A Medicare and Medicaid contractor processes payments and related information for services received from the entire spectrum of health care facilities, including hospitals, skilled nursing facilities, physicians, laboratories and suppliers. This company has been administering Medicare contracts in multiple U.S. states since the program began in the 1960s.
The company deals with massive amounts of data pertaining to Medicare beneficiaries and Medicaid recipients, as well as hundreds of thousands of individual files from doctors, hospitals and clinics. Fraudulent activity within the Medicare and Medicaid systems costs Americans an estimated $60 billion every year. As a processor of Medicare and Medicaid claims, the company sought to detect and prevent fraud – saving itself, its clients and taxpayers fraudulent claims expenses.
Detecting insurance fraud requires a complete picture of individuals and transactions occurring in isolated data silos. Transactions in one data silo can seem perfectly legitimate, but there may be information in another source that, when the two are combined, reveals the fraudulent nature of the transactions. It’s only when the data can be seen in its larger context – from a more comprehensive perspective – that patterns of fraud emerge. The company needed a system that would allow it to gain this perspective and automatically make connections across segmented groups of data. To do this, they turned to DataFlux.
DataFlux provides a single, unified platform for data management. The DataFlux Data Management Platform allows users to build corrective routines for problematic data, perform data matching and address verification, and extend those rules across the enterprise in batch or real time – all from a single interface. This approach gives business users improved control over enterprise data quality, allows them to deliver reliable, trusted data across the enterprise and lets them make informed business decisions based on accurate data.
The company used DataFlux technology to form a complete picture of the scale and nature of its data quality issues, profile its data to detect larger patterns and effectively act on those discoveries.
To form complete pictures of individuals and suspicious transactions, the company utilized DataFlux technology to match diverse data from disparate databases and immediately flag any dubious transaction. For example, if a transaction associated with a particular Social Security number occurred at a clinic in Florida at the same time a transaction associated with the same Social Security number occurred at a hospital in New Jersey, the software can flag the transaction as a possible case of identity theft.
Notification of suspicious transactions allows the company to immediately shut off that avenue of fraud, contact the involved parties and turn the evidence over to law enforcement. The company estimates that with DataFlux, it has already been able to prevent over $270 million dollars worth of fraud.