Ace buys Chubb: what It means for insurance technology

Donald Light

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Jul 1st, 2015

Today’s blockbuster announcement of Ace buying Chubb will have a lot of industry ramifications—some of which will play out in the IT sphere.

No doubt there has already been an IT assessment element in each insurer’s due diligence efforts. Between now and the effective date of the merger, there will be a lot of planning focused on:

  • Efficiencies and platform rationalization–aka “let’s figure out what is the right number of core systems, which core systems will be the survivors, and how data conversion and integration will work”
  • Cloud, SaaS, data management/stores, and analytics
  • Professional service and SI support capabilities that can scale to the new Chubb
  • Which systems will best support a digital roadmap

Some seemingly redundant systems may survive—at least over a 1 to 3 year period. For that to happen, the business (and/or various geographies’ compliance) requirements of the operating units using these system will be too divergent or too difficult to quickly build into a single surviving system.

All this reinforces the reigning market message to insurance technology firms. If you want to be around in 10 years:

  • Design highly configurable and agile systems that feature ease of integration
  • Have enough scale to meet the needs of bigger and bigger insurer customers—grow, merge, or wither

 

The changing demographics of the U.S. and how they affect insurance

Tom Scales

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Jun 26th, 2015

The U.S. Census Bureau recently released new information on the diversity of the population in the US and it is a fascinating read, at least for an insurance nerd like me.

Census Report

To summarize some key points for insurers:

  • For the first time, Millennials outnumber Baby Boomers. This means that your potential target market is more technology literate and less understanding of the weaknesses of your systems. No phone app? They’ll find the carrier that has it.
  • For the first time, more than 50% of youth (5 years and younger) are minorities. If you don’t have diverse marketing programs, this single statistic says you should, and will be reinforced below.
  • The 65 and older group grew to 46.2 million, a growth of over 1.5 million in one year. This group is also more technologically literate than ever before. Don’t underestimate this group’s expectations.
  • Only ten states have a majority male population, highlighting the need to market directly, and properly, to women.
  • All race and ethnic groups had more births than deaths except non-white, non-Hispanic, where the population declined, again highlighting the need for diversity marketing.
  • Hispanics outnumber Blacks 55.4 million to 45.7 million. While both should remain targets, specialized Hispanic programs make sense.
  • Asians represent 20.3 million, a growth of 3.2% in a single year.

For the most part, this information is of interest to the curious and to the actuaries, but it strongly reinforces the image of the United States as a melting pot. We’re diverse, we’re all both unique and alike, and the needs of our customers are rapidly changing. If you’re not offering new ways to engage, including Exchanges, Roboadvisers, Mobility and more, your company will be left behind.

All of this highlights a particular need – the need for Innovation. How do I connect this raw data to the need for Innovation? It’s simple. Our industry has a well-deserved reputation for moving slowly and for being behind other financial services companies. We are even farther behind companies in other industries. The barriers to enter our space have never been lower. Capital is cheap, technology is improving and the marketplace is shifting.

Which leads to the question: Has your company culture embraced innovation? Do you have a process to encourage experimention and fast failure? Do you have an approach to change that can bypass the traditional, and constraining, project gates to fast track new ideas?

Having discuss this topic with some many companies, it is clear that many, if not most, insurers have not reached this step. The desire is there, but that last leap to make it happen is often lacking. There are stellar examples of exceptions, but even more examples of the status quo.

My colleague, Mike Fitzgerald, has made innovation his primary focus for the last eighteen months and his research and his work is insightful. If you have not spoken to Mike, then I strongly suggest such a call is worthwhile. His insights into innovation in insurance are wonderful and can help you company overcome the barriers and hurdles.

It is an exciting time in our industry. Let’s all be part of the change.

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Open source, analytics and the pace of change

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Jun 26th, 2015

I love spotting ironies such as how this years Strata | Hadoop World conference (the UK one) spent more time discussing Apache Spark and whether it was a successor to Hadoop or another tool in the box than it did discussing Hadoop and it’s applications. It was great to see members of the insurance industry there amongst the retailers and banks as well.

“But wait?!?!!” I hear you say, “Hadoop isn’t all that old is it?” Herein lies the great challenge for the CIO faced with requests for open source tools. These are dynamic, social projects without the same stickiness as those legacy systems insurers spend time worrying about. Not only do users / consumers / fans of open source software shift between projects but the contributers / developers do too. With the rising use of tools like R, Python, Linux, GIT, Hadoop, Spark, Docker, Capistrano and all manner of wacky projects on the go and being adopted by insurers how should a CIO respond? Prohibition tends to lead to shadow IT and surprises down the line far more unwelcome than managing some new software. The key advice is to understand these types of projects can be more transient than other enterprise software. Experiment with them but be careful of expensive, enterprise installations that are hard to extract later down the line. In truth insurer adoption of some of these technologies will outlive the fashion for them but it still requires planning for their removal or worst case, their ongoing support.

