Application of Soft Computing to Tax Fraud Detection in Small Businesses

Abstract

In this paper, we present a soft computing model for tax fraud detection in small firms and businesses. Inputs to the model are periodical finance reports and related information about market and inspection firms, and outputs are an inference of the tax fraud status. First, after using fuzzy inferences, the system determines a close business class to which the inspected firms belong. Next, training by statistical data from the business class, neural network (NN) is used to determine the fraud status of the inspected firm. Training data for the NN is periodical finance reports, market information of the business class and fraud history of the inspected firms. Finally, we describe initial evaluations and our future works.