Citing stagnant and declining fraud enforcement budgets, a new report from Deloitte University Press pushes government fraud fighters to adopt a "holistic approach" to fraud prevention that relies on data and predictive analytics to identify trends.
According to the report, this holistic approach is made up of five strategies:
- Data collection
- Learning systems that respond to emerging schemes
- An emphasis on prevention
- Targeted behavioral techniques that facilitate compliance
- Data sharing
The report notes that "data collection is critical to the prevention of fraud, waste and abuse," and can come from both internal and external sources, including other benefits programs or state agencies. Government agencies should view fraud prevention as "a chess match" and use data to create learning systems and statistical models that will quickly identify new threats.
Adopting a "prevention focused strategy" can have a three-fold impact on fraud by reducing the costs of a retrospective investigation and discouraging future schemes, in addition to stopping the initial overpayment. The report also points to programs like TennCare, Tennessee's Medicaid program, which uses "collective intelligence" to share audits and investigations with other insurers.
Predicative analytics helped federal and state agencies uncover the largest fraud bust in U.S. history, cementing it as a tool to identify sophisticated fraud schemes. However, some have argued that the Centers for Medicare & Medicaid Services is too concerned about provider backlash to use the full force of predictive analytics.
To learn more:
- read the Deloitte report
Predictive analytics helps fraud fighters detect sophisticated schemes [Special Report]
Feds, states turning to predictive analytics to prevent fraud
Why Big Data still isn't putting a dent in Medicare fraud