Using genetic programming to detect fraud in electronic transactions

The volume of online transactions has raised a lot in last years, mainly due to the popularization of e-commerce, such as Web 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 proposes the use of Genetic Programming (GP), an Evolutionary Computation approach, to model and detect fraud (charge back) in electronic transactions, more specifically in credit card operations. In order to evaluate the technique, we perform a case study using an actual dataset of the most popular Brazilian electronic payment service, called UOL PagSeguro. Our results show good performance in fraud detection, presenting gains up to 17.72% percent compared to the baseline, which is the actual scenario of the corporation.