Tag Archives: Peggy Brinkmann

Milliman Pixel

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.

To learn more about Pixel, click here.

Ride sharing is opening up new business opportunities for auto insurers

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.

In their article “Ride sharing and insurance issues,” Milliman consultants Michael Henk and Peggy Brinkmann discuss the risks of insuring TNC drivers as well as the new opportunities for innovation. Here is an excerpt:

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.

Enhanced processes of mining unstructured data

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 video playlist

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.

Predictive analytics: Uncovering value in the data
• Milliman consultants Nancy Watkins, Matt Chamberlain, Peggy Brinkmann, and Sheri Scott consider how insurance companies can use predictive analytics to enhance their pricing and underwriting practices.

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.

Read these two articles to learn more about claims analytics:
Predictive analytics, text mining, and drug-impaired driving in automobile accidents
Distracted driving: Text-mining accident descriptions

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.

Predictive analytics: Uncovering value in the data

Predictive analytics enables companies to identify their best- and worst-performing customer groups by helping them enhance their decision-making processes.

In this short film, Milliman consultants Nancy Watkins, Matt Chamberlain, Peggy Brinkmann, and Sheri Scott discuss how predictive analytics can uncover value in new and expanding data sets. They also discuss how insurance companies can use the technology to improve their pricing and underwriting practices as well as increase profitability.

What do you want most from big data?

Big data is changing decision-making processes across many industries. In this new article, author Neil Cantle discusses how analyses of large data sets can be used to forecast business results or to learn about the interrelated factors that drive business.

Here is an excerpt:

So, what is big data all about? Well, it depends. To some, it is simply about applying new processing techniques that enable you to run queries over very large datasets. This can be a useful thing to do, but the key point is: “What questions does your analysis seek to answer?”

There are two main responses to this. First, is “prediction” – trying to find “reliable” similarities between the behaviors of some subset of factors in your “big” dataset and the outcome you want to “predict.” This can be useful if the relationships uncovered happen to make sense and persist over time. But it is always possible to find some variables somewhere which, for a period of time at least, behave similarly to the one you are interested in without having any real relationship between them whatsoever. Eventually that apparent relationship will disappear and your “predictions” are suddenly not very good, but you don’t know why.

The second type of analysis is arguably a more satisfying one – seeking “explanation” not just “prediction.” Studying large sets of information to learn about the underlying mechanism driving the outputs you see brings insight and meaning, helping you to find out more about “why” things are related, not just that they move in apparently similar ways.

For more perspective on some approaches making it possible to manage and extract value from big data, read Peggy Brinkman’s article “Why big data is a big deal.”