Using Social Network Analysis (SNA) to spot bad actors
We all know that healthcare fraud continues to roil the industry. Mention worthy, most often only 5% of the overall fraud committed are discovered. MFCU (Medicaid Fraud Control Unit) and MFSF (Medicare Fraud Strike Force) have been identifying new schemes of fraud that keep evolving year-after-year. With current efforts, lot of these fraud schemes evade the eyes of the investigators. While every dollar that is spent on investigation gives an ROI of $7.7, depiction of an up-trend year-on-year with corresponding increase in investment is not amicable. Also, considering the fact that even the fraud schemes evolve year-after-year in-parallel, such additional investments are just a reactive chase route which doesn’t offer any permanent solution. In most cases, Fraud is well-considered, carefully organized and importantly a time evolving phenomenon.
Fraud can be associated with sociology. It often has a centrality. There are only a handful of brains behind majority of fraud schemes. This can be attributed to the certain associations among the bad actors. Sadly, due to the complexity of transactions and fast changing data, statistical methods fail to provide such connections and inferences.
Investigators need high visibility on these associations to understand and anticipate fraud before it occurs. Associations provide investigators with significant leads which would otherwise be difficult to find out. For example, the complex relationships between the providers and their direct neighborhood (own family), employees, shared resources, contractors, owners, organizations, etc. Social Network Analysis (SNA) helps the investigators in achieving this visibility.
SNA is a process that maps and measures these associations with the actors and groups within a given network. It derives the possible relationship between the actors by virtue of certain common behaviors such as purchase of properties, luxurious goods used, sites they visit, reviews added in websites, places of social interest, etc. SNA unleashes the associations among actors and their corresponding social ties. It gives information about the ‘who likes who and what’ due to social influences and also due to the effect of pressure from the actors around.
With its intuitive feature – Provider 360, KYP effectively uncovers hidden associations among the health care actors and their corresponding ties. Amidst hundreds of public and private data and inherent complexity in data interpretation, Provider 360 does all-round linking of providers and their associations in the form of a social graph that can be instantaneously grasped by the investigators, helping them in forming evidence based opinions. This enables identification of potential fraud-nets, thereby facilitating not just recoveries, but prevention of further losses.
For further information on Provider 360, please write to us on firstname.lastname@example.org