Tag Archives: echo

Growing Import of ‘Edge Computing’

I’ve often thought the most valuable interactions happen with the people at the edge of our networks. The people we meet serendipitously, through our more distant contacts. It’s here, on the edge, where the sparks of creativity really fly.

Recently, I’ve been putting this theory to the test by taking the time to meet face-to-face with people I might more usually only connect with by email or LinkedIn.

It’s a refreshing experience. One of the many benefits is frequent exposure to new ideas and new ways of thinking from people who view the world through a different lens. There are other benefits, such as getting to see the new musical “Bat Out of Hell” in the company of an American lawyer. But I digress …

It’s not just people who benefit from networking at the edge. Computers do, too, and there’s an interesting analogy to be drawn here with the emerging importance of edge computing. This is where processing and data are placed at the edge of our networks to have the maximum effect.

Let me explain.

Over the last decade or so, we’ve seen processing and data increasingly centralized in the cloud. This has been driven partly by the cost-effectiveness and scalability of cloud computing and partly by the growth of big data.

Amazon Alexa is an excellent example of how this works. Voice commands are picked up by an Amazon Echo device, converted from speech to text and fired off to the cloud where natural language processing (NLP) and clever software are used to interpret and fulfill your requests. The results are served back to your Echo in less than half a second. Very little processing takes place on the Echo, and very little data is stored there; all the heavy lifting is done centrally in the cloud.

See also: The Big Lesson From Amazon-Whole Foods  

This model works well if the edge device (the Echo) is always connected to the cloud via the internet, the arrival rate of new data (your voice commands) is relatively low and the response time is not critical (we’re happy to wait half a second for Ed Sheeran to start his next song).

But it doesn’t work so well if the edge device is not always connected, if the volume of real-time data streaming into the device is huge or if an instant response is needed.

Imagine you’re being driven to the theater by your AI-controlled smart car equipped with hundreds of sensors gathering real-time data from every direction. If the sensors detect a child running out in front of the car, there’s no point firing that data off to the cloud for processing. It has to be processed and acted on instantly and locally by the car itself.

There are many, many more examples where the edge devices (cars, traffic lights, fitness bracelets, microwaves, safety critical sensors on assembly lines… in fact, very many of the billions of devices that will be connected to the internet of things over the coming years) will need the ability to process their own real-time data.

These edge computing devices will still connect to the cloud, but the location of the processing and the data will vary according to need — in the cloud for asynchronous machine learning insights and improvements; at the edge for real-time processing of real-time data streams to determine real-time actions.

Developing the hardware and software for these devices will require new ways of thinking. It’s not about big data; it’s about small, fast data. And I’m sure we’re going to see dramatic improvements in battery efficiency, data storage and processing capability of these intelligent edge computing devices.

The Internet of Things is actually going to become the Internet of Small Powerful Intelligent Things (although I doubt that acronym will catch on).

See also: Major Opportunities in Microinsurance  

Most interestingly of all, though, from a cultural and business perspective, is the innovation this edge computing will enable, such as:

  • The insurance industry will be able to offer better deals and new types of policies driven by the intelligence embedded in the insured assets.
  • The health industry will be able to provide preventative care supported by intelligent wearables monitoring everything from activity to blood sugar levels.
  • The entertainment industry will be able to deliver interactive content without those annoying buffers and whirling circles.

And, who knows, maybe edge computing will also help us communicate more effectively with each other. Because spending time at the edge of our networks, as I have been discovering, is where the sparks of creativity really fly. Like the musical “Bat Out of Hell,” it’s one experience I can thoroughly recommend!

Forget ‘Intel Inside’; It’s Now AI Inside

I am sure we are all familiar with the Intel slogan, “Intel inside.” This has been a very powerful tagline and one that has helped Intel become the dominant PC chip supplier. (I know I was very influenced by the slogan and very rarely bought a non-Intel PC as a consequence.)

