Speaker "Mark Kanner" Details Back



Healthcare Fraud Detection


Big Data Analytics for Health Care Fraud Detection An ounce of prevention is worth a pound of cure Description: According to FBI, total health spending in America was around $3.3 trillion in 2015 with spending continuing to outpace inflation. According to several recent studies, estimates for healthcare spending lost due to fraud, waste and abuse (FWA) ranged between $90B and $330B! At the same time, only a small portion of this money was recovered in 2014 from individuals and companies who attempted to defraud various health care programs. The talk will show how we are developing a comprehensive, preventive and intelligent analytical framework for detecting fraudulent, abusive and wasteful claims in near real time using big data technologies and advanced machine learning techniques. The speaker will provide a quick overview of our in-house FWA detection framework and demonstrate how it is used to discover specific FWA scenarios identified in the most recent months. Next participants will deep dive into fundamental properties of fraud as observed in claims data and learn how to use these attributes to construct meaningful features to feed anomaly detection algorithms. The presentation will walk listeners through this process from initial calculations to analyzing results and provide broad guidelines for successful feature generation that can be applied to listeners own projects.


Mark R Kanner, Ph.D., Lead Data Scientist at Aetna Data Science Organization. Mark is a lead data scientist on the fraud detection team at Aetna and is responsible for both research and application of anomaly detection techniques for the automated identification of healthcare provider fraud. Previously Mark worked on the population health management team at Aetna, building machine learning models to determine which customers to reach out to for Care Management programs. Mark has a PhD in Physics from City College of New York.