Tag Archives: google

The Sensor Revolution

An announcement by Google and insurtech BlueZoo last week signals the next level of deployment of sensors, a development that will not only let insurers price risk more accurately but will help them counsel clients on how to avoid those risks in the first place.

The announcement involves BlueZoo installing sensors in buildings that detect cellphones looking for WiFi signals. At the moment, rules of thumb tend to be used to generate estimates for the occupancy for restaurants, bars, ballrooms, etc. The BlueZoo approach, essentially counting cellphones, will be much more accurate. Unlike with traditional methods for estimating, the BlueZoo sensors will also be able to monitor constantly, letting insurers and building owners know about issues such as surges in occupancy.

Those surges can be correlated with risk and even used to alert the manager of, say, a bar that everyone should be alert to the possibility of a slip and fall in the bathrooms.

As intriguing as the Google-BlueZoo announcement could be for the insurance industry, it’s actually just the latest in a series of developments that will make essentially all information available on any issue at zero marginal cost. That’s because sensors can — and will be — everywhere. Sensors won’t need to be connected to the electric grid or to the WiFi networks that BlueZoo is using. They’ll be able to draw power even in remote locations, from tiny solar panels and batteries, and to connect to the cloud via cellular networks or satellites. Nowhere will be out of reach.

Lots of information will increasingly become available just because we’re all carrying sensors with us, in the form of our smartphones. Those sensors have been informing traffic management for decades now — when you see red show up on the route in front of you, you aren’t actually seeing cars slowing down, you’re seeing the phones in the cars slowing down. They’ll generate all kinds of other useful information, too, far beyond what BlueZoo and Google are using.

Other sensors will increasingly envelope us and map our world. While the recent fuss about space travel has been jaunts by billionaires, the far more consequential story is that SpaceX has carried nearly 900 satellites to space already this year. Those satellites will augment the world’s communication network while also providing continual updates with more more detailed images of the Earth than are now available. Those images will let insurers — among many others, including governments — track vulnerabilities to natural disasters, spot erosion and other developing risks and generally have a map of the entire Earth that they can monitor every day.

Cameras will continue to spread on Earth, too. We’ve all seen what the adoption of body cams by police officers has meant, and are now surprised when some public event isn’t caught on some camera somewhere. These days, cameras are just a lens plus a bit of battery and some memory, so they can be put just about anywhere at almost no cost and be connected to the cloud via WiFi or satellite. Even just the increased use of cameras on cars will capture massive amounts of new information as they drive around, and that information can be put to good use. (To bad use, too, but I’ll focus on the good for the moment.) Other types of sensors, notably lidar, will also be mounted increasingly on cars, especially as autonomous vehicles spread, and will generate huge amounts of new data, not just on traffic but on everything they pass.

Sensors will be built into just about every device, so they can warn owners before they break down, can sense water leaks and can provide any other sort of warning or information that might be useful. Sensors will increasingly even move into our bodies. Having them on our wrists has been helpful, but we’re not far away from being able to swallow sensors the size of a grain of rice that would provide real-time information on blood pressure, blood sugar and other measures that matter much more than how many steps we take in a day.

Add up all the ways that sensors, including cameras, will spread, and you have a full-on revolution in the information available to all of us, including insurers, to better monitor and manage our world.

You’ll note that I haven’t said when I think all this magic will happen. That’s both because it won’t be a single event — it’s already been happening for decades — and because a firm prediction is hard. I can tell you that two colleagues and I have written a book coming out this fall that refers to the infinite availability of information at no marginal cost as a Law of Zero that we argue will be firmly in place by 2050, with many of the benefits appearing well before then.

I’ll tell you more about the book as we get closer to the publishing date and, thus, more about the Laws of Zero, including the one on ubiquitous information. For now, it’s enough to start pondering where sensors can go in the short to medium term, how to harvest the information from the new sensors and from existing ones such as smartphones and how to analyze information in ways that improve decisions by insurers and many others.

There will be a ton of work involved — but profound improvements will result.

Cheers,

Paul

A Breakthrough in AI

You may have seen articles last week about a breakthrough for artificial intelligence in medicine that managed to be both arcane and exciting at the same time. Google’s DeepMind research arm solved a 50-year-old problem related to predicting how proteins fold themselves — news only for geeks, right? Think again. Understanding how these chains of amino acids fold themselves into 3D shapes, providing the structural components for the tissues in our bodies, opens up all sorts of possibilities for exploring our inner workings and for rapid development of drugs.

What I haven’t yet seen explained — amid all the speculation about just how many Nobel Prizes in Medicine will spring from the work — is that the type of AI that DeepMind developed to solve the protein-folding conundrum should also provide breakthroughs in insurance. This type of AI can take dead aim at some core issues in insurance, especially in underwriting and claims.

