from 2005 to 2007.