Valen Unveils New ‘Unavailable Loss History’ Model for Workers’ Compensation
DENVER, July 17, 2019 – Valen Analytics, an Insurity company, and provider of proprietary data, analytics and predictive modeling for P/C insurers, today announced the latest in its suite of predictive models with the launch of the Unavailable Loss History Model. The first-to-market model enables accurate scoring of insurance policies that have 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 Valen Data Consortium. The model is built and tested against a large and granular data set of approximately 650,000 policies presenting $7.6B in premium. By leveraging this diverse data set with 10 years of loss experience, the new model is finely tuned to answer the question “How do similar policies perform?” as an indicator of risk quality.
“With this latest solution, we are entering the next stage of industry innovation in predictive analytics,” said Kirstin Marr, President of Valen Analytics. “Being able to deliver valuable insights despite the absence of a key piece of information like loss history is a true testament to the power of partnering around data. As insurers pursue the small commercial market, the ability to accurately assess risk, while collecting less information on the application is crucial to gaining market share. We are confident that we will continue to develop such impactful solutions as our consortium scales.”
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. The Valen Data Consortium, which is comprised of data from dozens of third-party data sources and more than 60 insurers, has significantly grown in the past year, with 57% growth in Workers’ Compensation and 266% in Commercial Auto. This momentum allows Valen to better serve insurers with more accurate predictive insights needed for real-time decision making.
About Valen Analytics
Valen Analytics, an Insurity company, provides proprietary data, analytics and predictive modeling for property and casualty insurers. We work with insurers who are actively looking to utilize modern approaches to pricing, risk selection, claims triage, and premium fraud. Our customers are focused on increasing competitive pressures, fighting adverse selection with innovative solutions, and raising awareness for the impending “experience gap” with initiatives such as Insurance Careers Movement. Our customers span many lines of business including Homeowners, Personal Auto, Workers’ Compensation, Commercial Auto, Commercial Package, Commercial Property, and BOP. Valen integrates with several Insurity products and solutions, including DataHouse and Policy Decisions platforms, enabling improved data management and data-driven decision making. Learn more about Valen at www.valen.com.
Insurity, Inc. enables property & casualty insurers to transform their enterprise and achieve their business goals. Insurity’s core processing applications and data integration and analytics solutions are backed by rich insurance expertise and are in production with over 200 insurers, processing billions of dollars of premium each month. Insurity’s solutions address the needs of all carriers – from the Top 20 insurers to small or regional commercial, personal, or specialty lines writers, as well as MGAs. For more information, visit www.insurity.com.
Source: Valen Analytics