Attract(SM) Commercial

Accurately and consistently predict insurance loss

Attract(SM) Commercial is a series of proprietary insurance scores developed by LexisNexis using policy and loss data representing over $8 billion in commercial premiums. These scores were designed for the relative rank ordering of applicants and policyholders by claim frequency and/or loss ratio. They can be applied at the time of quote, underwriting and/or renewal.

Benefits

LexisNexis offers a suite of insurance scores specifically developed for underwriting, including:

  • Business insurance
  • Business owners insurance
  • Commercial automobile
  • Workers' compensation

Attract(SM) Commercial models can be fully disclosed to regulators and insurance carriers.

Get it now: for more information about Attract(SM) Commercial, please call 800.869.0751.

More Information

Literature

  1. Attract(SM) Commercial
  2. Attract(SM) for Business Owners Underwriting

Get it now: for more information about LexisNexis® Attract(SM) Commercial, please call 800.869.0751.

Contact Us

For more information about our solutions call 800.869.0751 or contact us online

LexisNexis® Attract Commercial does not constitute a “consumer report,” as that term is defined in the federal Fair Credit Reporting Act, 15 USC 1681 et seq.(FCRA). Accordingly, this version of the service may not be used in whole or in part as a factor in determining eligibility for credit, insurance, employment or another purpose under the FCRA. LexisNexis® Attract for Business Owners Underwriting is a consumer reporting agency product provided by LexisNexis Risk Solutions Inc. and may only be accessed in compliance with the Fair Credit Reporting Act, 15 U.S.C. 1681, et seq.

Due to the nature of the origin of public record information, the public records and commercially available data sources used in reports may contain errors. This product or service aggregates and reports data, as provided by commercially available data sources, and is not the source of the data, nor is it a comprehensive compilation of the data. Before relying on any data, it should be independently verified.

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