But I believe that this slogan will be rapidly replaced by “AI inside” because I believe we are almost at the point when ALL future apps will include elements of AI. I also believe there is a very good chance that Amazon’s Alexa might become the de facto automatic speech recognition platform that will sit in front of (outside) every single app in the future.  (My rationale is here.)

Why do I say this?

First, you need to recognize that AI is not one singular, all-embracing technology. Rather, it is a set of technologies that hope to emulate the way a human interprets and acts upon information — albeit at the speed of light and (we hope) without error, on a 24/7 basis. As such, AI includes technologies such as natural language (voice) processing (NLP), semantic analysis and cognitive processing.

Second, these technologies have become pervasive. The CEO of IBM recently announced at Davos that Watson (a supercomputer and a collection of AI APIs) is now having a (positive) impact on the lives of some billion people (about 1/7th of the world’s  population). I don’t know how many Echo and Dot units have been sold by Amazon (it must be tens of millions, at least) but each unit gives you access to Alexa, which uses both voice recognition and processing.

See also: 10 Questions That Reveal AI’s Limits  

Third — and most important — you don’t need to have a degree in AI (any more) to deploy AI.

AI was notionally conceived by Alan Turing in 1936 (but, in one sense, you can trace the origins of AI all the way back to Archimedes!). I was taught elements of AI at university in the early 1970s, but I didn’t have a chance to develop an AI app until the mid-1990s when I was a consultant for the Nationwide Building Society. We had just finished a ground-breaking piece of work that involved the development and deployment of the world’s first touch-screen-driven, customer self-service system. This system was a huge success on all measures, so the client I was working for at the time asked me:

“How far can you take this idea? Could you, for example, develop a system that’s as good as — or even better than — our best sales person?”

Without knowing it at the time, he was asking me to develop our first AI system. Fortunately for me (because I am certainly not an AI expert), Accenture had just hired an authority on the subject. He was swiftly assigned to my project team, and we stepped once more into the unknown world of innovation.

We started by gathering a team of the client’s top sales people. We then sat them down with our AI expert, who had been carefully briefed on the rules governing the sale of regulated products. We also called in the services of a user experience designer to obtain a better understanding of people’s risk appetites and option requirements. Last, but certainly not least, we asked a group of customers to help us develop the system that would be designed for their stand-alone use.

The result blew everyone away.

It even won the support of the U.K. Financial Services Authority (FSA), which agreed to assess the system for compliance. The FSA tested and analyzed every aspect of the new application — and then signed off. It was the first time the FSA had ever approved a sales platform that removed the need for a sales person.

Remember, this happened in the early ’90s — long before Java, Windows 95 and the first PlayStation were launched. Our system is a tribute to a client who not only had the vision to see the possibilities but also had the courage to take on the challenge — as well as the very real risk of failure.

However, there is a sad but rather revealing postscript to this story.

What happened to this ground-breaking system? Well, it was lauded, feted and widely acclaimed — and then quietly shelved. The building society decided to focus on building its Systems of Record (SoR) rather than its Systems of Engagement (SoE). And, sad to say, that was not an uncommon fate back then. Real innovation is often too radical for most risk-averse management to stomach. Sometimes it takes time to build an appetite for the truly ground-breaking. And maybe — just maybe — 20 years later, that time has come.

There was another problem: I only had one AI programmer at my disposal, and there weren’t that many more in the U.K. at the time. Given this, it would have taken a considerable amount of time to build an industrial-strength application that could have been put into the hands of any customer. But now we don’t have that problem.

One of the firms we at Clustre represent is an AI consultancy that is AI-technology-agnostic. It conceives, designs and builds AI-driven customer and employee apps that use a variety of AI technologies — as appropriate. It was recently asked by a loyalty card operator to show how AI could be used to allow a card holder to get an answer to a query without talking to a human or having to scour through FAQs (which I think are generally pretty useless). The firm created a web-based chat bot that used Watson to help recognize and understand the question and used another product to drive the Q&A process and, ultimately, answer the question.

