AttractTM for Commercial

More accurate and consistent loss prediction through predictive modeling for commercial insurance

Rank order a business, business owner or commercial driver by their loss propensity at time of quote, underwriting or renewal.


Address Your Needs
Access insurance scores developed for specific underwriting sectors—including business insurance, business owners insurance, commercial automobile and workers compensation.

Become More Profitable
Decrease your loss ratios, improve your expense ratio and maximize your risk insight at time of quote/rating.


Make Better Business Decisions
Benefit from predictive modeling for commercial insurance based on scores that are relatively ranked for loss propensity.

Leverage Robust Data
Access proprietary insurance scores built and validated on policy and loss data representing over $8 billion in commercial premiums.

Be Flexible
Leverage predictive analytics for commercial insurance rating and/or underwriting with scores that can be used off the shelf or incorporated into a propriety model.

LexisNexis Attract Commercial , LexisNexis Attract for commercial auto underwriting and LexisNexis Attract for business owners underwriting (non-FCRA) do not constitute a “consumer report,” as that term is defined in the federal Fair Credit Reporting Act, 15 USC 1681 et seq.(FCRA). Accordingly, these services 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 (FCRA) 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. These products or services aggregate and report 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.