There is a common misconception that as technology does more, people do less. But while automation relieves people of manual tasks, it also frees them to do their jobs smartly. The benefits of technology are obvious in fraud mitigation, an area of the insurance enterprise where technologies around data mining and analytics have helped special investigations unit (SIU) members take deeper dives into claims data to uncover patterns of potential fraud that previously went undetected. When it comes to fraud mitigation, technology doesn't replace human ingenuity -- it augments it by providing investigators and claims adjusters with more context and more information. As such, the true value of data mining and analytics to fraud mitigation is realized in the innovative ways that carriers leverage it.
And for certain, carriers have had to become more innovative as the frequency and complexity of fraud have increased. Fraudsters have ramped up their efforts recently in large part because of the economic downturn. "The most serious current fraud threat is the increasing number of people who are in economic difficulties -- or think they may be in the near future -- and look for claims checks from insurers as a way of obtaining some quick cash," notes Donald Light, a senior analyst in Celent's insurance practice.
As fraudsters have turned up the intensity of their attacks, their tactics have become ever more sophisticated. When it comes to fraud prevention, it is no longer possible for technology organizations to hope to maintain a status quo -- those that do not proactively stay a step ahead of the bad guys become their prime targets.
"The world is getting smaller, the technology is getting more sophisticated and the bad guys unfortunately are getting more sophisticated too," says Robert Zandoli, VP of IT risk and compliance and chief information security officer at New York-based MetLife ($51 billion in total revenue). "On the other side of that, we have many programs using state-of-the-art technology to continue to keep up with those threats."
Citing her own experience as well as conversations with peers, Sheri Farrar, executive director of Chicago-based Health Care Service Corp.'s (more than 12.3 million members) special investigations department, says there has been an increase in healthcare fraud recently. Traditional schemes -- such as billing for services not rendered, miscoding to be paid for services not covered and up-coding -- remain, she adds, but they've grown and evolved. "The traditional fraud is still there," Farrar notes. "But we're seeing increases in using those types of schemes in more-sophisticated ways."
To fight those schemes, insurers are turning to increasingly sophisticated data analytics tools. According to Celent's Light, data mining and analytics solutions make up the foundation of modern fraud mitigation technology. "Data mining enables analysts within the claims department or an SIU to find patterns that are invisible to individual adjusters because a single adjuster only sees a small portion of the total claim volume," Light says. "Predictive analytics builds on the data mining findings to create red flags and claim potential scores."
Health Care Service Corp. (HCSC) is among the carriers that are employing analytics successfully in the fight against fraud. In June the company announced that it had uncovered a fraudulent scheme involving an Illinois allergist's office that was billing for non-rendered services, unbundling services and balance-billing members. The scam generated $800,000 in fraudulent claims and resulted in $2.5 million in fines and restitution.
According to Mone Petsod, a senior investigator in HCSC's SIU, the initial allegations against the allergist involved very small amounts of money. Even after the FBI contacted HCSC with more complaints from members, the case still had little momentum, he admits, acknowledging that in the past the case could have stalled. But by leveraging a joint solution developed in conjunction with IBM (Armonk, N.Y.) and SAS (Cary, N.C.), Petsod was able to uncover suspicious patterns during a data analysis.
"The allegations involved rather small sums of money, but when we looked into the data we found other issues with this same provider and her billing," Petsod recalls. "That's when the government began to investigate this case further."