Tag Archives: 3d

Don’t Be Distracted by Driverless Cars

Because of the arrival of autonomous vehicles, a good number of prognosticators are loudly heralding the end of auto insurance and the insurers that rely on this income stream. While there is no doubt that the auto insurance will change over time because of autonomous vehicles, the doomsday folks paint a picture of an almost certain — and right-around-the-corner — cataclysmic change, with the general population cheering wildly about not having to pay for auto insurance anymore.

Disappointingly for those people, this is a highly unlikely scenario.

At the most fundamental level, autonomous vehicles are expensive. Until the bulk of the population can afford autonomous cars and trucks, we will be living in a world with a foot on each side of the driving paradigm. This means that, while the autonomous vehicle might not hit the car in front of it, the vehicle behind it may very well end up in its back seat. Uninsured and underinsured coverage, comprehensive coverage, coverage for property damage and liability coverage will be needed for a long, long time.

See also: Connected Vehicles Can Improve Claims  

In the meantime, auto insurers will have the opportunity to work out the liability paths with the autonomous auto manufacturers. And auto insurers will have the time to adjust their product mixes and financials. Underwriters and claims personnel will adjust processes and practices. Believe me, I am not suggesting that change won’t happen. It will. And some of the changes will be painful from several perspectives. But with all the various factors potentially reining in the spread of autonomous vehicles — cost, regulators, consumer wariness — insurers will adjust. At least the insurers that believe in innovation and data and analytics will adjust — and flourish.

What people are not focusing on is the rapid change in the products and services that are coming from the convergence of emerging technologies — and this is happening right now. Take a quick perusal of the topics in SMA’s Next-Gen Innovation Community, and some examples come to light:

  • To diagnose a rare genetic disorder, a physician combined an exomes analysis with a facial comparison using an app called Face2Gene, which was developed by the same programmers who taught Facebook to find your face in your friends’ photos.
  • To reduce manufacturing times and production periods, Adidas will use 3D printing or additive manufacturing methods as the core technology of its factory that produces sneakers.
  • The use of “cobots” — collaborative robots that perform tasks alongside humans — is rapidly gaining popularity, and it is the fastest-growing segment of the U.S. robotics industry.
  • The global semiconductor industry is pushing to develop new chip designs, materials and manufacturing processes. One reason is the widening use of the artificial-intelligence technique known as deep learning in products — both consumer and manufacturing products.

Additional examples could consume volumes of space. But what does this convergence mean for the insurance industry? It means unknown risk and unknown liability.

Unlike the autonomous vehicle world, where underwriters and claims adjusters understand the general risk and liability landscape, the convergence of emerging technology is generating significant numbers of unknowns. Relationships between the unknown risks and liabilities will be the most challenging.

The insurance industry should not be as concerned about the impact of autonomous vehicles as it should be about what is happening now in the rapidly changing world of products and services because of the effects of emerging technologies. Underwriters and claims personnel must be supported by sophisticated data and analytics capabilities using AI, machine learning and cognitive computing (pick your favorite advanced tool!)  because risk and liability will not be in the same forms as are currently familiar to insurance professionals.

See also: Autonomous Car Tech Reaches Mid-Market  

There are a lot of reasons that insurers will not look the same in five to seven years. Core modernization, digital capabilities and, yes, autonomous vehicles will generate change. But the halls of successful insurers will not be empty. They will be filled with technical experts in product liability, D&O, E&O, cyber and medical malpractice — to name but a few product lines that will have the ability to rapidly respond to a risk and liability landscape that didn’t exist  in their wildest imaginations two to three years ago.

Insurers that are not preparing themselves for this eventuality will fail.

Geospatial Solutions: A Vital Enabler

At SMA we have long been tracking the rise of smart things and their implications for the insurance industry. A variety of emerging technologies has been rapidly advancing to make everything imaginable smart. But participating in the ESRI User Conference in San Diego this year has driven home one key point: Geospatial solutions will have a critical role in making sense of all those smart things. The notion of a connected world is not an academic pursuit – possibilities to ponder about sometime in the future. It is a here-and-now issue affecting every industry, including insurance.

See also: Insurance and the Internet of Things  

The Internet of Things is already upon us. Sensors and embedded chips are present in buildings, infrastructure, agricultural settings, vehicles, devices in the home, medical facilities and government operations. Add to that billions of mobile phones and the capability to track location, movement and environmental conditions, and the result is many connections and massive amounts of data already measuring, monitoring and acting on the world around us. Predictions about the adoption of connected things vary widely, but, by any measure, the connection points and the data volumes will continue to increase exponentially. The problem, then, is not deploying smart things or collecting data from the smart things. The fundamental problem is the ability to combine and analyze data to gain some insights. In some cases, those insights might trigger decisions with global implications, solving some of humanities thorniest problems. In other cases, the insights might lead to a small action that improves the life of one individual.

Enter geospatial solutions. Analytics and big data, in general, have essential roles to play in understanding the data generated in the connected world. But visualizing that data in a way that tells a story and reveals insights is the province of geospatial solutions, an area that has much to contribute to the connected world. Unfortunately, old impressions of geographic information systems (GIS) linger, especially in insurance. Most insurers have GIS solutions to do geospatial analysis, but they tend to be used by a small number of specialists for very specific applications. Today, the advances in 3D; animation; digital capture through drones, satellites, or LiDAR; and other technologies offer new opportunities. Tools for spatiotemporal analysis (understanding changes over time), crowdsourcing of real-time data and cloud-based collaboration platforms for maps and apps have elevated the discipline and provided government and industry with the potential to gain a deep understanding of the world to aid in addressing both new and old problems.

