Tunde Panaki | August 17, 2017

How Predictive Analytics Can Change the Insurance Game 

The Intelligent Future

Since the industrial revolution, technology has been part of our daily lives and the threat of robots “taking over the world” has scared generations into working harder and smarter. Jobs performed by “metal people” have not yet come to fruition, but the fate lingers and the fear remains as technology moves closer to things once unimaginable.

For years children grew up watching the Jetsons, a show about living in space with aircraft-like cars and the beloved Rosie, a robotic woman that helped with caring for the children and cleaning the house.

Typically, when people think of robots they assume a human-like machine with arms, legs, and a head. But technically, a robot is a device capable of carrying out a complex series of actions automatically. Going by this definition, we can easily see that various industries today employ some form of robotics to attain goals once thought impossible. In the insurance industry, for example, a prevailing use of robotics is in the field of predictive analytics and machine learning, especially around claims.

Evolution from Actuarial to Predictive

Predictive analytics and machine learning in the insurance industry is quickly growing from a novelty to necessity. Analytical models allow insurers to train, score, and automatically evaluate data sets to provide more meaningful insights. According to Property & Casualty 360, “unlike traditional actuarial analysis which relies heavily on assumptions and usually lags in relevance, predictive analytics provides a more robust, scientific model of past and present information for insight into real-time claims processing decisions.”

With escalating rates of claims leakage, rising processing costs and increased electronic communications, insurers understand the need to minimize errors by streamlining the claims process. Insurers are now moving away from relying on manual processes to implementing automated, independent, real-time analytics to ensure the integrity of their claims processes. Those insurers who have introduced predictive analytics into the claims process know that predictive analytics is an absolute game changer that provides a multitude of benefits.

Benefits of Predictive Analytics in the Insurance Industry 

Predictive analytics has a wide array of benefits for insurers. By analyzing data from several sources, predictive analytics can provide insurers with data that is specifically tailored around each policyholder, including life expectancy, likelihood to renew, likelihood to file a claim, etc. All of these are valuable pieces of information to help design the right products for each policyholder. Since policyholders are humans that continue to grow, the power of predictive analytics is that it can be trained to automatically incorporate changing circumstances.

Also, with predictive analytics, a claims manager can get unprecedented insights into claims such as: what is the likelihood that a claim with a defined threshold is fraudulent or how to detect low risk claims that would be more profitable to pay off without human interaction. However, to fully harness the power of predictive analytics, insurers will need to evolve people skills, process capabilities, and technology tools in order to fully capitalize and steer the company in a new direction based on predictive insights.

Identifying the Right Predictive Analytics Solution

To leverage predictive analytics, insurers need an all-inclusive, end-to-end predictive analytics platform. The platform should be built to handle not one, but rather many steps from data ingestion and preparation to analysis and operationalization, in real-time. The platform should also be able to dissect and analyze vast amounts of data.

The platform should apply machine-learning algorithms for segmentation, classification, recommendation, regression and forecasting. Any user should then be able to easily create reports and dashboards to visualize the results and collaborate across teams.

To learn more about  predictive analytics, check out this data sheet.

Download the Data Sheet

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