I promised analytics in the title too didn’t I? Well Spark is all about real time analytics and is having an interesting impact in the machine learning and predictive modelling space. It gets around some of the issues with interacting with Hadoop while still delivering performance. With open source projects survival of the fittest is the order of the day, far more so than in classic insurance software markets. Hadoop has it’s place, with many insurers globally investing in it.We will see new fashions in analytics approaches and more opensource tools I’m sure. Some will follow the Dodo.

For those interested in Hadoop have a look at my report from 2011, when Hadoop was new and cutting edge. It seems it requires an update.

Listen up auto insurers. Driverless Cars? No Problem. Collision Avoidance Technology? Hold On!

Donald Light

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Jun 23rd, 2015

The just released report by the National Traffic Safety Board (NTSB) contains some important findings on collision avoidance systems’ potential to prevent or mitigate the severity of rear-end collisions.

Some of the data points are eye-popping: a predictive analysis found that this technology could prevent or reduce deaths and injuries in 87% to 94% of all accidents. A private study by the trucking firm Con-way found that these technologies reduced rollovers by 41% and rear-end collisions by 71%. Still impressive, but less startling, the Insurance Institute for Highway Safety found a reduction in claims frequency in three luxury models of 7% to 14% (without estimating changes in severity).

The good news for driver and passenger safety is that auto manufacturers are competing vigorously to offer these features in their new cars and trucks (The NTSB study has a 9 page Appendix listing these manufacturers and models).

The NTSB study may also nudge the National Highway Traffic Safety Administration to, someday, mandate these technologies in new vehicles.

The long term implications for auto insurers though are similar to the implications of autonomous vehicles: fewer and less severe losses, resulting in competitive and regulatory pressure which will drive down premiums substantially.

The auto insurance business is going to shrink. But the real question is how fast? Or to put the question more precisely, when will there be a critical mass of autonomous and collision avoidance-equipped vehicles on the road? The NTSB study is speeding up that timeline just a little bit.

Why I’m passionate about customer service in Life Insurance

Colleen Risk

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Jun 1st, 2015

Last week, I was watching the National Spelling Bee as they crowned two winners for the second consecutive year. What an amazing performance! One of the winners spoke of her passionate pursuit of winning the Bee. The comment made me reflect on a question I was asked during my interview with Celent. The question was whether I was passionate about life insurance.

I found the question quite interesting. I thought of two recent customer service experiences.  My father passed away a year ago and was smart enough to have protected my mother with life insurance. This is the whole point of our industry – to be there in the most difficult time of people’s lives. I’m pleased to say that the claims representative I spoke with was compassionate, efficient and knowledgeable. She obviously had a passion for helping others.

The second interaction was not as happy. A problem on a different insurer’s customer portal kept me from completing a desired change. I called the customer service number to complete the update. The customer service representative directed me back to the website. When I told her it was not working, she implied that I was the problem since it was working for everyone else. After a discussion about my perceived ineptness, she reluctantly made the requested change. She was disinterested, annoyed and uncaring.

With so much of the today’s interaction being digital, it is crucial that companies hire people with passion. The choices are too many and the personal interactions too few to not take advantage of every opportunity to build customer loyalty.

Anything worth doing should include a passion to become the best. Whether you are a spelling bee participant, an analyst or a customer service representative, passion drives the best results.   It was a good question, and I can firmly state that I AM passionate about life insurance.

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Striking data point from Mary Meeker’s Internet Trends 2015 report

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May 29th, 2015

I spoke this morning with an operations executive at a large insurer which distributes personal lines products through independent agents. He said that they are working feverously to deliver digital service tools to the customer service representatives (CSRs) at agents because they know that the average CSR is now 19 to 26 years old. This insurer is transitioning from a telephony-centered approach to one which includes chat, secure messaging, and intelligent avatars in order to meet CSRs’ expectations about how service should be done. As any insurer distributing through the independent channel knows, the company that keeps the CSRs happy wins!

In our innovation research, we repeatedly see the influence of Millennials’ expectations around the consumer experience, but a data point from Mary Meeker’s Internet Trends 2015 report identifies an equal, if not higher, motivator for change from workers. Millennials now represent the largest percentage of the U.S. workforce.

Take a close look at Slide 109. It shows that in 2015, for the first time, Millennials make up 35% of U.S. workers. Gen X and Boomers represent 31% each. The data signals a tipping point and it is pretty clear which way this trend is going to continue.

