Valen Unveils New ‘Unavailable Loss History Model’ for Workers’ Compensation
Redefines Industry Standard in Predictive Modeling for Policies with No Information on Loss History
Prior loss experience has traditionally been one of the key parameters in evaluating the quality of the risk when an insurer is quoting a policy. Yet, insurers can score up to 70% of small premium policies without any loss information using models that aren’t built for these types of policies. Valen’s pioneering model, now in its fourth generation, delivers highly accurate predictions for insurance policies without any loss history information by employing a combination of third-party and synthetic variables derived from the robust
“With this latest solution, we are entering the next stage of industry innovation in predictive analytics,” said
According to Valen’s recent study, synthetic variables developed with Valen’s consortium data demonstrated up to 13 times more predictive power than variables built with policy-only data. Synthetic variables are built from computations of more than one variable and leverage the breadth and depth of experience in the consortium.
About Valen Analytics
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