As leaders of property/casualty insurance carriers, you are routinely exposed to conversation about predictive models, along with assertions that the benefits realized by some carriers in auto insurance pricing can be replicated in other lines.
Executive SummaryTo make informed decisions about adopting predictive models, insurers must ask how implementation costs compare with model benefits. The author proposes a statistic called Value Of Lift, or VoL, to quantify the value of predictive modeling.
In the personal automobile insurance marketplace, the early adopters of predictive analytics—the extraction of meaningful, actionable insights from raw data—have gained significant competitive advantages over the past decade. Most notably, these personal lines insurers have used predictive models to develop more refined pricing by introducing credit reports, detailed vehicle classifications, and other predictors to a rating process that previously relied on variables like territory, age, gender and marital status. But even in this line, where the application of predictive modeling is relatively mature, new data sources such as vehicle telematics are emerging to form a whole new level of analytics.
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