We develop a model of fraud in organizations that allows
an evaluation of the relative efficacy of mechanisms
designed to prevent fraud while explicitly recognizing
the social processes underlying the formation of organizational
norms. To develop our model, we use a method that
is relatively new in accounting research: agent-based modeling
(ABM). Designed to study the emergence of macro-level
phenomena from micro-level interactions, ABM is well
suited to address questions involving organizational outcomes
(e.g., a culture of fraud) resulting from the interactions
between individuals within an organization and
organizational variables. The use of ABM confers an additional
advantage: It allows us to gain insights into fraud
even when data in organizations are censored.
Our model is comprised of an organization represented
by 100 independent, heterogeneous agents (employees)
and a set of simple interaction rules. Following Cressey’s
(1953) characterization of occupational fraud (known as
the fraud triangle hypothesis), any agent in our model possessing
motive, opportunity, and an attitude that frames
the fraudulent act as acceptable will commit fraud. We allow
agents to repeatedly interact, with an eye toward
emergent aggregate fraud levels and the dynamics of fraud
over time. We begin with a benchmark model in which all
agents have opportunity and motive. We then modify our
model to investigate the impact of mechanisms to prevent
or detect fraud. We first investigate the impact of modifying
the likelihood that agents perceive the opportunity to
commit fraud. Next, we consider a hierarchy in which
higher-level honest employees exert greater influence than
lower-level employees (i.e., ‘‘tone at the top’’). Then we
consider the impact of asymmetric influence exerted by
fraudsters relative to honest employees (which can arise
as a result of ethical training, the implementation of a code
of ethics, or a variety of other interventions). Finally, we
consider the impact of detection and termination efforts.