AI is funny. It tends to be talked about as a single thing, but it’s really a whole bunch of things, pushing against limits in a wide range of directions. And some of the progress is flashy without being all that important.

For instance, when IBM’s Watson defeated the greatest Jeopardy champions in 2011, IBM talked about sending Watson to medical school. After all, if it could beat Ken Jennings, what couldn’t it do? But Watson’s breakthrough was in natural language processing, a great advance if you want to be able to talk to a computer but little help if you’re trying to cure cancer. Similarly, when DeepMind beat the world champion at Go in 2017, the event made for fun headlines but not much more. The AI is terrific for any setting where there are a small number of rules and where the computer can play games against itself ad infinitum to optimize its approach, but how many real-world situations fit that description?

By contrast, what DeepMind accomplished in solving the protein-folding problem is of deep significance because the approach the scientists used — known as supervised deep learning — can be applied to so many business situations, including in insurance.

Without getting too deep into the details (which you can find in this excellent piece in Fortune, if you want to geek out like I did), the scientists faced a problem far more complex than businesses face: trying to figure out how a protein folds itself, in the milliseconds after it is created, based on a host of forces. While we’ve been able to sequence the human genome for more than 15 years now, you also have to know how the string of amino acids folds, because the shape determines so much of how the protein behaves.

Although a famous conjecture in 1972 said it should be possible to predict a protein’s shape just from the sequence of amino acids in it, the computation had proved to be too complex. Instead, the shape of a protein had to be determined through a complex chemical process and, often, through the use of a special type of X-ray produced by a synchrotron the size of a football stadium. The process could take a year and cost $120,000, for a single protein.

(I realize I may be giving you flashbacks to high school biology and chemistry and perhaps some unpleasant memories, but I’m just about done with the science and am getting to the implications for insurance.)

What the scientists had going in their favor were two things: a sort of answer key, because of some 170,000 proteins whose shape had already been determined experimentally, and some coaching tips that could help the AI focus on the key variables.

That starts to sound like a business situation, especially, in terms of insurance, in claims and underwriting. If you want to train an AI to take over tasks, you have underwriters and adjusters who can tell you what the right answer is and who can guide the AI’s self-training by steering it toward certain variables. Over time, that AI can become as good or better than a human at, say, looking at photos of the damage in a car accident and estimating the damage.

At least, that’s how it worked for DeepMind on a much harder problem. On a scale where 100 is perfect accuracy, the previous best AIs scored about 50, well below empirical methods, which scored about 90. But in a recent competition in which AIs predicted the shape of proteins whose forms had been determined experimentally but had yet to be published, DeepMind’s median score was 92 — a computer prediction outscored that year-long, expensive, physical process. Importantly, DeepMind’s AI can tell scientists how confident it is about each prediction, so they know how heavily to rely on it.

The immediate application for the DeepMind AI will, of course, be in medicine. There are some 200 million proteins whose shapes haven’t yet been determined, and the AI can quickly go to work on those. (The required computing power is only perhaps 200 of the graphics chips used in a PlayStation.) Understanding the shapes will help researchers see what drugs might interact with which proteins, potentially reducing drug development time by years and lowering costs by hundreds of millions of dollars.

However, how this AI moves into the mainstream remains to be determined. DeepMind functions as a research arm of Google, not as a business, and has promised to ensure that the software will “make the maximal positive societal impact,” but you could hardly blame Google if it tried to recoup the development costs through charges to Big Pharma. Only once this AI filters through medicine will it, I imagine, spread to other business problems, such as those that insurance faces.

For me, it’s enough to know at the moment that this sort of AI is possible, because that means that a lot of smart people will accelerate their efforts to bring supervised deep learning to insurance. While the wins at Jeopardy and Go were startling, the AI that solved the protein-folding problem will prove to be far more consequential.

Stay safe.

Paul

P.S. Here are the six articles I’ll highlight from the past week:

Smart Contracts in Insurance

Smart contracts will likely be used first for simpler insurance processes like underwriting and payouts, then scale as technology and the law allow.

Time to Try Being an Entrepreneur?

With businesses cutting back, many are asking that question. But there are huge misconceptions about how to think about the issue.

Surging Costs of Cyber Claims

With home-working widespread because of COVID-19, security around access and authentication points is critical.

4 Stages of Dominance in Performance

Chances are, you have natural gifts. However, many of the skills you need must be developed, nurtured and maintained intentionally.

Vintage Wine? Sure. But Vintage Tech?

Legacy systems that have evolved over long periods can be bloated and far less efficient and cost-effective than more modern technologies.

Do Health Plans Have the Right Data?

Health plans strive to deliver efficiency and great customer experiences and improve care outcomes. But what data are they missing?

