Neural Network Predictor for Fraud Detection: A Study Case for the Federal Patrimony Department

Abstract

-Fraud detection is necessary for any financial system. However, the way of committing frauds and also for detecting them have evolved considerably in the lasts years, mainly due the development of new technologies. Therefore, fraud detection via statistical schemes has become an important tool to reduce the chances of frauds. In this paper, we present a study case applied to the tax collection per month of the Federal Patrimony Department (SPU). In this study case, we analyze some of the current methods for fraud detection, as Rule-Based Systems and Neural Networks classifiers, and propose the use of Neural Networks predictors for detecting fraud in time series data of the SPU.