Machine learning (ML) is all the rage, often described as the new frontier in predictive analytics. While its history dates back to the 1950s with pioneering research on simple algorithms, more recent developments in the 1990s paved the way for the new wave of applications in artificial intelligence. Most agree this technology will have a notable impact on the industries and the jobs of the future. From a predictive modeling perspective, incorporating new data into evolving models allows the predictive power to continually improve.

Related: The power of analytics for insurance: You ain't seen nothin' yet

Industry implications

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

Veteran data scientists in the insurance industry use a variety of techniques to develop predictive variables and final model output. They understand that insurers are required to go beyond a simple understanding of model scores; regulators often require an explanation for the decisions and recommendation made by the model, as well as a deeper understanding of why it makes the recommendations it does. Underwriters and claims adjusters also need the same context and transparency to have confidence incorporating model recommendations into their decision making.