Fraud is constantly evolving. In the past, preventing and detecting benefits fraud involved manual processes – essentially following a paper trail. Today, fraud management is more complex as benefits administration moves from paper to digital. An increasingly more sophisticated approach that includes rigorous “checks and balances” is critical to establishing comprehensive fraud management programs. Plan sponsors and benefits providers know it’s more prudent and cost-effective to prevent fraud before it happens rather than try to recover funds after the fact. Plan sponsors can work with their benefits provider to build fraud protections into the plan structure. For example, implementing maximums on benefits may reduce the risk of abuse, misuse, or over-use of health benefits. To support plan design initiatives that limit misuse and abuse of benefits, many benefit providers are turning to artificial intelligence (AI) and machine learning to conduct proactive investigations. Certain types of AI technologies are able to not only find and compile masses of data like never before, but also find patterns at both the individual and aggregate levels beyond human capabilities. These two proactive approaches, along with other techniques, are designed to tackle fraud and abuse head on – before it happens, and if fraud does occur, mitigate its impact and continually investigate incidents to identify the root causes and establish preventive measures. Join Steve Richardson, Supervisor – Benefits Management & Investigation Services at Green Shield Canada, as he shares innovative ways to obtain and analyze benefits fraud information, the role of AI in proactively seeking fraud and abuse in the industry, and how people, such as field operations staff, impact fraud investigations. Steve will also share fraud investigation stories, discuss trends in the industry, and offer suggestions for plan design to limit fraud. |