Watch as the buzzword “Worker Experience” is added to the already well-worn “Consumer Experience.” For insurers that want to gain an advantage with their own workers, and with their distributors’ CSRs, the field is wide-open. All they need to do is innovate, experiment, put some funds at risk, and transition to digital working.

KPCB

A four year old’s employment records (or how not to handle a data breach)

Tom Scales

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May 28th, 2015

Yesterday one of my four year old twins received a letter from a major health insurance carrier (we’ll leave them nameless, tempting as it is). The letter states that the carrier had a data breach and that his information may have been included. The list was pretty extensive, including name, address, telephone number, email address, date of birth, social security number and employment history.

That’s a pretty big list and everything you need to steal an identity. They assure me that no health information was shared, but I think they have their priorities wrong. I don’t care if the thief knows I have high cholesterol, but I do care that they have my social security number. I admit I am curious about the employment history of my four year old – I think he has been holding out on me. I wondered how he had so many Legos.

The challenge? We’ve never had a policy from this particular carrier. Their FAQ site (a whopping four pages of minimal information) says it could have been because they process for other carriers, but nope, none of them either.

So I set out to find out more information, particularly whether others in the family were affected, since we are all on the same policy (Mom, Dad and five small kids).

I started my quest at 8:45am, on the website, and then the phone center opened at 9am. What a frustrating two hours. After talking to 11 people, from 4 different companies, do I know the answer to any of the questions? Not a single one. It all started with the vendor that the problem was outsourced too. I feel for those phone clerks, as they were provided almost no information. I then found a way to the carrier (a blog post in its own right), who didn’t know any more, but managed to transfer me to two other insurance companies, neither of whom had a clue why as they didn’t have a breach and I was never their customer.

My concern is that this means they don’t even know what was stolen, where it was stolen, who’s information was stolen and more. If they don’t know that about me, what about you?

I honestly don’t know how you protect yourself. You can’t really go off the grid. I could do without credit cards, and go to cash, but I can’t do without utilities or health insurance. I also understand that identity theft is big business, but the protections taken by major companies feel so lax. This is the FIFTH major breach of our family in less than 18 months. My credit card, from a major bank, has been replaced three times (only one breach was their own).

So to the point of the post, for those still with me:

If I was responsible for data security at any of these firms, I’d fire myself. There are solid, dependable companies doing security work. If you r company has not hired one to test your security, do it. Do it today. You should be doing penetration tests, at least annually. You should have solid company policies on data access, and that access should be extremely limited. People need information to do their jobs, but they don’t need all the information. Does your company have a data governance policy? If not, start today. We all know that IT budgets are limited and that our user communities, including our customers, want more and broader access. I just caution that you move with speed, but not without safeguards. Everything can be breached. Your firewalls, your apps, your website and even, as in the case of one breach, your cash registers.

More important than all of this, though, is how you handle the breach when it occurs. Even with the most amazing safeguards, some pretty smart people, and governments, are hacking into private data. When it occurs, it should not be a shock to your company. You shouldn’t mobilize a task force after it happens. You should never consider this an IT problem – it is a major problem for the most senior levels of your company, and your reputation. Your company probably has, I hope, an IT Disaster Recovery (DR) plan. Does it include a data breach? Many don’t. They worry about floods, power outages, even pandemics, but not a data breach. Even if your DR plan does include data breach, are the actions your company will take fully laid out? If you are going to use a vendor, have they been chosen and briefed and is the conduit of key information already prepped? Is the spokesman for your company prepared and ready to speak publicly immediately? In my case, the time between the public announcement of the breach and the time we received the letter was over three months. Three months!

Hopefully this post will cause at least one reader to start asking questions in their company and that those questions will be well received.

You don’t want to be the next company in the news, do you?

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Your Natural Best Friend will certainly know that you are sad. But will your customer service chat bot know?

Donald Light

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May 27th, 2015

AI and machine learning things are moving right along.

A few months ago, in a Celent report, I predicted the emergence of a “Natural Best Friend,” a term combining “natural language” and “best friends forever.” However, there is nothing organic about the Natural Best Friend; it is completely a product of technology.

The Natural Best Friend will at some point pass the Turing Test (interacting with a person in a way that is indistinguishable from how another person would interact). Natural Best Friends will become sources of not only trusted information and advice, but also of companionship, friendship, and perhaps even some form of wisdom and intimacy.

