Fraud is a major concern for health insurance payers, with kickbacks being one such scheme. Kickbacks in the medical community typically occur in the form of referrals or prescribing the use of specific drugs/equipment. Because of these referrals, providers participating in a kickback scheme will often have an unusually strong connection to each other in the data. We can link medical providers to each other through patient lists, bank accounts, physical proximity, etc. Having these connections makes this problem a prime candidate for network analysis. However, due to the direct relationship between participants in a kickback scheme, second degree connections—and thus communities—are not as useful for detection. This talk will cover how we have created a custom formula to highlight the strongest connections in an egonet, and how it allows us to score and rank the resulting subgraphs for investigation prioritization.