  Efficient financial administrations need to ensure compliant behavior of all
tax subjects without excessive personnel costs or obstruction of compliant
companies. To do so, accurate prediction of non-compliance or fraud is crucial.
Social Network Analysis (SNA) provides powerful tools for fraud prediction as
fraudulence is often clustered in certain areas of real world social networks.
In this paper we present our results of comparing PageRank and the more recent
BiRank to infer risk-ranks based on network structure and prior fraud
information. Specifically, we model our social network from company register
data. We find that in this case study BiRank outperforms PageRank in both
quality of the resulting ranks for fraud prediction and run time. The results
show that this class of algorithms is generally useful for fraud and risk
prediction and more specifically also illustrate the potential of BiRank in
comparison, as it opens up new modeling opportunities. Our results show that
selecting companies for tax audits based on BiRank yields a precision of 16.38%
for the top 20.000 subjects selecting 83.4% of all fraud cases (recall).
