applicationsThis month we are focussing more on Analytics & Data Science, as well as applications of both in businesses.

To expand on that latter theme, we have another guest blog post courtesy of our friends at Insurance Thought Leadership blog.

In this guest blog post, Cathy Chang and Heather Nelson from Silicon Valley Data Science, share their experience of applications for US motor insurers. They explore the options of mining three types of data (usage data, external data & real-time data).

Whether your business is an insurer or not, I hope you find their reflections on the opportunities provided by such datasets to be insightful. Including more analytical intelligence into pricing optimisation, based on understanding the consumer/user better, should have relevance beyond insurance or even financial services.

American Insurers and data usage

For more than a decade, Americans have been trained to assess and buy insurance products as commodities. This is partly thanks to commercials by Geico, the biggest advertising spender in insurance for many years, which has pushed the concept that “Fifteen minutes can save you 15%”, portraying policies as “the same”, where the only differentiator is the price. Some have dubbed insurance’s being viewed as a commodity as the industry’s biggest challenge.

On top of price-centric buying behaviour, most consumers who are required to purchase certain insurance products — such as medical and auto — expect to have a wide selection and may switch insurance carriers at a blink of an eye. With competition increasing, big data and associated technologies provide timely opportunities by reshaping the modern insurance landscape.

The insurance business model typically comprises four parts:

Insurance companies have always used data in each part of the business model — to assess risk, set policy prices and to win/retain consumers. Previously, insurers would formulate policies by comparing customers’ histories, yielding a simplistic and not-very-accurate assessment of risk. Today, our increasing ability to access and analyse data as well as advancements in data science allow insurers to feed broader historical, continuous and real-time data through complex algorithms to construct a much more sophisticated and accurate picture of risk. This enables insurance companies to offer more competitive prices that ensure profit by covering perceived risk and working within customers’ budgets. Such prices, or setting policy premiums, come from underwriting.

In this post, we will focus on an underwriting use case in the highly competitive auto insurance space, where accuracy of risk assessment and rate setting ultimately drive the insurers’ profitability. Future posts will address other parts of the insurance business model.

More accurate (and competitive) pricing

Auto insurance may be the most competitive part of the insurance marketplace. Customers shop around (often marketed to by price-comparison services) and change insurers at will. To offer competitive premiums that allow profitability, auto insurers have no choice but to assess risk as accurately as possible.

In auto insurance, insurers use both “small” and “big” data. David Cummings explains the two as:

Traditionally, underwriters have developed auto insurance prices based on smaller data — such as the car’s make, model and manufacturer’s suggested retail price (MSRP). But ‘bigger data’ is now available, providing far more information and allowing insurers to price policies with a better understanding of the vehicle’s safety. From manufacturers and third-party vendors, insurers can learn about a car’s horsepower, weight, bumper height, crash test ratings and safety features. That big data helps insurers create sophisticated predictive models and more accurate vehicle-based rate segmentation.”

As data increasingly becomes the lifeblood for insurance companies, the combination of big data and analytics is driving a significant shift in insurance underwriting. For example, faster processing technologies such as Hadoop have allowed insurers like Allstate to dig through customer information — quotes, policies, claims, etc. — to note patterns and generate competitive premiums to win new customers.

The data and analytics movement has also made room for newcomers like Metromile to enter the market. Although the company started out with no proprietary data of its own, Metromile has quickly gained customers and collected data with a new model: auto insurance by the mile.

This entrance of Metromile into the auto insurance space has both disrupted the industry and put pressure on incumbent insurance providers to make advances with their own models.

In auto insurance underwriting, a number of ways to use new data to achieve more accurate pricing have gained attention:

  • Using usage-based insurance (UBI)
  • Leveraging external data
  • Leveraging real-time data

Usage-based insurance (UBI) and the Internet of Things (IoT)

UBI can be used to more closely align premium rates with driving behaviours. The UBI idea is not new — there have been attempts to align premiums with empirical risk based on how the insured actually drives for a couple of decades. In 2011, Allstate filed a patent on a UBI cost determination system and method. Progressive, State Farm and The Hartford are just a few examples of other companies that are embracing UBI methods in underwriting.

Technological evolutions like the Internet of Things (IoT) and all its attendant sensors provide new ways to capture and analyse more data. The UBI market has flourished and is expected to reach $123 billion by 2022. The U.S., the largest auto insurance market in the world, will lead the way in UBI marketing and innovation in 2017. With UBI’s market potential, there has also been a rise in business models such as pay-per-mile insurance for low-mileage drivers using UBI methods in underwriting. Embracing UBI methods in underwriting is no small feat, because of the huge amounts of data that must be collected and integrated. Progressive collected more than 10 billion miles of driving data with its UBI program, Snapshot, as of March 2014. For the most part, the data focuses on mileage, duration of driving and counts of braking/speeding events. These are all “exposure-related” driving variables, which are considered secondary contributors to risk. They can be bolstered with external data such as traffic patterns, road type and conditions, which are considered primary contributors to risk, to create a more accurate picture of an individual driver’s risk.

Leveraging real-time data

Real-time data is a subset of the rich external data set, but it has some unique properties that make it worth considering it as a separate category. The usage of real-time data (such as apps that engage customers with warnings of impending weather events) can cut the cost of claims. Insurers can also factor data such as weather into the overall assessment at the time of underwriting to more accurately price the risk. In the earlier example of using external data to shorten the underwriting process, accessing external information in real-time and checking with multiple sources makes the information in auto insurance application forms more accurate, which, in turn, leads to more accurate rates.

Underwriters can also work with integrated sales and marketing platforms and can reference data such as social media updates, real-time news feeds and research to provide a more accurate assessment for those who seek to be insured. Real-time digital “data exhaust” — for example, from multimedia and social media, smartphones and other devices — has offered behavioural insights for insurers. For example, Allstate is considering monitoring and evaluating drivers’ heart rate, electrocardiograph signals and blood pressure through sensors embedded in the steering wheel.

Insurers can influence the insured’s driving behaviours through real-time monitoring, significantly altering the relationship with each other. A number of insurance companies, such as Progressive — in addition to the pay-per-mile insurer Metromile — are monitoring their customers’ driving real-time and are using that data for underwriting purposes. Allstate filed a patent on a game-like system where drivers are put in groups. Those in the same group could monitor driving scores in real-time and encourage better driving to improve the group’s driving score. Groups can earn rewards by capturing better scores.

Conclusions for data & analytics applications

There’s no doubt that the risky business of insurance is sophisticated. The above examples of leveraging UBI, external data and real-time data merely scratch the surface on data-driven opportunities in auto insurance. For example, what about fraud? Efficiency and automation? Closing the loop between risk and claims? Because only 36% of insurers are even projected to use UBI by 2020, those that embrace data-driven techniques will quickly find themselves ahead of the game.

While it’s outside the scope of this post, we should note that leveraging data and methods shouldn’t be done without careful consideration for consumers. As consumers enjoy easier insurance application processes, as well as having more products to choose from and compare prices on, increasingly they will want to understand how these data and analytics techniques affect them personally — including their data privacy and rights.

As we pause and reflect on how data and analytics have driven changes in auto insurance underwriting, we welcome questions and discussions in the comments section below. In the future, we’ll examine other ways the insurance market is becoming more data-driven, including the changes that data and analytics are driving in auto insurance claims and the rising focus of marketing.

Thanks to Cathy & Heather for that post. If you have applications of analytics, in your business or industry, that you’d like to share please get in touch.

We’d love to include more case studies here on one of the Top 50 Customer Insight resources in the world! 🙂