Insurance Networking News

IN ADDITION TO THE USUAL WITCHES’ brew of hard fraud, soft fraud, staged accidents and slip-and-fall fraud rings, insurance carriers must contend with increasing instances of fraud borne of a policyholder’s adverse financial circumstances. The home-owner or SUV owner seeking to get out from under a loan with a little help from a can of kerosene is far from apocryphal. “Fraud has historically been committed out of acts of greed,” says David Rioux, VP of corporate security and the special investigative unit (SIU) for Erie, Pa.-based Erie Insurance. “Now, given the economic situation we’re in, fraud is being committed out of acts of desperation.”

Perhaps as troubling as the fraudulent claims carriers manage to detect, are the frauds that escape detection.

“The funny thing about fraud is that nobody knows how much there really is,” notes Donald Light, a senior analyst at Boston-based Celent. “Insurers only know how much they have found—they don’t know how much they haven’t found.”

When it comes to fraud detection, experience is often the best teacher. “Insurers are always getting better at it, but, by nature, you almost have to be burned before you find the fire,” adds Robin Harbage, C-counsel consultant at San Diego-based EMB in North America.

While the totality of insurance fraud is likely unknowable, new data from the Des Plaines, Ill.-based National Insurance Crime Bureau (NICB) offers one way of quantifying the problem. NICB data encompass-es property/casualty, commercial and vehicle data, but the most pronounced surge occurred in auto claims, as suspicious car fires were up 20% from last year, suspicious auto glass claims up 76% and “phantom” accidents were up 46%.

In fact, for the first half of 2009, NICB data shows increases in nearly all referral categories compared with the first half of 2008. Overall, a total of 41,619 questionable claims were referred to NICB for closer review and investigation by member insurance companies in the first half of 2009, compared with 36,743 received during the same period a year earlier.

This upswing in fraud is not limited to the property/casualty sector. Workers’ compensation claims can swell as workers anxious about layoffs file claims, aware that workers’ compensation benefits are generally larger and longer-lasting than unemployment benefits.

So how do insurers stem this tide? By making better use of their most abundant and critical asset: data. Using predictive analytics to fight claims fraud is not new.

Once the province of the well-heeled, the proliferation of predictive analytics technologies means now most any insurer has the financial and technical ability to bring these solutions right to their desktops. While the solutions have improved significantly in the past five to 10 years, sporting better graphic interfaces and greater use of visual link analysis to aid in the identification of suspicious claims, they are beginning to plateau. “From a pure technology perspective, there is not much new in the last few years,” acknowledges Light.

THE HUMAN FACTOR

Absent technological leaps forward, how are insurers advancing the use of predictive analytics to fight fraud? One strategy is to use predictive analytics to augment human decision-making, or make better use of human capital. As analytic models shift from batch mode to real-time, they can help with areas such as adjuster assignments. For example, an experienced adjuster could be assigned to an accident scene while remnants of the accident are still present.

“I look at predictive analytics as a data watchdog—a guide for adjusters and investigators to be on the alert to critical data,” says Gregory Melanson, manager, special investigation unit, for Warwick, R.I.-based MetLife Auto & Home. “You have finite resources, so you only want to investigate files that really need it.”

Melanson says it is important to synthesize information culled from analytics with human interpretation. Insurers can integrate predictive analytics with existing business rules, such as special consideration for any claims that occur within the first two weeks of a new policy. “You are not going to pay three years’ worth of premiums and then make a claim,” Harbage says. “Many fraudsters will make a claim before the check clears or bounces.”

While Melanson says the insurance industry is not trailing fraudsters, he sees areas where insurers can be more proactive. For example, incorporating existing technologies such as geo-coding can help investigators ferret out fraud, he says. “It may prompt an investigator to ask: Why would someone go to a medical clinic 50 miles away?”

Yet, just because a model flags a claim it doesn’t necessarily mean the claim is fraudulent. Analytics can highlight exonerating behavior in a seemingly suspicious claim as well, saving the adjuster the time and aggravation of following a false lead. Analytics works best when paired with solid claims management and

diligent investigative techniques. Melanson stresses that the technology is not an exact science, and companies need to guard against becoming over-reliant on analytics. “Often there are mitigating factors that are not in the data,” he says.

Likewise, Rioux says predictive analytics work best as a conduit to the information needed by adjusters and investigators to make informed decisions. “From the first notice of loss throughout the entire life of the claim, we continuously score the claim, and there is a threshold we set,” he says. “When it breaks that threshold, a notice will go to that claim handler.”

Rioux says analytic models now account for one-third of referrals to SIU. “That’s actually a very good number,” he says. “The reality is there are some adjusters that don’t benefit as much from predictive analytics; they find these things off gut feeling.”

One area where Rioux concedes humans come up short is in consistency. Automation breeds consistency, he says. While adjusters and investigators can suffer from workload or training issues, predictive models do not. “It levels the playing field across an organization,” he says. “One thing about technology is that it doesn’t have good days or bad days. Our claims are being scored consistently across the organization.”

THE NOTES

Oddly, one bountiful source of predictive information—the copious amount of notes created by adjusters—has only recently been incorporated in predictive models. Traditionally, carriers would use structured data such as historical records of known claims fraud, plus policy and public data to build their predictive models. Presently, steady advances in text mining capabilities have made this once-underutilized asset another primary feedstock for analytics. Rioux says Erie now incorporates adjuster notes into its predictive models, which were originally built using hundreds of predefined data fields such as names and addresses.

“One of the more recent additions to our solution was the introduction of adjuster loss notes because text mining is huge,” says Rioux, who is also president of International Association of Special Investigators, Baltimore. “The notes tell the story. Data fields just hold pieces of information. Being able to read notes, and organize them in a fashion useful for the model, has really given predictive analytics a substantial lift.”