abstract = "The volume of online transactions has raised a lot in
last years, mainly due to the popularity 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 17.72percent percent compared to
the baseline, which is the actual scenario of the
corporation.",
notes = "In Portuguese
Also known as \cite{Assis:2013:UGP:2526188.2526221}",