Milliman today announced a new innovation in the insurtech space: AccuRate Fleet, a telematics-based risk score created with Azuga, Inc. to help improve commercial auto insurer profitability.
Milliman teamed up with Azuga, a leading provider of connected vehicle and fleet technology, to study how fleet driving behavior coupled with actual accident data can lead to predictive models for commercial auto insurers. Using 1.5 billion miles of Azuga commercial auto driving data and 5,700 accident reports, Milliman modeled the indicators of crash frequency and created a risk index to help insurers, managing general agents (MGAs), and start-ups in the commercial auto space price risk better.
“Commercial auto insurers have faced years of worsening combined ratios, and with this product we strongly feel that we can guide insurers to assess and price risk more accurately,” said Peggy Brinkmann, a principal at Milliman and codeveloper of AccuRate Fleet. “There’s an opportunity here for those in the commercial auto space to use existing and widely accepted technology and optimize their risk pools quickly.”
“Commercial auto has become an increasingly challenging market for building a profitable customer base and insurers can’t simply keep raising rates,” said Ananth Rani, cofounder and CEO of Azuga. “Azuga Fleet telematics has demonstrated significant reductions in accident frequency and severity at scale. The AccuRate Fleet score from Milliman further cements our leadership in delivering results both to the insured fleets and now the insurance carriers.”
Today actuaries and insurers are able to apply predictive analytics in novel ways because of advanced technologies, larger data sets, and increased computing power. A recent Risk & Insurance article featuring Milliman’s Peggy Brinkman and Phil Borba explores four key areas where advances in predictive analytics are changing the way insurers conduct business: claims, driving safety, property risk, and competitive rating.
Milliman’s Pixel is a web-based, competitive analytics platform that helps insurers use objective and comprehensive information to grow their business.
In this video, Milliman actuaries Nancy Watkins, Peggy Brinkman, and Cody Webb discuss how Pixel helps insurers compare their premiums with those of competitors, identify market sectors where they might be experiencing adverse selection, and access competitive information needed to make sound pricing decisions.
Transportation network companies (TNCs), better known as ride-sharing services, such as Uber, Lyft, and Sidecar, have created challenges for regulators and insurance companies. However, as cities and states work on governing this new mode of transportation, insurers should take advantage of the opportunity to develop new products covering ride-sharing drivers.
One of the central ambiguities in ride sharing is the question of who pays when there is an accident… To answer the coverage question, it is helpful to break down the vehicle use into four different categories:
1) Ride sharing driver isn’t logged in to the ride sharing service and is thus not available for hire.
2) Ride sharing driver is logged in to the ride sharing service and is available for hire, but has not yet found a passenger.
3) Driver and passenger have confirmed a ride share and the driver is en route for pickup.
4) Ride share is in progress.
There is general agreement that accidents arising in the first category would be covered by the personal auto policy, but the second category has been more controversial…
Even insurers that are denying claims related to ride sharing are concerned about covering the personal usage of vehicles also used for ride sharing…
Without access to standard personal auto coverage, though, ride-share drivers are in need of new products and/or rating plans. Today’s typical rating plan varies rates by pleasure, work, and business usage, and mileage is difficult to capture accurately. To address concerns that the ride-share vehicle personal usage risk might be higher than expected under current rating plans, carriers could introduce a new variation of business usage for ride-sharing vehicles. Carriers could also perhaps require ride-sharing vehicles to enroll in usage-based rating programs in order to ensure the most accurate data about vehicle usage to get appropriate rates for the personal use of these vehicles.
Innovative analytical tools and high-performance computing are providing insurers the means needed to analyze huge volumes of unstructured data. In this Risk.net article (subscription required), Milliman’s Neil Cantle discusses how these advances offer carriers a more sophisticated approach in analyzing inherent risks and developing best business practices.
Here is an excerpt:
Many of the new generation of tools for unstructured data were initially developed to enable search engines such as Yahoo and Google to tackle the vast resources of the web. Key among these is the Hadoop framework for the management and processing of large-scale disparate datasets on clusters of commodity hardware. Hadoop has a number of modules for such things as distributing data across groups of processors, filtering, sorting and summarizing information, and automatically handling the inevitable hardware failures that arise in large computing grids. All of the technologies mentioned are open source, which means they are free and readily available, and they are also supported by many proprietary commercial extensions and equivalents.
The breakthrough with new data sources and tools is the ability to query things for which the data has not been organized in advance. This can reveal new patterns, trends and correlations that can be helpful in managing risk and spotting opportunities, says Neil Cantle, principal and consulting actuary at Milliman, based in London.
… “[The new data capabilities] enable insurers to look more broadly and deeply into the world in which the policyholder lives without necessarily being specific about the person, and allow them to start making inferences about an individual and their behavior,” says Cantle.
The article also focuses on the emergence of data scientists who are entrusted with mining new data sources. Milliman’s Peggy Brinkmann expounds on data science and the techniques data scientists use to extract value from large amounts of information in her paper “Why big data is a big deal.”
Predictive analytics enable organizations to identify their most profitable and expensive customer groups. These tools analyze business data and processes to help executives make informed decisions. The following videos highlight Milliman’s predictive analytics solutions.
Hurricanes and analytics: A 21st-century approach to pricing
• Matt Chamberlain discusses how geographic information systems (GIS) can be used to price hurricane risk. To learn more about how geocoding can lead to more accurate pricing, read this article.
Improving claim analytics through text mining
• In this video, Phil Borba explains how text mining can reveal valuable information hidden in the narratives of auto insurance claims that could lead to improved underwriting practices.
Milliman Datalytics-Defense: A new approach to understanding defense costs
• Milliman Datalytics-Defense analyzes data related to litigation costs to help businesses develop more effective claims defense strategies. Milliman actuary Chad Karls offers perspective in this video.
For more information about Milliman’s predictive analytics solutions, click here.
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