Google and Applied Systems: 6 Months In

The insurance world was caught by surprise last October when Google’s Capital G investment arm announced a substantial investment in Applied Systems (north of $100 million). It was seen by many as an endorsement of the independent agent (IA) channel. If Google believes it will make a nice return on investment in a company serving the IA channel, then it must believe the channel will survive and grow. From the Applied Systems viewpoint, in addition to the extra capital to invest in the platform, it was anticipating access to world-class technology and expertise from Google. So now that the investment/partnership is six months in, what can we say about the progress?

Recently, I was fortunate to witness some of the activity first-hand, as Applied invited me to join them at the Google Cloud Next event in San Francisco. For me, it was a chance to “experience” Google and meet some of the players in the Applied/Google partnership. I’ve come away with several observations about Google and Applied Systems.

  1. Deep partnership: As originally promised, the Google investment was more than money looking for a return. Applied and Google are collaborating at the development level, with dozens of Applied developers being trained and exposed to Google tech.
  2. Future promise: It is still early in terms of how Google tech and expertise will influence Applied/IVANS systems, but there are indications that the first fruits will be visible this summer, and more enhancements and capabilities will be built into the product road map over the next several years.
  3. New era of computing: The shift to a new era of computing is well underway. The event was focused on developers, and the entire event was filled with sessions and discussions about containers, connectors, Kubernetes, APIs, big data, cloud, edge computing security, AI/machine learning and other technologies and approaches that are transforming how computing systems are designed, built and managed.

See also: Whole New World for Customer Contact  

My one disappointment at the event was that insurance was not very visible. There were hundreds of speakers and dozens of use cases, but nothing for insurance. Banking, retail and healthcare use cases and solutions were prominent (as were those from many other industries), but insurance only received a passing mention. Let’s hope the Applied/Google relationship will change that, and that more technology harnessed to address specific insurance use cases will be in evidence by next year’s event.

When Incumbents Downplay Disruption…

An unmanned car driven by a search engine company? We’ve seen that movie. It ends with robots harvesting our bodies for energy.

That is a line from a 2011 Chrysler car commercial mocking Google’s self-driving car project.

Another Chrysler commercial was even blunter: “Robots can take our food, our clothes and our homes. But, they will never take our cars.”

Chrysler’s early mocking of Google’s efforts exemplifies the fact that few cling to the status quo tighter than the companies that best understand it and have the most stake in preserving it. It is human nature to value what one does well and look askance at innovations that challenge the assumptions underlying current success. Sprinkle in some predictably irrational wishful thinking and you have the mindset that too quickly dismisses potentially dangerous disruptions.

Ironically, seven years later, those Google “robots” are now mostly driving Chrysler Pacifica minivans. Those robots have taken Chrysler’s cars and driven more than 10 million miles. Chrysler benefits by selling cars to Waymo, the spinoff from that Google project, but not nearly as much as it might have from building the robots themselves. Waymo is valued at $175 billion, about five times Chrysler’s market value.

History brims with other examples.

When Alexander Graham Bell offered to sell his telephone patents to Western Union, the committee evaluating the deal concluded:

Messrs. Hubbard and Bell want to install one of their ‘telephone devices’ in every city. The idea is idiotic on the face of it… This device is inherently of no use to us. We do not recommend its purchase.

Ken Olsen, who disrupted IBM’s mainframe dominance with his DEC minicomputers, mocked the usefulness of personal computers in their early days. He declared, “The personal computer will fall flat on its face in business.” Olsen was very wrong, and DEC would eventually be sold to Compaq Computer, a personal computer maker, for a fraction of its peak value.

See also: Why AI IS All It’s Cracked Up to Be  

Steve Ballmer’s initial ridicule of Apple’s iPhone is also legendary, though the words of the then-CEO of Microsoft were mild compared with the disdain on his face when asked to comment on the iPhone launch.

Years later, after he retired, Ballmer insisted that he was right about the iPhone in the context of mobile phones at the time. What he missed, he admitted, was that the strict separation of hardware, operating system and applications that drove Microsoft’s success in PCs wasn’t going to reproduce itself on mobile phones. Ballmer also didn’t recognize the power of the business model innovation that allowed the iPhone’s high cost to be built into monthly cell phone bills and to be subsidized by mobile operators. (Jump to the 4:00 mark.)

The biggest challenge for successful business executives—like Ballmer, Olsen and those at Western Union—when confronted with potentially disruptive innovations is to think deeply about potential strategic shifts, rather than simply mock innovations for violating current assumptions.

Another perhaps soon-to-be classic example is unfolding at State Farm Insurance.

State Farm released an TV ad that is a thinly veiled attack on Lemonade, a well-funded insurtech startup. Lemonade makes wide use of AI-based chatbots for customer service. State Farm, instead, prides itself on its host of human agents. In the ad, a State Farm agent says:

The budget insurance companies are building these cheap, knockoff robots to compete with us… These bots don’t have the compassion of a real State Farm agent.

