Tag Archives: telematics

Milliman launches AccuRate Fleet at InsureTech Connect 2020, introducing new telematics-based risk score for commercial auto insurers, MGAs, and start-ups

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.”

To read more about AccuRate Fleet, click here.

To subscribe to Milliman’s insurtech updates, contact us here.

Milliman named “Service Provider of the Year” for fifth consecutive year by Middle East Insurance Review

Milliman is pleased to announce it was named “Service Provider of the Year” for the fifth consecutive year at the Middle East Insurance Review’s 2018 Middle East Insurance Industry Awards (MIIA).

Citing Milliman as an “undefeated contender” in this category, the award recognizes the firm’s market innovation in the digital space and leadership as it pertains to education and research across the Middle East’s insurance and reinsurance industry. Specific achievements include creating a digital consulting team to engage with the market on innovative products such as telematics and machine learning, as well as helping the industry prepare for the adoption of International Financial Reporting Standard (IFRS) 17. Milliman also received accolades for helping develop the first-ever mortality study in the Middle East region.

At Milliman we are passionate about the work we do, and strive to continuously innovate and educate in order to serve the industry and people across the Middle East and North Africa (MENA). We are honored to receive this award for the fifth year in a row, and thank the Middle East Insurance Review and the esteemed panel of judges for the recognition.

The MIIA, organized by Middle East Insurance Review (MEIR), was launched in 2014 to recognize and salute excellence in the MENA insurance industry, helping to boost standards and promote greater professionalism in the market. A panel of 14 distinguished judges across the industry assessed over 300 nominations, comprised of insurers, brokers, risk managers, service providers, and industry leaders.

For more information about Milliman’s work in the United Arab Emirates (UAE), click here. For more information about the awards, click here.




Telematics enhancing motor insurance data in Europe

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.




Telematics and UBI: How to gauge ROI?

How can insurers understand the return on investment (ROI) of telematics and usage-based insurance (UBI) programs? The right program design can reduce costs and positively impact revenue. In Europe, some UBI programs market add-on services aligned to the needs of insurers’ customers which can generate revenue. In a blog post entitled “Making the business case: Telematics investment for UBI,” Milliman’s James Dodge offers an approach for insurers to consider.




Telematics and machine learning can help insurers employ usage-based insurance

Telematics can help auto insurers implement usage-based insurance (UBI) by obtaining valuable data related to individuals’ driving behaviors. However, producing actionable information through generalized linear model (GLM) methods has been difficult for auto insurers. Machine learning techniques provide insurers with a better way of analyzing big data.

Milliman consultants Marcus Looft, Scott Kurban, and Terry Wade provide perspective in their article “Usage-based insurance: Big data, machine learning, and putting telematics to work.”

For auto insurers, machine learning holds the promise of enabling carriers to explore hundreds, if not thousands, of factors involved in calculating the potential risk of individual customers. Moving beyond GLM and introducing machine learning techniques with telematics data may enable insurers to leverage key competitive advantages.

UBI pricing necessarily supersedes traditional GLM methods because the complex interactions of the factors in play require machine learning to uncover them within a reasonable timeframe in order to be cost-effective. Pricing differences often cannot be fitted by GLM distributions. And correlations between telematics and non-telematics effects will tend to disturb the clarity of results in a single GLM. Distribution over different frequency and severity models only confuses the analysis of differences within telematics policies.

GLM techniques will thus always show how a business differs in terms of its dependencies on a limited set of specific factors, such as age, or how much mileage goes on a car, or other very high-level factors. But if a whole model is taken and everything modeled together to try to understand the risk for all of the policies, in the end there tends to be only a mixed bag of different effects. An insurer still hasn’t been able to look very deep into its business.