In Europe, more countries are now offering telematics services such as Pay As You Drive (PAYD), where drivers can benefit from lower premiums if they drive less, and Pay How You Drive (PHYD), which rewards “good” drivers. As new products and services emerge, it’s important for motor insurance companies to know how to extract information to deduce driving habits from telematics data. This article by Milliman’s Rémi Bellina, Antoine Ly and Fabrice Taillieu explores the technological choices and opportunities telematics provide insurers. It also explains how insurers can process data to detect driving behaviour based on projects led by Milliman’s analytics team.
Insurance customers expect personalized, agile, and on-demand delivery from carriers nowadays. Insurers must keep up with technological advances and implement them to provide solutions that address these expectations. In her Best’s Review article “Mind your ABCs,” Milliman’s Pat Renzi explores why insurance companies must center their strategic initiatives on using emerging technology like artificial intelligence (AI), blockchain, and the cloud. She also explains how partnerships that feature diverse experts will see faster, smarter, and more successful disruption.
Parsing a large computational process into smaller independent tasks that run in parallel to each other can help actuaries benefit from the time-saving efficiencies of cloud computing. Machine learning has parallel compute capabilities to assist with these tasks. In this article, Milliman’s Joe Long and Dan McCurley discuss how they were able to cut a three-month machine learning project down to four days using open source tools and the Microsoft Azure cloud.
There is high-growth potential for insurtech startups in the program business market. As a result these emerging companies are increasingly seeking to partner with or develop their own managing general agents (MGAs). While challenges exist, the morphing of insurtech startups into MGAs can be successful if done properly.
In his article “Getting with the program: Insurtech startups seek MGA success,” Milliman’s Tom Ryan outlines several recommendations that provide key considerations for insurtech businesses looking to develop program business through an MGA.
Here is an excerpt:
How can insurtech startups improve their chances for MGA success?
For insurtech startups looking to become MGAs, there are a number of ways to improve a company’s chance for success. Recommendations, based on past MGA successes, include the following:
• Find a friend. The surest way for an insurtech startup to enjoy the benefits of program business is to partner with an existing MGA and add their competitive advantage to an existing program. One problem with this approach is that it may be difficult to convince an already successful MGA that changes should be made and that it should experiment with an alternative process. The existing MGA may also have a different vision on how to best expand the business and scale up when compared to the insurtech startup and its investors.
• Do the homework. To overcome a lack of historical experience, extra care and consideration should be incorporated into creating a detailed business plan for the new MGA. The business plan should include a description of the market intended to enter, a list of competitors (along with a competitive premium analysis), and a description of the proposed technological competitive advantage and its impact on projected results. A key element of the plan should be a financial model which includes reasonable, industry-based assumptions to show potential carriers what the business results might look like under various scenarios. If the expected results are reasonably justifiable, the proposed plan will gain credibility with potential carriers despite a lack of hard data.
• Add an IPI. A valuable key addition to any new MGA, particularly one primarily staffed by those with a technology focus, would be an insurance professional interface (IPI). Similar to an application programming interface (API), an IPI, such as an experienced underwriter, program manager, or actuary, would allow new tech-based MGAs to better communicate their vision to insurance industry veterans on the carrier side. The right IPI can bring instant credibility to the proposed MGA and its business plan. This will shift the focus from technology (which the insurtech team will tend to focus on) to the expected insurance business results (which is what carrier audience will focus on). An experienced IPI can help with the business plan and financial model creation. The IPI will also be an asset when navigating through changes in the market cycle which have not been experienced in recent years, such as the advent of a hard market.
Milliman has announced that gradient A.I., a Milliman predictive analytics platform, now offers a professional employer organization (PEO)-specific solution for managing workers’ compensation risk. gradient A.I. is an advanced analytics and A.I. platform that uncovers hidden patterns in big data to deliver a daily decision support system (DSS) for insurers, self-insurers, and PEOs. It’s the first solution of its kind to be applied to PEO underwriting and claims management.
“Obtaining workers’ compensation insurance capacity has been historically difficult because of the lack of credible data to understand a PEO’s expected loss outcomes. Additionally, there were no formal pricing tools specific to the PEO community for use with any level of credibility—until gradient A.I. Pricing within a loss-sensitive environment can now be done with the science of Milliman combined with the instinct and intuition of the PEO,” says Paul Hughes, CEO of Libertate/RiskMD, an insurance agency/data analytics firm that specializes in providing coverage and consulting services to PEOs. “Within a policy term we can understand things like claims frequency and profitability, and we can get very good real-time month-to-month directional insight, in terms of here’s what you should have expected, here’s what happened, and as a result did we win or lose?”
gradient A.I., a transformational insurtech solution, aggregates client data from multiple sources, deposits it into a data warehouse, and normalizes the data in comprehensive data silos. “The uniqueness for PEOs and their service providers—and the power of gradient A.I.—emerges from the application of machine-learning capabilities on the PEOs’ data normalization,” says Stan Smith, a predictive analytics consultant and Milliman’s gradient A.I. practice leader. “With the gradient A.I. data warehouse, companies can reduce time, costs, and resources.”
For more on how gradient A.I. and Libertate brought predictive analytics solutions to PEOs, click here.
The group life and disability insurance sector has been slower to adopt predictive analytics than other lines of insurance. One reason for the sector’s lag is because insurers often have limited information on who they are insuring. However, there are still many ways to incorporate predictive modeling technology to improve results. Milliman consultant Jennifer Fleck provides some perspective in her article “Group insurance ‘Project Insight’.”