  Financial statement fraud detection is an important problem with a number of
design aspects to consider. Issues such as (i) problem representation, (ii)
feature selection, and (iii) choice of performance metrics all influence the
perceived performance of detection algorithms. Efficient implementation of
financial fraud detection methods relies on a clear understanding of these
issues. In this paper we present an analysis of the three key experimental
issues associated with financial statement fraud detection, critiquing the
prevailing ideas and providing new understandings.
