Characterizing and Preventing Chargebacks in Web Payment Services

Authors

  • Evandro Caldeira Federal Center for Technological Education of Minas Gerais (CEFET-MG) Computing Department (DECOM) Belo Horizonte, MG, Brazil
  • Gabriel Brandao Federal Center for Technological Education of Minas Gerais (CEFET-MG) Computing Department (DECOM) Belo Horizonte, MG, Brazil
  • Adriano C. M. Pereira Federal University of Minas Gerais (UFMG) Dept. of Computer Science (DCC) Belo Horizonte, MG, Brazil

Keywords:

Fraud Detection; e-Business; Data Mining; Computational Intelligence; Bayesian Networks; Logistic Regression; Neural Networks; Random Forest

Abstract

The volume of electronic transactions has raised a lot in last years, mainly due to the popularization of e-commerce, such as online retailers. We also observe a significant increase in the number of fraud cases, resulting in billions of dollars losses each year worldwide. Therefore it is important and necessary to developed and apply techniques that can assist in fraud detection, which motivates our research. This work aims to apply and evaluate some computational intelligence techniques to identify fraud in electronic transactions, more specifically in credit card operations. In order to evaluate the techniques, we define a concept of economic efficiency and apply them in an actual dataset of the most popular Brazilian electronic payment service. Our results show good performance in fraud detection, presenting significant gains in comparison to the actual scenario of the company.

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Published

2014-01-01

How to Cite

Evandro Caldeira, Gabriel Brandao, & Adriano C. M. Pereira. (2014). Characterizing and Preventing Chargebacks in Web Payment Services. International Journal of Computer Information Systems and Industrial Management Applications, 6, 10. Retrieved from https://cspub-ijcisim.org/index.php/ijcisim/article/view/240

Issue

Section

Original Articles