As I’ve previously written, AI is one of six information technology trends that is reshaping every information-intensive industry, including insurance. In fact, as I recently told a group of insurance executives, I believe insurance will probably change more in the next 10 to 15 years than it has in the last 300.

See also: Lemonade Really Does Have a Big Heart  

That doesn’t mean that Lemonade’s use of chatbots for customer service will destroy State Farm. But, as State Farm should know, customer-service chatbots are only one of numerous innovations that Lemonade is bringing to the game. As several McKinsey consultants point out, AI-related technologies are driving “seismic tech-driven shifts” in a number of different aspects of insurance. Lemonade has also adopted a mobile-first strategy and is applying behavioral economics to drive other business model innovations.

State Farm executives need to get beyond the mocking and think deeply about how emerging innovations might disrupt their strategic assumptions.

One way to do so is being offered at InsuranceThoughtLeadership.com, where ITL editor-in-chief and industry thought leader Paul Carroll has offered a “State Farm Lemonade Throw Down.” Carroll offers to host an online debate between the two firms’ CEOs about how quickly AI technology should be integrated into interactions with customers.

Lemonade’s CEO, Daniel Schreiber, has accepted. I hope Michael Tipsord, State Farm’s CEO, will accept, as well.

Better for Mr. Tipsord to face the question now, while there is ample time to still out-innovate Lemonade and other startups, than to be left to reflect on what went wrong years later, as Steve Ballmer had to do with the iPhone.

Digital Survival Tools for Agents

Whether the majority of your business is online or in-office, it is crucial for you to have the right tools to help you capitalize on the insurance market and get ahead of the competition in a changing landscape.

It does not matter what type of insurance you are selling, whether it’s employee benefits, life insurance, group insurance, voluntary benefits or property and casualty. While your role may not be directly affected by things like legacy system transformation, robotics and big data, there will be ripple effects. Besides obtaining new clients, presenting renewals and marketing, changes in regulation and advances in technology are all things that agents will have to contend with.

Here are three elements that savvy agents and brokers will want to consider.

Multi-generational marketing

Global populations are now categorized (albeit loosely) into four categories: Baby Boomers, Generation X, Millennials and Generation Z. Although Baby Boomers are still the largest population, the U.S. Census Bureau predicts Millennials will outnumber Boomers by 2019.

These differentiated markets make targeting sales much more difficult. Fortunately, there are online tools that can support you. The trick here is diversifying your presence. Ensure that you have a presence on multiple channels so that you are able to meet your customers where they are.

See also: 10 Essential Actions for Digital Success  

Update your agency website with a live chat feature, and ensure it is easy to contact you online. Examine whether it makes sense to use Twitter, Facebook or Instagram. If you do, you’ll need a strong content strategy that provides real value to pull in visiting prospects.

Don’t just surf the web, observe the web. Set up Google alerts and analytics and Hootsuite streams to follow partners and competitors. Watching for trends will keep you ahead of the game.

Administration tools

A strong agency management system can provide you with everything you need to support your customer lifecycle. When looking for the right one for you, think about CRM and marketing automation. Determine what will make it easier for you to track leads, nurture prospects, close deals and obtain commissions.

Once you’ve sold a policy, a high-quality microphone and webcam will enhance consistent communication with customers remotely on Skype, WebEx, GooglePlus Hangouts or even Facebook.

Get comfortable with automation

As you get comfortable with a new and diversified way of connecting with your customers, you’ll want to consider that insurance carriers are doing the same thing. Accenture’s Technology Vision 2018 report revealed 82% of insurance carriers agreed that their organizations must innovate faster just to stay competitive.

In a world where customers are shopping around for options and prices all the time, retention itself becomes a valuable commodity. Help carriers help you by learning what tools their new systems have to offer so you tap into all the resources available.

Do your insurance companies offer broker portals? Do they offer online quoting capability for immediate results? Can you generate a proposal or immediately sell a policy? Can you offer that functionality on your own website? The carriers that invest in your success by improving sales, underwriting and admin functions for quicker turnarounds and smooth renewals are doing themselves a favor, too.

See also: Agents Must Become ‘Discussion Partners’  

Think strategy

As you determine the best way to move forward, sit down with others on your team, start a Google doc and plan your strategy for the year ahead. As Yogi Berra wisely said, “If you don’t know where you’re going, you might not get there.”

What free tools will you use? Which ones will you invest money in? How will you track progress to determine ROI? What tools are working for you?

The best agents and brokers will be nimble enough to exploit the tools available to them and prepare for new ones as they arrive. The sooner you start, the more likely you’ll find yourself ahead of the digital curve.