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.
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.
Digital transformation is revolutionizing the way organizations conduct business. In this video, Walter Isaacson, president and CEO of the Aspen Institute, discusses how disruptive technology can present the insurance industry with new opportunities to pool risk.
Milliman has announced a new innovation in the InsurTech space—a driving “risk score” created with tech start-up Zendrive that is up to six times more powerful than the leading predictive models.
Milliman teamed up with Zendrive, a smartphone-powered driving analytics company, to study how distracted driving and other driving behaviors can lead to auto collisions. Using Zendrive data, Milliman verified the behaviors that were strong indicators of collision frequency and created a risk score to compare the “worst” drivers relative to the “best.” Their findings revealed that the worst 10% of drivers were over 13 times more likely to be involved in a crash than the best 10% of drivers. The results were based on one of—if not the—largest telematics data set in the United States. As of today, Zendrive has captured over 40 billion miles of driving behavior via smartphone sensors.
Smartphones can measure driving behaviors that traditional, first-generation telematics can’t, such as who is driving the vehicle and phone usage contributing to distracted driving. These new-age predictors contributed to a risk score that is over six times more accurate than the current industry leader models, which use traditional hardware-based telematics devices. There’s an opportunity here for auto insurers, especially commercial auto fleet insurers, to be early adopters of this technology, and improve their abilities to measure and rate risk.
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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.
As more drivers use smartphones to talk, text, and perform other functions while driving, concern over distracted driving and its contribution to climbing collision rates has increased. Using data collected by Zendrive, Milliman recently studied the impact of distracted driving and other driving behaviors on collision frequency. Consultant Sheri Scott provides some perspective in this article.