Tag Archives: predictive analytics

BETA Healthcare Group teams with Milliman Datalytics-Defense to develop data-driven defense strategies

Milliman today announced that BETA Healthcare Group has chosen Milliman Datalytics-Defense® as its platform for processing its defense cost invoices. Datalytics employs powerful data mining algorithms to help insurers detect patterns in attorney billing practices, delivering better understanding of both costs and defense strategies.

“It’s BETA’s goal to find innovative strategies to mitigate risk and reduce costs when it comes to managing our business,” says Tom Wander, CEO of BETA Healthcare Group. “Partnering with Milliman Datalytics-Defense helps us make more well-informed, data-driven decisions in our defense strategies, something we believe is essential to remain a leader in the industry.”

Milliman Datalytics-Defense provides real-time, actionable intelligence through responsive reporting dashboards that are built upon a robust data warehouse. The web-based tool is available on a subscription basis and can perform peer comparisons, allowing insurers to credibly benchmark their defense costs. The tool’s predictive analytic engine helps insurers develop best practices of claims defense.

“BETA has a reputation as a forward-thinking organization that knows success in the industry today must include cutting-edge tools such as Datalytics to manage claims effectively and efficiently,” says Chad C. Karls, Milliman principal and consulting actuary. “We look forward to working with BETA to identify claims handling strategies that will benefit not only BETA, but ultimately their members/insureds.”

To learn more about Milliman Datalytics-Defense, click here.

Constellation’s choice of Milliman Datalytics-Defense puts them at the forefront of claims defense management

Milliman today announced that Constellation has chosen Milliman Datalytics-Defense® as its platform for processing its defense cost invoices. Datalytics employs powerful data mining algorithms to help insurers detect patterns in attorney billing practices, delivering better understanding of both costs and defense strategies.

“With Milliman Datalytics-Defense we are excited about being able to extract additional intelligence from our defense cost data in order to assist us in making informed, data-driven decisions in our defense strategies moving forward,” says Steve Lacke, Constellation Chief Operating Officer. “While the MPL industry has experienced an unprecedented drop in frequency since the early 2000s, the cost of defending claims has not gone down commensurately. When we went to market to find a new tool to assist us in managing that cost curve, Datalytics was clearly different, and showed the greatest promise for us.”

Milliman Datalytics-Defense provides real-time, actionable intelligence through responsive reporting dashboards that are built upon a robust data warehouse. The web-based tool is available on a subscription basis and can perform peer comparisons, allowing insurers to credibly benchmark their defense costs. The tool’s predictive analytic engine helps insurers develop best practices of claims defense.

“We look forward to working with Constellation to identify cost-effective claims handling strategies by unlocking the information that is hidden in their defense costs invoices and turning it into actionable data,” says Chad C. Karls, Milliman principal and consulting actuary. “Constellation has a reputation of being forward-thinking and we believe that this move puts them at the forefront of ALAE management—something the entire MPL industry has struggled with for over a decade now.”

To learn more about Milliman Datalytics-Defense, click here.

NORCAL Mutual chooses Milliman Datalytics-Defense® to help improve claims handling with data-driven decision support

Milliman today announced that NORCAL Mutual has chosen Milliman Datalytics-Defense® as its platform for managing its defense costs. Datalytics employs powerful data mining algorithms to help insurers evaluate costs associated with defending claims in order to identify opportunities to improve results.

“We chose Milliman Datalytics-Defense because of its ability to provide an efficient approach to managing defense costs while increasing our ability to turn data into information…,” says Timothy J. Friers, NORCAL Mutual Senior Vice President and Chief Operating Officer. “The tool allows us to gain greater insight into our results and improve our claims defense strategies for the benefit of our insureds.”

Milliman Datalytics-Defense provides real-time, actionable intelligence through responsive reporting dashboards that are built upon a robust data warehouse. The web-based tool is available on a subscription basis and can perform peer comparisons, allowing insurers to credibly benchmark their defense costs. The tool’s predictive analytic engine helps insurers develop best practices of claims defense.

“Milliman Datalytics-Defense uses advanced text mining to unlock information that was previously hidden in defense costs invoices,” says Chad C. Karls, Milliman Principal and Consulting Actuary.

To learn more about Milliman Datalytics-Defense, click here.

Reading list: Florida’s private flood insurance market

Advances in catastrophe models and new state insurance regulations have opened the door for an affordable, risk-based private insurance market in Florida. This reading list highlights articles focusing on various issues and implications related to the market. The articles feature Milliman consultants Nancy Watkins and Matt Chamberlain, whose knowledge and experience is helping insurers to understand and price flood risk more precisely.

Forbes: “The private flood insurance market is stirring after more than 50 years of dormancy
The reemergence of private flood insurance has piqued the interest of carriers seeking to enter the market. Some catastrophe (CAT) modeling companies are creating flood models to help insurers price policies. Here’s an excerpt:

Nancy Watkins, a principal consulting actuary for Milliman, likened the current level of interest from insurers to enter the private flood insurance market to popcorn.

“We are at that stage where you can hear the space between pops. You can hear one kernel at a time,” she said. “What I think is going to happen is, in one to two years, there’s going to be a lot more going on.”

Bradenton Herald: “Important for homeowners to compare flood insurance options
Florida homeowners must consider the issues related to the National Flood Insurance Program (NFIP) and private flood policies. Private insurers can use predictive modeling technology to determine a home’s distinct flood risk.