So clever is the bot that it can easily handle misspellings and allow the questions to be phrased in a variety of ways and still operate properly. I would hazard a guess that this tool could handle at least 50% of all customer queries (the rest would be handed off to a human to resolve). That’s a lot fewer calls that need to be routed through to a human.

See also: Why 2017 Is the Year of the Bot  

So, you may ask, how many days did it take our AI consultancy to design and build this AI-driven chat bot? Just five.

Five days to design and build a tool that could potentially reduce call center volumes by around 50%!!!

AI has truly arrived, and everyone should be looking at how you are going to deploy it NOW!

10 Questions That Reveal AI’s Limits

AI developers are making amazing advances. Witness the excitement around AI’s progress in search, cancer diagnosis, genomic medicine, autonomous vehicles, Go, smart homes, machine translation, and even lip reading.

Progress in such complex problems raises hopes for the development of general-purpose AI that can be deployed in a wide range of intelligent, open-ended interactions with people like computer interface, customer service, planning and advice.

Photographer: Michael Nagle/Bloomberg

It is easy to imagine an enhanced Apple Siri, Amazon Alexa or IBM Watson that engages in conversations with people to answer questions, fulfill commands and even anticipate needs. In fact, unless you watch marketing videos with a very critical eye (like the latest one for Alexa shown below), you might even believe that AI has already reached this point.

Unfortunately, AI is far from this level of intelligence. AI lacks the capability to understand, much less answer, many kinds of easy questions that we might pose to human assistants, agents, advisors and friends.

Imagine asking this question of some AI-enhanced tool in the foreseeable future:

I am thinking about driving to New York from my home in Vermont next week. What do you think?

Most such tools will easily offer a wealth of data, like possible routes, including distances, travel times, attractions, rest stops, and restaurants. Some might incorporate historical traffic patterns for different times of day and even weather forecasts to recommend particular routes.

See also: Could Alexa Testify Against You?  

But, as the noted AI researcher Roger Schank smartly lays out in a recent article, there are many aspects of this question that AI tools will not address adequately any time soon—but that any person could easily do so now.

Understanding such limitations is key to understanding the near term potential of AI and what it really means to be “intelligent.”

Schank points out that a person who knows you would know much about what you are really asking. For example, is your old car up to the task? Are you up to making the drive? Would you enjoy it? How might Broadway show schedules affect your decision about whether or when to go?

“Real conversation involves people who make assessments of each other and know what to say to whom based on their previous relationship and what they know about each other,” Schank writes. “Sorry, but no ‘AI’ is anywhere near being able to have such a conversation because modern AI is not building complex models of what we know about each other.”

In additional to the above question, Schank offers nine other questions that illustrate what people can easily answer but AI cannot:

  1. What would be the first question you would ask Bob Dylan if you were to meet him?
  2. Your friend told you, after you invited him for dinner, that he had just ordered pizza. What will he eat? Will he use a knife and fork? Why won’t he change his plans?
  3. Who do you love more, your parents, your spouse, or your dog?
  4. My friend’s son wants to drop out of high school and learn car repair. I told her to send him over. What advice do you think I gave him?
  5. I just saw an ad for IBM’s Watson. It says it can help me make smarter decisions. Can it?
  6. Suppose you wanted to write a novel and you met Stephen King. What would you ask him?
  7. Is there anything else I need to know?
  8. I can’t figure out how to grow my business. Got any ideas?
  9. Does what I am writing make sense?

Answering these kinds of questions, Schank points out, requires robust models of the world. How do mechanical, social and economic systems work? How do people relate to one another? What are our expectations about what is reasonable and what is not?

Answering Question 2, for example, requires an understanding of how people function in daily life. It requires knowing that people intend to eat food that they order and that pizza is typically eaten with one’s hands.

Answering Question 5 requires analyzing lots of data, which AI can do, and thus help in making better decisions. But, actually making better decisions also requires prioritizing goals and anticipating the consequences of complex actions.