See also: How Connected Will Connected World Be?

Many insurers are considering the implications of the connected world and how it will affect their particular lines of business. Connected cars, smart homes, the quantified self, smart cities, autonomous commercial fleets and many other new areas create both threats and opportunities for insurers. Evaluating how geospatial capabilities can be harnessed to gain a better understanding of these emerging areas should be part of every insurer’s strategy and planning initiatives.

How Google Thinks About Insurance

For those of us wondering what Google plans to do in insurance — isn’t that all of us? — it’s worth looking at the company’s Project Sunroof. The project uses exceptionally sophisticated mapping data to determine which homeowners would most benefit from solar panels and, in the process, may provide some insight into how Google is approaching insurance.

To me, there are two key aspects of Project Sunroof. The first is that Google is taking a bottom-up approach that could inform a lot of decisions about insurance (while insurers traditionally go top-down). The second is that Google is being unusually smart about combining layers of information — some proprietary, some in the public domain; some new, some long-available — to produce what my frequent co-author Chunka Mui has, with a little help from me, labeled “emergent knowledge.” (“Big data” is the term commonly used, but data isn’t very interesting, while knowledge is. And the size of the database doesn’t matter. What matters is using developments in technology to look in the right places to find the right data to answer the right questions so that revelations emerge.)

Top-down vs. bottom-up

Insurers typically start with pools of risk. They’re getting much more sophisticated about subdividing those pools into ever smaller groups, but the thinking is still along the lines of “drivers without moving violations who travel 11,000 to 12,000 miles a year in generally suburban conditions.” Insurers will keep getting more and more specific and produce more and smaller pools but are still going from the top down.

Now look at Project Sunroof. Google is modeling the world in three dimensions and using that model to generate information house by house based on totally personalized criteria: on the square footage on the roof that would be available for solar panels, on the amount of cloud cover that is expected to obscure the sun above that house, on the effectiveness of the sunlight that will hit the roof (incorporating calculations based on temperature and on how the angle of the sun changes each day) and on any shade that would be cast on those panels from other structures. Although the article doesn’t say so, I assume Google calculates potential savings on solar based on the rates of each local utility. In any case, there are no pools in sight for Google — unless you want it to tell you about those in the backyards.

That same model of the world could be the basis for a house-by-house, car-by-car, person-by-person approach to insurance for Google. And, if this approach works, Google will gain the sort of information advantage that has proved to be almost impossible to overcome. Even the largest insurers would have a hard time spending the money that Google has to map the U.S. by having cars drive every single street to take pictures and collect data, by making a series of acquisitions of data providers and by employing a small army of people to manually fix errors and update maps — and Google would still have a years-long head start in developing its model of the world. Microsoft has thrown billions of dollars at search engines, but even Microsoft couldn’t overcome the fact that Google’s dominant share meant it was always learning and improving faster than Microsoft’s Bing. Apple’s map services were ridiculed, by comparison with Google’s, when Apple launched them in September 2012. Apple is now at least in Google’s ballpark on mapping, but no insurer can come close to Apple’s resources — a $646 billion market valuation and $202.8 billion of cash in the bank. That’s “billion,” with a “b.”

Emergent knowledge

Google obviously begins with a huge asset because of its prescient decision years ago to map the entire U.S. and because of the recent work that has made that map 3D.  But Google is also taking data wherever it can get it.

I know from some work I did at the Department of Energy in 2010 that national maps of sunlight have been available for years, and they have surely become far more detailed as the interest in solar power has spread, so I assume Google didn’t have to generate those maps on its own. Temperature maps are also in the public domain. (Especially high or low temperatures degrade the performance of solar panels.) Those maps will become increasingly granular as they incorporate data from smartphones and other widely used devices that can act as sensors — temperature will no longer be what the weather station reports from the Detroit airport; temperature will be known house by house. Overhead photos from satellites and, in some cases, drones are widely available, so Google can use those to check square footage of roofs, to see which direction the solar panels would point and so on. Google can collect information on rates from state utility commissions, where utilities have to make regular filings.

It’s easy to imagine Google layering similar types of information onto its map of the world for insurance purposes. In response to the federal Data.gov initiative, governments at all levels are making more information available digitally, so Google could incorporate lots of data about where and when accidents occur, where break-ins happen, where and when muggings occur and so on.

Google could incorporate private work that is taking a 3D approach to flood risk (whether your house is three feet higher or lower than the average in a neighborhood can make all the difference) and is being much more discriminating about earthquake risk. Google could add information, from public or private sources, on the age of homes, type of pipes used, appliances, etc. to flesh out its understanding of the risks in homes.

And, of course, Google will have lots of very precise information of its own to add to its model of the world, based on, for instance, what it knows about where your smartphone is and can infer about where you park your car, where and when you drive, etc.

Once you take all this information and map it to such a precise model, there will surely be some non-obvious and highly valuable insights.

WWGD: What Will Google Do?

Looking at Project Sunroof still doesn’t say a lot about how Google will attack insurance. Will it just sell increasingly targeted and valuable ads? Will it sell leads? Will it become a broker? Will it do more?

But I think it’s safe to say that, whatever Google does, its starting point will the most sophisticated model of the world — and that model will always be improving.