  The article proposes an expert system for detection, and subsequent
investigation, of groups of collaborating automobile insurance fraudsters. The
system is described and examined in great detail, several technical
difficulties in detecting fraud are also considered, for it to be applicable in
practice. Opposed to many other approaches, the system uses networks for
representation of data. Networks are the most natural representation of such a
relational domain, allowing formulation and analysis of complex relations
between entities. Fraudulent entities are found by employing a novel assessment
algorithm, \textit{Iterative Assessment Algorithm} (\textit{IAA}), also
presented in the article. Besides intrinsic attributes of entities, the
algorithm explores also the relations between entities. The prototype was
evaluated and rigorously analyzed on real world data. Results show that
automobile insurance fraud can be efficiently detected with the proposed system
and that appropriate data representation is vital.

Artificial Intelligence;Social and Information Networks;Physics and Society;Machine Learning