Developments over the past few years in predictive analytics are providing opportunities to improve the early identification of claims with a higher likelihood of piercing workers’ compensation reinsurance layers. Over the past decade or so, the field of claim analytics has moved from performing forensic work on closed claims to analytics that can identify at 60 days from the date of injury (or sooner) claims with a high likelihood of exceeding a retention level.
While an excess loss is obvious for some catastrophic claims, the buildup to the attachment point is less obvious for many excess loss claims due to the subtleties of compounding factors. A significant challenge with early identification analytics for claims that have not reached an excess loss attachment point is that the administration of the claim is often handled by several specialists without any single participant noticing the aggregation of costly factors.
A recent development in predictive analytics is the use of machine learning software that extends the principles of conventional multivariate analyses. In contrast to the conventional analyses, these advanced analytic methods are not limited to linear relationships. Another development is the extraction of text information from claim adjusters’ notes, nurse care manager reports, and medical reports.
The advances with machine learning software and text mining algorithms are necessary tools for the early identification of claims most likely to become excess loss claims. To learn more about how analytics has affected the early identification of claims, read this article by Lori Julga and Phil Borba.
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.
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.
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.
These tidbits of textual information about drug-impaired driving don’t just inform the claims-handling process, but they can also provide valuable input to underwriters, Borba says.
Finding that an individual has been under the influence of a drug or taking medications could provide a reason for a non-renewal, he says. “Or it can become a rating variable—almost similar to if you had been convicted of a DWI [driving while intoxicated]. You are then put under a different category,” Borba says, suggesting underwriting and pricing applications.
A report summarizing Milliman’s findings about DUID—driving under the influence of a drug—was published in the April edition of Milliman’s Insight publication. The report notes that other carrier benefits of identifying drug-impaired driving include better claims triage and assignment of liability.
“Finding that the other driver was DUID may be cause for a subrogation recovery against that driver, or provide enough additional evidence to increase the likelihood or size of recovery,” the report notes.
Explaining the claims triage benefit, Borba says that if carriers react quickly to any indication of drug use, then they will be able to reroute claims to adjusters who are familiar with different medications and know how to test for those. That’s important because the establishing that someone was DUID is a complex process, he says—much more involved than establishing a DWI.