What do “predictive modeling” and “fraud scoring” have to do with arbitrations to recover personal injury protection (PIP) benefits? In some cases, the results generated from these fraud detection techniques may result in a stay of arbitrations, an insurance fraud complaint, a referral to the Office of the Insurance Fraud Prosecutor and an inquiry by the State Board of Medical Examiners. While government resources in tandem with insurers are developing highly sophisticated and collaborative methods to detect fraud, information acquired during the course of PIP proceedings are serving up fraud fodder for protracted and costly litigation under the New Jersey Insurance Fraud Prevention Act.

Increasingly, coordinated efforts between government, personnel and technology have enhanced the ability of insurers to develop predictive models of fraud behavior. Predictive modeling involves the review of data to detect statistical patterns in previously discovered fraudulent behavior and establishes models that assign fraud scores on new claims with similar characteristics and patterns. By way of example, insurance fraud investigators may be alerted to certain treatment routines by referring providers or providers treating the same family members multiple times per week for the same chronic injury. Predictive models are then created to target similar cases that have had a high statistical propensity for fraud in the past and provide data for investigators to “flag” suspect claims that follow that pattern. At the same time, innovative technology, including predictive modeling computer software, similar to that used by banking and telecommunications companies to spot fraud, has been approved by the federal government to analyze and target potential health care fraud in real time.

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