  Big data methods are becoming an important tool for tax fraud detection
around the world. Unsupervised learning approach is the dominant framework due
to the lack of label and ground truth in corresponding data sets although these
methods suffer from low interpretability. HUNOD, a novel hybrid unsupervised
outlier detection method for tax evasion risk management, is presented in this
paper. In contrast to previous methods proposed in the literature, the HUNOD
method combines two outlier detection approaches based on two different machine
learning designs (i.e, clustering and representational learning) to detect and
internally validate outliers in a given tax dataset. The HUNOD method allows
its users to incorporate relevant domain knowledge into both constituent
outlier detection approaches in order to detect outliers relevant for a given
economic context. The interpretability of obtained outliers is achieved by
training explainable-by-design surrogate models over results of unsupervised
outlier detection methods. The experimental evaluation of the HUNOD method is
conducted on two datasets derived from the database on individual personal
income tax declarations collected by the Tax Administration of Serbia. The
obtained results show that the method indicates between 90% and 98% internally
validated outliers depending on the clustering configuration and employed
regularization mechanisms for representational learning.
