2015 Annual Training Conference & Anti-Fraud Expo

Sessions Speakers Exhibitors Show Specials Floor Plan Buzz

Building a Model to Combat Workers' Compensation Fraud (Room Gaslamp A-C)

To prepare for today's data challenges and beyond, government-funded and commercial insurance plans need a data management infrastructure that provides access to data across various channels. Because unscrupulous providers and suppliers often intentionally give inaccurate, incomplete or inconsistent information to prevent records matching across disparate systems, insurance plans need data quality capabilities that support entity resolution. To make use of all data sources, insurers need a robust business analytics foundation to identify suspicious patterns pointing to programmatic fraud, waste or abuse. And they need an infrastructure designed to stop improper payments, instead of chasing the moPostal employees who suffer work-related injuries or illnesses receive compensation and medical benefits under the Federal Employee's Compensation Act (FECA), administered by the Department of Labor/Office of Workers' Compensation Programs (DOL/OWCP). In FY 2014, FECA benefits paid by the Postal Service totaled $1.32 billion. The Postal Service Office of Inspector General (OIG) has incorporated data mining and analytics in the area of workers' compensation fraud and built predictive models that assist in identifying claimants and medical providers who have a higher likelihood of being fraudulent. Built as a one stop shop for investigators, we will present the visualization of the models output which provides risk scores and immediate access to detailed claimant and provider data allowing investigators to be proactive and focus their attention on cases with the highest fraud probability. This session presents the use of both data analytics in support of claimant and provider fraud investigations from the perspective of Criminal Investigators. An overview of the applications and models used is given, along with case studies that showcase the return on investment that can be expected.ney after it's long gone.