Machine learning (ML) is all the rage, oftendescribed as the new frontier in predictive analytics. While itshistory dates back to the 1950s with pioneering research on simplealgorithms, more recent developments in the 1990s paved the way forthe new wave of applications in artificial intelligence. Most agreethis technology will have a notable impact on the industries andthe jobs of the future. From a predictive modeling perspective,incorporating new data into evolving models allows the predictivepower to continually improve.

Industry implications

Insurance presents interesting and nuanced use cases whenapplying predictive models to assessing risk andpricing policies. Being a heavily regulated industry bringschallenges and the need to explain precisely how risk selection andpricing decisions are made. Also, consumers still want trustedadvisors (agents, direct insurers, etc.) to help them navigate theright insurance coverage for their needs. That means continuedhuman interaction in explaining coverage and pricing details.Particularly with the larger, more complex risks in commercialinsurance, it's clear that insurance analytics must go well beyonda 'one-size-fits-all' modeling technique.

Veteran data scientists in the insurance industry use a variety of techniques to develop predictivevariables and final model output. They understand that insurersare required to go beyond a simple understanding of model scores;regulators often require an explanation for the decisions andrecommendation made by the model, as well as a deeper understandingof why it makes the recommendations it does. Underwritersand claims adjusters also need the same context and transparency tohave confidence incorporating model recommendations into theirdecision making.

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