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Analytics preventing fraud, waste, and abuse in benefits programs
The spread of anti-fraud techniques capable of learning from enterprise-wide data and adapting to changing fraud techniques has generated excitement among executives at corporations and government agencies alike. CFO’s can benefit from learning about the wide range of anti-fraud activities.
Leveraging adaptive enterprise analytics allows a robust and enhanced approach to fraud risk monitoring. When done right, it even allows some types of fraud to be prevented before they’re ever committed. The article discusses how evidence-based anti-fraud systems allow CFO’s to prevent fraud, waste, and abuse, and how they can be developed into training for all key decision makers. The presentation will also showcase the benefits and the efficiencies gained by utilizing the anti-fraud systems.
Anti-fraud activities that can benefit from analytics
Analytic insights can develop stronger compliance rules that can be used to frustrate fraudsters at the pre-payment level, or use creative techniques like soft notices to stop a scheme from continuing. In particular, there are at least six anti-fraud activities that can benefit from analytics, while avoiding the trap of creating more work for investigative teams already operating over capacity.
- Analytics can help policy makers institute improved compliance procedures
- Analytics drives improvements in continuous monitoring
- Evidence-based systems prioritize investigator and prosecutor activity
- Analytics powers soft notices to prevent fraud
- Evidence-based approaches focus fraud prevention training programs
- Analytics can support renegotiating benefits provider contracts
Analytics must be evidence-based and actionable
While many CFOs have begun to realize the importance of implementing evidence-based approaches, too often analytics is still used to generate leads for which there is not sufficient bandwidth to pursue.
Prevention should be the priority. It is far more economical to stop a payment or dissuade fraudulent activity than it is to investigate, prosecute, and recover. It’s clear that as part of a proactive prevention strategy, analytics can be used to generate better compliance procedures and change behavior. Through machine learning and the reduction of false positives, data can provide stronger continuous monitoring to stop fraud before payments are made. There will always be a need to investigate and prosecute select cases, particularly where the fraud is egregious. Given the limits on investigative and prosecutorial resources, analytics should be used to focus activity where it is most needed. Risk-based scoring approaches can filter leads and drive return on investment.
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