Answering open-ended questions like Question 7 requires knowing the context of the question and to whom you are talking.

Answering advice-seeking questions like Question 8 requires the use of prior experiences to predict future scenarios. Quite often, such advice is illustrated with personal stories.

See also: Insights on Insurance and AI  

Many AI researchers (like Schank) have explored such capabilities but none have mastered them. That does not mean that they never will. It does mean that applications that depend on such capabilities will be much more brittle and far less intelligent than is required.

One way of thinking about AI is that it consists of the leading edges of computer science. Mind-bending computational capabilities are being developed in numerous application domains and deserve your attention. Generalizing those capabilities to human level intelligence, and therefore assuming their widespread applicability, is premature.

Having a clear-eyed view of what AI can and cannot do is key to making good decisions about this disruptive technology—and leaving the irrational exuberance to others.

Why 2017 Is the Year of the Bot

In the 2013 movie “Her,” Theodore Twombly, a lonely writer, falls in love with a digital assistant designed to meet his every need.  She sorts emails, helps get a book published, provides personal advice and ultimately becomes his girlfriend. The assistant, Samantha, is A.I. software capable of learning at an astonishing pace.

Samantha will remain in the realm of science fiction for at least another decade, but less functional digital assistants, called bots, are already here. These will be the most amazing technology advances we see in our homes in 2017.

Among the bestsellers of the holiday season were Amazon.com’s Echo and Google Home. These bots talk to their users through speakers, and their built-in microphones hear from across a room. When Echo hears the name “Alexa,” its LED ring lights up in the direction of the user to acknowledge that it is listening. It answers questions, plays music, orders Amazon products and tells jokes. Google’s Home can also manage Google accounts, read and write emails and keep track of calendars and notes.

Google and Amazon have both opened up their devices to third-party developers — who in turn have added the abilities to order pizza, book tickets, turn on lights and make phone calls. We will soon see these bots connected to health and fitness devices so that they can help people devise better exercise regimens and remember to take their medicine. And they will control the dishwasher and the microwave, track what is left in the refrigerator and order an ambulance in case of emergency.

See also: What Do Bots Mean for Insurance?  

Long ago, our home appliances became electrified. Soon, they will be “cognified”: integrated into artificially intelligent systems that are accessed through voice commands. We will be able to talk to our machines in a way that seems natural. Microsoft has developed a voice-recognition technology that can transcribe speech as well as a human and translate it into multiple languages. Google has demonstrated a voice-synthesis capability that is hard to differentiate from human. Our bots will tell our ovens how we want our food to be cooked and ask us questions on its behalf.

This has become possible because of advances in artificial intelligence, or A.I. In particular, a field called deep learning allows machines to learn through neural networks — in which information is processed in layers and the connections between these layers are strengthened based on experience. In short, they learn much like a human brain. As a child learns to recognize objects such as its parents, toys and animals, neural networks learn by looking at examples and forming associations. Google’s A.I. software learned to recognize a cat, a furry blob with two eyes and whiskers, after looking at 10 million examples of cats.

It is all about data and example; that is how machines — and humans — learn. This is why the tech industry is rushing to get its bots into the marketplace and are pricing them at a meager $150 or less: The more devices that are in use, the more they will learn collectively, and the smarter the technology gets.  Every time you search YouTube for a cute cat video and pick one to watch, Google learns what you consider to be cute. Every time you ask Alexa a question and accept the answer, it learns what your interests are and the best way of responding to your questions.

By listening to everything that is happening in your house, as these bots do, they learn how we think, live, work and play. They are gathering massive amounts of data about us. And that raises a dark side of this technology: the privacy risks and possible misuse by technology companies. Neither Amazon nor Google is forthcoming about what it is doing with all of the data it gathers and how it will protect us from hackers who exploit weaknesses in the infrastructure leading to its servers.

Of even greater concern is the dependency we are building on these technologies: We are beginning to depend on them for knowledge and advice and even emotional support.

