High performance implementation of tax fraud detection algorithm

Abstract

Tax fraud includes a large spectrum of methods to deny the facts and realities, claiming wrong information, and accomplishing financial businesses regardless of what the legal frameworks are. Nowadays, with the development tax systems and the large volume of the data stored in them, need is felt for a tool by which we can process the stored data and provide users with the information obtained from it. According to tax politics, especially value-added tax, the rate of tax fraud is now increasing. Based on the investigations, recent researchers tend to use similar and standard methods to detect tax fraud, which includes, association rules, clustering, neural networks, decision trees, Bayesian networks, regression and genetic algorithms. Because of large volume of tax database, most of the studied methods about fraud detection are computationally intensive. In order to increase the performance of fraud detection algorithms such as Bayesian networks, parallelism techniques are used in this paper. We used parallel technology of Microsoft .Net, parallel loops and P-LINQ on the Intel Xeon server with 16, X7755 dual core processors and memory of 32GB. The implementation results on real database show that a speedup of up to 9.2x is achieved.