Tax Evasion Risk Management Using a Hybrid Unsupervised Outlier Detection Method

TL;DR

This paper introduces HUNOD, a hybrid unsupervised outlier detection method combining clustering and representational learning, to improve interpretability and domain relevance in tax evasion risk management using big data.

Contribution

HUNOD uniquely integrates two machine learning approaches and domain knowledge, enhancing interpretability and validation of outliers in tax datasets.

Findings

Achieved 90-98% internally validated outliers

Demonstrated effectiveness on real tax datasets from Serbia

Enhanced interpretability with explainable surrogate models

Abstract

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.