The relationship between Theodore Twombly and Samantha doesn’t turn out very well. She outgrows him in intelligence and maturity. And she confesses to having relationships with thousands of others before she abandons Twombly for a superior, digital life form.

We surely don’t need to worry yet about our bots becoming smarter than we are. But we already have cause for worry over one-sided relationships. For years, people have been confessing to having feelings for their Roomba vacuum cleaners — which don’t create even an illusion of conversation. A 2007 study documented that some people had formed a bond with their Roombas that “manifested itself through happiness experienced with cleaning, ascriptions of human properties to it and engagement with it in promotion and protection.” And according to a recent report in New Scientist, hundreds of thousands of people say “Good morning” to Alexa every day, half a million people have professed their love for it, and more than 250,000 have proposed marriage to it.

See also: Top 10 Insurtech Trends for 2017  

I expect that we are all going to be suckers for our digital friends. Don’t you feel obliged to thank Siri on your iPhone after it answers your questions? I do, and have done so.

10 Predictions for Insurtech in 2017

It’s time to reflect on the passing year, mark my predictions from last year and throw some light on what I see 2017 holding in store.

In my post from this time last year, I made a number of predictions, so, now, I wanted to look at how I did. Feel free to jump in and see how close to the mark I was and share your perspectives.

Reviewing 2016 — How did I do?

1. Fintech and insurtech.  In last year’s piece, I said that 2015 was the year of the zone, loft, garage and accelerator and that this would continue in 2016 with more focus. Regarding fintech and insurtech, I was right. We have seen some heavyweight investment (more so in the U.S. and Asia) and no major failures, to my knowledge. Trending up. Points: 1. 

2. Evolution of IoT. In 2015, I wrote, “2016 will be the year we all realize (IoT) is just another data/automated question set.” Evolution here is continuing, but not at the pace I expected. New firms such as Concirrus (and many others) have come up with some great examples of managing and leveraging the ecosystem. Points 2.

3. Digital and data. At the end of last year, I said 2016 would continue to be a big area of growth for both. There’s been progress, yes, and pace and traction ahead of what’s expected. Points 3.

4. M&A will continue but will slow. I think this has slowed this year, with two of the three major regions in the latter half of the year focused on Brexit and the U.S. election. Now, folks are trying to work out where that leaves fintech/insurtech. Points 4.

5. Will the CDO Survive? I said I thought we’d see a move back to the chief customer officer. Well, no sign of my chief customer officers yet! (Although, after writing this, I came across three chief customer officers, so it’s a start). Have you ever asked an insurance company or people inside the company “who owns the customer?” To me, we’re still product-centric rather than customer-centric. Points 4.

6. New business models. I said last year that we’d need to be clear on what the new business model will be — and what it needs to be. This year, there’s been lots of talk in this area, including here at Deloitte in our Turbulence Ahead report. We identified four business models for the future: 1) Individualization of insurance, 2) Off-the-shelf insurance, 3) Insurance as utilities and, finally, 4) Insurance as portfolio. It may take longer for this to materialize, but, without doubt, these models are coming. See my colleague Emma Logan describe these here. Points 5.

7. What we buy and sell. I believed that, last year, we’d move away from a product mindset to become more relevant and convenient. But we’re still in talking mode, although the ideas here are evolving rapidly. Expect an all-risks policy in Q2 2017. Points 5.

8. Cyber is the new digital. There has been an increase in the number of products and players, but there still hasn’t been any personal cyber policy. I expect that to come in 2017 still. Points 6.

9. Partnerships and bundling. In 2015, I thought we’d see a big rise in the partnerships between insurers and third parties. That’s happened. Points: 7.

So I’m marking my 2015 predictions as 7/9 (or 78% ) — a good effort, but I may have been a bit too ambitious.

See also: 4 Marketing Lessons for Insurtechs  

Moving into 2017

Re-reading the above, I still feel all my predictions are valid, be it the end of the CDO, the birth of personal cyber or an all-risks policy. I’ve been involved in enough conversations over the last 12 months to say these are all very real, although some are closer to seeing the light of day than others.

