Practical Machine Learning for Health Care Fraud, Waste and Abuse Detection

Aleksandar Lazarevic
Senior Director
Aetna Analytics Organization
SERC 306
Thursday, November 16, 2017 - 11:00
The US spends more than $3 trillion or 18% of GDP annually on health care. Nobody knows how much of this money is misused, but according to the FBI, the amount lost due to fraud, waste and abuse (FWA) ranged between $90B and $330B! This talk will offer practical advice on how to effectively organize and join various health care data such as claim and clinical data, how to appropriately set the problem and design an effective machine learning based solution that will not only detect suspicious FWA leads, but will also direct investigators through a faster review by providing effective visualization and suggesting most likely reasons behind such leads.