  Financial fraud detection is an important problem with a number of design
aspects to consider. Issues such as algorithm selection and performance
analysis will affect the perceived ability of proposed solutions, so for
auditors and re-searchers to be able to sufficiently detect financial fraud it
is necessary that these issues be thoroughly explored. In this paper we will
revisit the key performance metrics used for financial fraud detection with a
focus on credit card fraud, critiquing the prevailing ideas and offering our
own understandings. There are many different performance metrics that have been
employed in prior financial fraud detection research. We will analyse several
of the popular metrics and compare their effectiveness at measuring the ability
of detection mechanisms. We further investigated the performance of a range of
computational intelligence techniques when applied to this problem domain, and
explored the efficacy of several binary classification methods.