Moving into 2017, here are my top 10 trends to watch:

  1. Speed. Almost all conversations about insurance start with a statement that we’re not moving quickly enough — from transforming and modernizing the legacy estates to quite simply getting products to market quicker. We can no longer wait six months to launch new or updated products. Look at those who managed to capitalize on Pokemon Go insurance cover. In insurance, we’ll move from fast walking to jogging and sprinting. But take caution: This is still a marathon, and there’s still a long way to go. In fact, as Rick Huckstep wrote recently, the sheer speed at which the insurance market has grown in the last 21 months is part of the challenge and attraction.
  2. AI, cognitive learning and machine learning. AI has been long bandied around as a material disruptor. On the back of collecting/orchestrating the data, it’s critical to drive material insight and intelligence from this and allow organizations, brokers and consumers to make subsequent decisions. In 2017, AI will come of age with some impressive examples, including voice. In 2016, we saw Amazon’s Echo and Google Home product launches, as well as some insurers — like Liberty Mutual — giving voice a try. Imagine asking freely, “Am I covered for…?” or, “What’s the status of my claim?” Adding this skill to the mix will likely be table stakes. In addition, AI will augment other solutions to drive value, e.g. robotic process automation, which I wrote about here. All this boils down to getting a better grip on the amazing data we have already while leveraging the vast open data sets available to us.
  3. Line of business focus shift. The insurtech world will make a definitive shift from all the wonderful personal line examples to SME (the next obvious candidate) and to more specialty and complex commercial examples. Will Thorne of the Channel Syndicate wrote a great piece on this in November. While the challenges are harder and more complex, I believe the benefits are greater once we get to them.
  4. Believers. The market has polarized somewhat between those who believe in major innovation and are pushing hard, and those who don’t (or have a different focus and near-term objectives). The range is from those who worry about the next 90 days/half-year results to those who are actively looking to cannibalize their business and investing to find the most efficient way to do this. Here, there’s no right or wrong, with hundreds of organizations strewn across the path. I still believe more will move to the cannibalization route as the first carriers start to unlock material value in 2017, including continued startup acquisition. Oliver Bate (Allianz) had an interesting and positive perspective on this during his company’s investor day in November.
  5. Scale and profitability. Over the last 12 to 18 months, I’ve seen some great startup organizations; internal innovation and disruption teams; VCs; and more. Now is the time to work out how we industrialize and scale these. This is the very same challenge the banking and fintech communities are going through. If you’re an insurance company with 30 million or 80 million global customers, should you be worried about Startup X that has 10,000 or 100,000 customers? If they do manage to scale, can they do so profitability? This reminds me of a recent article about how unprofitable Uber is, but, with millions of engaged customers, they have our attention now. Profitability will become front and center. In fact, Andrew Rear over at Munich Re Digital Partners put together a good post on what the company looks for and why he and the team chose the six they did.
  6. Orchestration. With all of these startups in insurtech, we’ll need to quickly understand what role they play. Are they a platform play, end product play, point disruptor or something else? Regardless, given the volume and velocity of data generation, the importance of both API connectivity and the ability to orchestrate it will increase dramatically. For me, these are table stakes.
  7. External disruptors. In the Turbulence Ahead The Future of General Insurance report released earlier this year, we identified six key external disruptors that are happening regardless of the insurance industry. These are 1) the sharing economy, 2) self-driving cars and ADAS, 3) the Internet of Things, 4) social and big data, 5) machine learning and predictive analytics, and 6) distributed ledger technology. The key for me within insurance is to identify what role we’ll play. I believe we’ll continue to firmly be the partner of choice for many given our societal and necessary position in the global economy.
  8. Micro insurance. Here, I specifically mean the growth of micro policies, covering specific risks for specific times. Whereas we typically annually see 1.1 policies per customer, we’ll see eight to 10 micro policies covering a shorter period (episodic or usage-based insurance) as per our business models described in the Turbulence Report. This will be true for all lines of business. We’ve already seen some great launches in this space — including Trov, which partnered with Munich Re in the U.S., AXA in the U.K. and SunCorp in Australia. There’s been global access through partnering with established players that has created a new way to market to the next generation. While we switch this on manually by swiping left and right (given some of the external disruptors and location based services), this will very much be automatic going forward. Insurers will need to find new ways to orchestrate, partner and find value to bring in clients. It won’t be just one policy, it will be many that they orchestrate to deliver clients everything they need.
  9. Blockchain and DLT. I almost didn’t include blockchain here, but two factors have led me to include this for the first time: 1) the number of requests we’re now seeing in the market for both specific solutions and more education/use cases and 2) the fact that nine of the 18 startups in the FCA’s new Sandbox are blockchain-related. In 2016, we saw lots of PoC examples, trials and the first live insurance product on the blockchain (see: FlightDelay). Some use cases are more developed than others, and some markets are more suitable than others (I’m still looking for good examples in personal lines), so I believe this will evolve in 2017 but that there won’t be scale breakthroughs. However, along with the World Economic Forum, I firmly believe that “The most imminent effects of disruption will be felt in the banking sector; however, the greatest impact of disruption is likely to be felt in the insurance sector.” We still must ask, “why blockchain?” Just because you can use it? It needs to be the right solution for the right business problem. Horizontal use cases such as digital identity or payments offer compelling use cases that can easily be applied within insurance. In many ways, blockchain, for me, feels much more like an infrastructure play in the same way we would do core systems transformation (policy, claims, billing, finance, etc.)
  10. Business as usual — for now! Partly related to No. 4, we still need to run our business. How we do this and how we set up for the future will be another challenge — not just from a technology perspective but from a people and organization design perspective. (How we work, collaborate and more.) What are the transition states from our current models to a new world in 12, 24 or 36 months. Forward-thinking organization are putting plans in place now for their organizations in the years to come. This will become more important as we embed, partner and acquire startups and move toward new ways of engaging and working with customers.

