IRTED-TL: An Inter-Region Tax Evasion Detection Method Based on Transfer Learning

Xulyu Zhu, Zheng Yan, Jianfei Ruan, Qinghua Zheng, Bo Dong
2018 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE)  
Tax evasion detection plays a crucial role in addressing tax revenue loss. Many efforts have been made to develop tax evasion detection models by leveraging machine learning techniques, but they have not constructed a uniform model for different geographical regions because an ample supply of training examples is a fundamental prerequisite for an effective detection model. When sufficient tax data are not readily available, the development of a representative detection model is more difficult
more » ... e to unequal feature distributions in different regions. Existing methods face a challenge in explaining and tracing derived results. To overcome these challenges, we propose an Inter-Region Tax Evasion Detection method based on Transfer Learning (IRTED-TL), which is optimized to simultaneously augment training data and induce interpretability into the detection model. We exploit evasion-related knowledge in one region and leverage transfer learning techniques to reinforce the tax evasion detection tasks of other regions in which training examples are lacking. We provide a unified framework that takes advantage of auxiliary data using a transfer learning mechanism and builds an interpretable classifier for inter-region tax evasion detection. Experimental tests based on real-world tax data demonstrate that the IRTED-TL can detect tax evaders with higher accuracy and better interpretability than existing methods.
doi:10.1109/trustcom/bigdatase.2018.00169 dblp:conf/trustcom/ZhuYRZD18 fatcat:qwi467xsxnaefbqg33chqmmbva