## 2 Related Works
Additionally, three supervised learning techniques, namely, neural network, decision tree and Bayesian network were used to build classification models able to detect fraudulent taxpayer behavior. Similarly, the research in [21] included a comparison between Artificial Neural Networks (ANN) [25], SupportVector Machines (SVM) [26], and K-Nearest Neighbors in the context of credit card fraud detection. The reported results showed that ANN outperforms the other models. In [27], the authors introduced a fraud detection approach for Mellon Bank. ANN proved to be more accurate and to enhance the timeliness and overall detection performance. ANN overtook decision trees and Naive Bayes classifiers when associated with financial data to detect fraud. In summary, the multilayer perceptron (MLP) model has been recommended for fraud detection tasks [28]. In [29], the researchers introduced a multilayer perceptron neural network model to detect fraud in personal income tax forms. The reported findings show that the multilayer perceptron can be considered as an efficient classifier to predict fraudulent taxpayers, and estimate the taxpayer's likelihood of cheating tax. One should note that the latter approach can be generalized to recognize fraud patterns for other types of taxes. A Hybrid Unsupervised Outlier Detection (HUNOD) model was presented in [22] to mine risky tax behaviors. In particular, user knowledge was fed into a combination of representational learning and clustering to detect outliers in personal income tax data. The authors claim that the interpretability of the detected outliers is performed through the training of explainable-by-design surrogate models over outliers validated internally. Recently, in [30], the authors coupled Artificial Neural Networks with a real dataset to detect factors related to income tax fraud. Their approach was designed to reduce time, effort, and cost taken by auditors in the manual identification of cases to be audited. This was the first study to adopt Artificial Neural Networks for income tax fraud detection in Rwanda. Similarly, financial prediction was tackled in [31] using deep convolutional neural networks (DCNN) and multilayer perceptron (MLP). In particular, the authors, used an 8-layer MLP and a 13-layer DCNN for their credit scoring model. The models were assessed using Australian and German credit scoring data. The reported experiments proved that DCNN achieved a considerably higher performance compared to MLP. The researchers in [32] outlined a transfer learning approach to build a tax evasion detection model. Specifically, exploited conditional adversarial networks to encode a collection of labelled tax evasion records by extracting the relevant features. 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. Based on this finding, they built deep neural network and random forest [15] models to classify unseen records as suspicious or evasion-free cases. Semi-supervised learning was also used to tackle tax evasion detection. In [34], a semi-supervised approach was introduced for VAT audit case selection. Precisely, a gated mixture variational autoencoder network [35] was adapted to extract relevant features and map them into some predefined classes. Another solution based on positive and unlabeled (PU) learning techniques was depicted in [36]. One should note that PU techniques are suitable for data collections including a small subset of data that are positively annotated while the remaining records are not labeled.