LexisNexis Fails to Ensure Driving History Accuracy in Consumer Reports, Class Action Claims [DISMISSED]
by Erin Shaak
Last Updated on October 10, 2022
Contreras v. LexisNexis Risk Solutions, Inc.
Filed: February 10, 2021 ◆§ 1:21-cv-20577
LexisNexis has failed to implement reasonable procedures to ensure the accuracy of the information it includes in consumer reports, a class action claims.
October 10, 2022 – LexisNexis Driving History Class Action Dismissed After Mediation
The proposed class action detailed on this page was dismissed without prejudice following mediation between the parties that resulted in a settlement.
The parties submitted to the court on February 17, 2022 a mediation disposition report that says the case was “completely settled” and that a stipulation of dismissal was on the way. The plaintiff submitted to the court a stipulation of dismissal on March 22.
Details of the settlement were not immediately available via court documents.
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LexisNexis Risk Solutions, Inc. has failed to implement reasonable procedures to ensure the accuracy of the information it includes in consumer reports, a proposed class action claims.
According to the lawsuit, the consumer reporting agency’s automated procedures for gathering information do not include filtering processes to confirm individual consumers’ identities and ensure that records are matched to the correct person. As a result, the suit says, LexisNexis furnishes to auto insurers “tainted reports” that include inaccurate information about consumers while the individuals “have no automatic way of knowing” what information is being shared about them.
Once a mistake is made, it has a “cascading effect” whereby successive auto insurers will “rely on the original mistake” when issuing insurance and setting premiums, leaving a consumer with almost no recourse to correct the inaccurate information, the case in Florida federal court stresses.
The lawsuit claims LexisNexis has violated the Fair Credit Reporting Act by failing to implement reasonable procedures to ensure “the maximum possible accuracy” of the reports it furnishes about consumers.
According to the suit, LexisNexis is essentially a “data broker” who sells consumer information to companies looking to assess someone’s risk profile for the purpose of offering mortgages, insurance or other products. Despite the publicly available nature of the information, the defendant fails to “double-check” consumers’ identities by matching their Social Security number, address, middle name and driver’s license number, the lawsuit alleges.
The plaintiff, a Miami resident, says that a person with the same name as their son was involved in a January 17, 2020 car accident in Arizona for which three citations were issued. Upon renewing his automobile insurance with Progressive Direct Auto the following June, the plaintiff discovered his consumer report incorrectly associated the aforementioned car accident with his son, which caused his monthly premium to increase by $256, the suit relays. Upon investigation, the plaintiff was told that the inaccurate information was supplied to Progressive by LexisNexis, the case says.
The plaintiff says he was told any correction to the information had to come from LexisNexis. After the plaintiff invoked the defendant’s correction procedure, which took months, the defendant failed to correct its mistake, despite being presented with documentary evidence that it had ascribed the accident to the wrong person, the suit alleges. “Compounding its error,” LexisNexis told the plaintiff that it could not correct the information provided to his insurer unless the insurer requested it, the case says.
Although the plaintiff changed his auto insurance after his six-month policy ended, his premiums were still $96.83 more per month than the amount charged by Progressive before the inaccurate information was reported by LexisNexis, according to the complaint.
“LexisNexis’s mistake costs Plaintiff at least $256 every month for the six months he remained with the subject automobile insurer,” the complaint states. “Because the mistake is ‘released,’ there is no way to know how often, and to what effect, the mistake will be repeated by data merchants.”
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