PAPERS
To return to the "Published Papers" main page, click here.
THE SEVENTH INTERNATIONAL CONFERENCE ON FORENSIC COMPUTER SCIENCE - ICoFCS 2012
Print ISBN 978-85-65069-08-3 - Online ISBN 978-85-65069-06-9, pages 61-66
DOI: 10.5769/C2012010 and http://dx.doi.org/10.5769/C2012010
Neural Network Predictor for Fraud Detection: A Study Case for the Federal Patrimony Department
By Antonio Manuel Rubio Serrano, Joăo Paulo Carvalho Lustosa da Costa, Carlos Henrique Cardonha,
Ararigleno Almeida Fernandes, and Rafael Timóteo de Sousa Júnio
To download this paper, click here.
Print ISBN 978-85-65069-08-3 - Online ISBN 978-85-65069-06-9, pages 61-66
DOI: 10.5769/C2012010 and http://dx.doi.org/10.5769/C2012010
Neural Network Predictor for Fraud Detection: A Study Case for the Federal Patrimony Department
By Antonio Manuel Rubio Serrano, Joăo Paulo Carvalho Lustosa da Costa, Carlos Henrique Cardonha,
Ararigleno Almeida Fernandes, and Rafael Timóteo de Sousa Júnio
To download this paper, click here.
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.
KEYWORDS
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.
KEYWORDS
