Welcome to the Pondera FraudCast, a weekly blog where we post information on fraud trends, lessons learned from client engagements, and observations from our investigators in the field. We hope you’ll check back often to stay current with our efforts to combat fraud, waste, and abuse in large government programs.
Way back in 2006, I read an article in the Harvard Business Review that described how the Internet had changed the sales profession. One key observation dealt with the “de-coupling” of the sales cycle from the buying cycle. Prior to the Internet, buyers had to contact vendors for information on their products. Today, buyers do their own research and successful salespeople need to unhinge preexisting customer assumptions prior to starting their sales process.
I believe that the Internet has had an even greater impact on fraud in government benefit programs. Government agencies are under constant pressure to move applications, certifications, and other processes on line to make them more convenient for citizens and businesses. This makes perfect sense because, after all, government exists to serve the needs of the citizens. Unfortunately, moving these processes to the Internet dramatically increases the incidence of fraud.
The Internet provides a degree of anonymity that makes it extremely attractive to fraudsters. The number of fictitious businesses and “ghost beneficiaries” in government programs has exploded in recent years. Many of our customers deal with applications associated with out-of-state or out-of-country IP addresses. Others come from deceased or incarcerated individuals. Still others show indicators of originating in “sweat shops” that create bulk applications and claims.
Just like the salesman that had to adjust to the new sales cycle, it’s important that government program integrity staff adjust to the changing fraud landscape. IP spoofing, anonymous email services, and the wide availability of stolen identities are realities in the post-Internet fraud market. Relying solely on the traditional detection and investigation techniques is no different than the sales person who thinks their prospect hasn’t done any of their own research.
Big data analytics and the predictive engines they spawned give Internet companies a way to monetize the online experience. By tracing online behavior, companies can target advertising, promotions, and point of sale opportunities based upon past buying decisions. Online habits of Web users can be associated with ideologies, interests, and values. Increasingly sophisticated probability engines predict future buying decisions with enough accuracy to fuel a dramatic increase in online sales and commerce over the past decade. Analysts use temporal versions of these tools to forecast market trends and evaluate risk.
In the fraud detection market however, early attempts to detect fraudulent behaviors using these same probabilistic engines have achieved limited success.
What makes detecting fraud different than detecting interests, values, and ideologies? The simple answer: Fraud is binary in nature–either a particular sequence of behaviors is fraud or it is not. For example, if an individual provider of medical services submits claims to an insurance company for 5,000 hours of services in a week (an instance from actual data), there had better be around 100 employees licensed to provide that service. If there are only three or four employees with the required licenses, the provider has committed fraud. Probabilistic engines struggle to detect fraud because they are not capable of modeling this“all or nothing”nature of violating a law.
At Pondera, we still make use of predictive analytics. But rather than detecting absolute fraud, we use the algorithms mostly to inform our fraud scores and to detect emerging fraud methods.
Once reliable methods of detecting fraud have been developed, predictive engines can also play an important part in helping insurance companies, financial institutions, and government agencies prioritize targets of investigation. Predictive models can identify the highest value targets that will recover the most money or disrupt the largest criminal organizations.