BY ARA C. TREMBLY
The insurance industry in its virtually endless battle against fraudulent claims and other scams is increasingly relying on more sophisticated varieties of technology to winnow out crime, experts say.
Insurers, according to industry professionals, are turning to claims scoring technologies to evaluate potentially fraudulent claims, and to predictive modeling to help identify in advance the types of claims that may be suspicious.
Front-end fraud detection tools, for example, will flag claims at initial point of collection, then evaluate them based on a scoring mechanism early in the claims process, said Greg Powers, vice president of sales and business development for Innovation First Notice, based in Newton, Mass.
Such tools utilize a rules-based engine—a technology that gives carriers the capacity to automate a rule set (if x, do y), he explained. One rule might specify that if the claim is filed within 30 days of inception, it should be flagged and passed to the insurer’s special investigation unit.
“Or if the home address is a P.O. box, flag it; or put 30 or 40 scenarios into a scoring module and set a threshold by account, jurisdiction, line of business, and let the application automate the scoring of the claim,” he added.
“At the end of the phone call, I will have a score,” he noted. “It happens automatically up front. If you set a fraud threshold at a score of 50 and you get 60, it will be routed to the SIU.”
If a claim submitted via the Web is a fast-track claim and flags are raised, that may automatically jog a company to make the phone call to the applicant, Mr. Powers noted.
“If a claim is lighting up criteria that would lead to SIU attention, you may want to ask additional questions to further qualify referral to the SIU and to provide a potential warning to the applicant that if this is a suspicious claim, it may be cut off,” he added.
Predictive modeling adds capabilities to the benefits of rules-based engines, he noted.
“Modeling technology looks at more data sources and outside trends, as well as relationships and patterns among different data marts,” he said. “It’s like a giant funnel [through] which you pour variables into a system to take a closer look at the data, then statistically analyze it with algorithms that are not usually part of rules engine.”