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.
How can a company leverage customer data and turn it into actionable information? This was the challenge one transportation provider faced when its modeling system began underperforming after the company implemented it to predict revenue and passenger traffic. In this article, Milliman consultant Antoine Ly discusses how the firm created a machine-learning model that helps the company analyze various aspects of its ridership, leading to more informed financial decisions.
Here is an excerpt:
Working from a mock-up drafted by the client, the [Milliman] team reproduced the dashboard to the client’s specifications, but it is now supported by newly developed software as well as the client’s data warehouse. The dashboard allows the client’s management team to quire different aspects of passenger usage to gain insight into traffic flows and revenue. Colour-coded symbols, which when clicked on, give managers a concise picture of a train’s revenue and traffic. Managers can also quire the system based on selected features for both past usage and anticipated ridership, and are now able to make more informed decisions about pricing, the need for discounts or adjustments to marketing campaigns.
Because the model can adapt to new situations, deviations from the average error are confined to a much more narrow range. This means managers can have more confidence in the model’s predictive value and increases their ability to manage revenue.
Registration for the 2017 Data Science Game is officially open. The Data Science Game is a two-phase competition showcasing teams of data science students from universities around the world. An online qualifier will take place on April 15 with the final stage happening in September.