Product pricing or ratemaking is the process of establishing rates charged by an insurer for accepting the risk. Insurance companies, and specifically actuaries, rely heavily on using historical data to predict future behavior or create premium rates to price products. They are now using approaches like high-performance analytics to not only speed the entire process but also capitalize on all available data. Consider these “before and after high-performance analytics” examples:
BEFORE: Actuaries relied on univariate or one-way analysis for pricing and monitoring price efficiency.
AFTER: More insurers are using advanced analytical techniques such as generalized linear modeling.
BEFORE: Actuaries relied on using a subset of historical data to run pricing models because the time it takes to prepare the data and run the models was too time-consuming.
AFTER: To combat these problems, insurers are now turning to high-performance analytics to provide faster processing on the growing volumes of available data.
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NOTE: Originally published on the Business Analytics Knowledge Exchange as part of the High-Performance Analytics Series.