Financial fraud detection is an important problem with a number of design aspects to consider. Issues such as problem representation, choice of detection technique, feature selection, and performance analysis will all affect the perceived ability of solutions, so for auditors and researchers to be able to sufficiently detect financial fraud it is necessary that these issues be thoroughly explored. In this paper we will analyse some of the relevant experimental issues of fraud detection with a focus on credit card fraud. Observations will be made on issues that have been explored by prior researchers for general data mining problems but not yet thoroughly explored in the context of financial fraud detection, including problem representation, feature selection, and performance metrics. We further investigated some of these issues with controlled simulations, concentrating on detection algorithms, feature selection, and performance metrics for credit card fraud.