Recent work has attempted to build models to
predict the presence of management fraud.
Results from logit regression analysis of 75
fraud and 75 no-fraud firms indicate that
no-fraud firms have boards with significantly
higher percentages of outside members than
fraud firms (Beasley, 1996). Hansen et al.
(1996) use a powerful generalized qualitativeresponse
model to predict management fraud
based on a set of data developed by an
international public accounting firm. The
model includes the probit and logit
techniques. An experiment was conducted to
examine the use of an expert system
developed to enhance the performance of
auditors (Eining et al., 1997). Auditors using
the expert system exhibited the ability to
discriminate better among situations with
varying levels of management fraud risk and
made more consistent decisions regarding
appropriate audit actions. Green and Choi
(1997) presented the development of a neural
network fraud classification model
employing endogenous financial data.