Most healthcare fraud-detection technology on the market today just scratches the surface – current fraud-detection strategies are leading to the recovery of only an estimated 10 percent of a $150 billion annual loss.

Predictive-modeling technology is poised to turn that statistic on its head. The advanced analytics made possible by predictive modeling give healthcare insurers the tools that their special investigative units (SIUs) need to not only detect more fraud but to predict fraud.

Tune into the second on-demand Webcast in our series on using analytics to enhance fraud detection and disease management. Join Kyle Cheek, director of fraud analytics for Health Care Service Corporation (HCSC), and Richard Ingraham, SAS senior industry strategist, as they explain:

• Why the collectible percentage of fraudulent claims is so low.
• How SIUs are measured for effectiveness.
• How proactive detection can improve loss percentages.
• How predictive modeling can enhance analytic capabilities and improve fraud detection and prevention.
• Why rules-based solutions are insufficient.

Cheek will also discuss how SAS enables HCSC to acquire added intelligence regarding both claims abnormalities and the best use of SIU resources. Don't miss your opportunity to learn how analytic units can best support SIUs and the appropriate use of modeling tools in this arena.

We hope you will view Detecting Healthcare Fraud Through Cutting-Edge Analytics!