Valen Technologies, an advanced data and analytics provider for property/casualty insurers, today announced two new customers: AmeriHealth Casualty Insurance Company and the Arbella Insurance Group.

As part of the partnerships, the companies have integrated Valen’s InsureRight predictive analytics solution into each company’s underwriting practices for the workers’ compensation services. Additionally, these carriers will be utilizing features of the recently launched InsureRight 2.0 in an attempt to optimize their competitive advantage in the field and better serve their customers.

“Predictive analytics is quickly becoming a fundamental component to competitive underwriting,” said Eileen Currie, VP of Commercial Lines for the Arbella Insurance Group. “Leveraging advanced data and analytics incorporates science into the art of underwriting. Valen is helping our underwriters integrate predictive analytics into their decision-making to better understand how we select and price risk.”

Powered by two Valen predictive models, InsureRight provides a Risk Score and a Misclassification Score, both of which facilitate a quick turnaround time and positively impact carrier’s overall pricing accuracy. Both insurers are hoping that InsureRight will enable their underwriters to access predictive analytics data in order to better assess risk on each policy, and ultimately, achieve better results in a fluctuating market.

The new Risk Score feature identifies risks for new and renewed policies and provides a price indication; The Misclassification Score recognizes policies that are likely misclassified so that underwriters can take steps to prevent premium leakage.

New functionalities were also recently added to the InsureRight product in September. The update included Multi-State Scoring, which allows customers to score multi-state policies without needing to split the policies into single state inputs, and Class Fit Analysis, which provides greater insight into the impact of misclassification challenges and enforces consistency in underwriting questions related to class fit.

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