Tampa Bay Times: “Remember the flood insurance scare of 2013? It’s creeping back into Tampa Bay and Florida
Real estate and insurance experts comment on the possible effects that high flood insurance rates may have on homeowners. Insurers express interest in the granular modeling of flood-prone territories.

Tampa Bay Business Journal: “Why some Tampa Bay property insurers are offering flood coverage and others are not” (subscription required)
Insurers need to weight the risks and rewards associated with the underwriting of flood insurance. A few carriers have already decided to participate in Florida’s private flood insurance market.

Predictive analytics can make playing daily fantasy sports a homerun

While most daily fantasy sports (DFS) players usually swing and miss, big data management and predictive analytics have the capacity to increase a player’s chance of winning more consistently. In this article, Milliman’s Michael Henk and Nicholas Blaubach discuss the monetary success that some advance modelers are having on DFS websites using predictive analytics. The following excerpt highlights the steps necessary to build a DFS predictive model.

There are some basic steps that serve as general “rules of thumb” when we set out to develop our predictive model to make us millions in DFS.

First, we need an objective. We want our model to optimize our roster, giving us the most potential points. In our DFS example, we’d want a predictive model that will help us identify the best players for the cost (in order to stay under the salary caps) for any given contest.

Next, we gather our data… Gathering the data and getting it into a proper format for our predictive model is another story, but historical sports data is easy to find online. One thing to consider here is the traditional actuarial concern of credibility. If the data isn’t credible, it’s highly unlikely that we’ll be able to build a successful model from it….

After we choose the data to use, we need to select and transform the specific variables in the data set. The structure of the predictive (or independent) variables in relation to the target (or dependent) variable determines how well a model works. We can transform variables (by taking logarithms, for example) or bucket variables to see what gives us the best fit. Sports data can have hundreds (or even thousands) of variables….

Next, we process and evaluate our model. The key to good model performance is obviously getting the best fit. If we’ve done the other steps up to this point well, this step should run smoothly. Here we identify the ideal number of variables and use performance metrics to evaluate the model fits….

Once all of that is done, it’s important to not merely implement the model and ignore it. It requires routine maintenance. As time goes by and data continues to emerge, we need to take time to reinvestigate the data, update the models, and challenge some of our initial assumptions. The best models are continually updated and recalibrated, audited on a regular basis, and replaced when they are no longer effective.

Distracted driving accounts for 60% of teen crashes

A new report by the AAA Foundation for Traffic Safety found that six out of 10 teen driver accidents resulted from being distracted. Here are some key findings from the report:

The most frequent potentially-distracting behaviors were conversing or otherwise interacting with passengers and cell phone use.

• Passengers were present in 36% of all crashes
o 84% of passengers were estimated to be ages 16-19; fewer than 5% were parents or other adults.
o Driver was conversing or otherwise interacting with passenger in 15% of crashes.

• The driver was engaged in cell phone use in 12% of crashes
o Visibly using a cell phone in 8% of all crashes;
o Cell phone use appeared likely (driver looking at or manipulating something out of view of the camera) in an additional 4%.

• Cell phone use varied significantly by crash type:
o Visible in 21% of road-departure crashes, not visible but likely in additional 13%
o Visible in 10% of rear-end crashes, not visible but likely in additional 8%
o Least prevalent in single-vehicle loss-of-control crashes (most of these involved adverse weather or surface conditions).

• Drivers operating or looking at cell phones looked away from the forward roadway excessively – spent an average of 4.1 seconds out of final 6 seconds before the crash looking away.
• The driver exhibited no reaction at all before impact in over half of rear-end crashes involving cell phone use.

Distracted driving has key implications for the auto insurance industry. In the article “Distracted driving: Text-mining accident descriptions,” Milliman’s Phil Borba discusses how text mining can help insurers capture cell phone use at the time of an accident. The article also identifies major considerations regarding the use of cell phones on auto insurance premiums and claim adjusting.

The cellphone (and use of electronic equipment generally) introduces a new aspect into the operation of a vehicle and one which poses a challenge for setting insurance rates. Generally, cell phones are seen as distractions to the operation of a vehicle and are likely to increase the frequency and possibly the severity of accidents. Furthermore, the nature of the equipment may have differing effects on the frequency and severity of accidents. Steering-wheel and voice-activated controls for built-in cell phones may be safer than plug-in after-market equipment, while external devices (e.g., hands-free headsets) may be the least safe model.

Insurance premiums notwithstanding, more responsible use of cellphones may occur through state laws prohibiting or limiting their irresponsible use. Similar to the case with powerful vehicles that can increase speed beyond safe limits, state laws may impose some control over the irresponsible use of cellphones. Violations of state laws can carry fines, and the points on one’s license can increase the driver’s insurance premium.

Drivers’ use of electronic communication devices will also influence claim adjusting—in particular, assigning responsibility and liability when a distraction has occurred. However, the most commonly used data-capture reports do not enable the report-taker to report if the driver was distracted, or the nature of the distraction. As an alternative, claim adjuster notes, and other text reports, can provide a great deal of information on the circumstances attendant to an accident. These text-format data sources provide a great deal of information that can be tapped for deciphering the activities preceding, during, and after an accident.

Watch this video to learn more about how text mining can enhance claim analytics.