Interestingly, there are now also so many accelerators, garages, hubs, etc. that startups all now have a lot of choices regarding where to incubate and grow. This presents a whole new challenge on the rush to insurance disruption.

See also: Asia Will Be Focus of Insurtech in 2017  

Finally, there are two other observations I wanted to share:

  1. China. While I don’t spend any time in China, it’s hard not to be in awe of what is going on — specifically, the speed and scale at which things are happening. China’s first online insurer, Zhong An, did an interview with Bloomberg regarding what the company is doing with technology (including blockchain) and, more importantly, its scale ($8 billion market cap in two years, 1.6 billion policies sold) — and the only concern from the COO, Wayne Xu, is that the company isn’t moving quickly enough! Step away from this and look further to what’s happening with disruption in general with Alipay and others from the BAT (China’s equivalent of GAFA — Baidu, Alibaba and Tencent) is simply amazing. There’s a good FT article on Tencent, the killer-app factory, and the sheer speed and scale of disruption.
  2. Community. The global insurtech (and fintech) community is an amazing group of people from around the world who have come together across borders and time zones to further challenge and develop the market. Each geography has its own unique features, mature players, startups, labs, accelerators, regulators and, of course, independent challenges. We don’t always see eye to eye, which makes it all that more rewarding because you’re challenged by industry veterans and outside-thinking entrepreneurs. This year’s InsureTech Connect in Las Vegas with more than 1,600 people was truly amazing to see. Things have clearly moved far beyond a small isolated hive of activity with varying levels of maturity to a globally recognized movement. It was great to meet and to see so many carriers, startups, VCs, regulators and partners looking to further the conversation and debate around insurance and insurtech. This community will, no doubt, continue to grow at a fast pace as we look for insurtech successes, and I look forward to seeing how the 2017 discussion, debate and collaboration will continue.

As always, I look forward to your feedback! What I have I missed?

Here’s to an exciting 2017!