The use of the Natural Best Friend has obvious applications in throughout the entire insurance life cycle: from underwriting to service to claims. Even the possible characteristics of companionship, friendship, wisdom, and intimacy may be of use to insurers. Consider insurers’ brands, built over decades, which stand for trust, reliability, and succor. Once it becomes socially normal to have a personal relationship with the Natural Best Friend, insurers’ (and many other service industries’) sales and service processes will change dramatically.

IBM has just announced it is developing customer service software that can interpret the customer’s emotional state by the content and pattern of the customer’s chat messages. Somewhere in the future, the software may be able to analyze a customer’s voice to determine the emotional playing field. Here’s a link to the WSJ story (warning: this might be behind a paywall).

The family tree that will produce a baby boom of Natural Best Friends now has a new branch.

The critical importance of testing

Tom Scales

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May 26th, 2015

It should go without saying, but good testing is absolutely critical to producing technology that benefits your organization. As a former insurer CIO, I can attest that the group most often squeezed to meet a deliverable date is the quality assurance team that has the incredible responsibility to ensure accuracy.

Perhaps our organization was unique, but having also spent years at vendors, doing implementations, I see it happen over and over. Projects run late and rather than adjusting the date, or the project content, testing gets squeezed, often with unintended consequences.

The corollary to poor testing is “Day 2.” This is code in the IT community for “we didn’t get it done, but will let someone else worry about it later.” This is compounded in the Life insurance industry, as “later” can sometimes be measured in years. This means that the team that built the code may be long gone by the time Day 2 rolls around, or worse, the fact that there are open items will be lost in the history or lore of the organization.

The reason I bring this topic up is an article I read recently that dramatically shows the challenges of inadequate testing. For the nerds amongst our readership, one of the classic fails of programming is an overflow. For the non-nerds, this just means you have a counter in your program that isn’t big enough. Rather than continuing to count up, it wraps back to zero and starts over. The results can be major to a program and it is an error that might not be discovered for years. Remember, you’re counting something, so reaching this limit can take a long time.

The latest programming error to experience an overflow? The Boeing 787 Dreamliner.

It seems that you need to reboot the plane every 248 days or risk the plane falling from the sky. 248 days, measured differently, 2^31 one-hundredths of a second. Now I have no idea what they’re counting, but apparently it is pretty important.

Should testing have caught this? Of course. Did it? Apparently not.

So the next time you want to cut short testing, remember this post and ask yourself “should we?” You may not have a plane falling from the sky, but you could have a hidden calculation error that could cost your company. Take a small error and multiply it over time and it quickly becomes a very, very large error. A potentially catastrophic financial error.

The rise and fall (and rise) of Artificial Intelligence

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May 21st, 2015

Artificial intelligence has been around nearly as long as humans have been able to think about themselves, about thought and what they do. Empathy is wired into us – some more than others but we are all capable of thinking from another’s point of view. This capacity leads us to anthropomorphize things that aren’t human, to imbue things in our daily lives with human qualities like moods, characteristics and personality. When we build puppets, robots, models that look sort of human it is easy to for us to assign it with greater power, ability and promise than is really there. For marketers in other fields, to have consumers attribute their products with ‘magical’ properties would be a dream come true but for artificial intelligence it is a nightmare – one the industry has expended funds marketing against.

Artificial intelligence has delivered many great tools which today we take for granted. Our phones listen to us and understand our requests in the context of our calendar, our camera’s recognise faces and social networks tell us who those faces belong to, machines translate words from one language to another (although don’t get the translations tattooed just yet) and the list goes on. We chuckle at these mistakes these learning and adaptive systems make, we see the huge strides and investment and we expect a new human like intelligence to emerge in the short term. Around the middle of every decade since the 60’s there has been a peak in excitement for AI, a frustration with it’s lack of progress, and a reduction of funding or AI winters as they are called. In the eighties it was LISP machines, in the nineties it was expert systems. Now in the twenty-tens (I thought it was teenies but that’s a kids show apparently) we are seeing a resurgence of AI, a blending of machine learning, predictive modelling and cognitive computing along with self driving cars.

This raises some rare and interesting questions:

  • Are we headed for a new AI winter?
  • Or an AI apocalypse?
  • Also, will I still be cleaning my home in 2020?

It is certainly true to say the set of tasks we can expect software and physical computing systems to do is vastly increased compared to just a decade ago, and massively so since the 60s.

Doing all the things humans can do and living in our society, empathising and understanding us in that broad context is still well beyond computers – but engaging with us in specific, well-defined domains such as about our calendar or what we would like to buy from the shop is well within their grasp today.

Previously difficult tasks such as searching a database for information, reading that from a screen and keying it into another screen is now entirely possible – see the earlier blog post on bots.

Having a drone fly itself around an obstacle to reach an objective is still very hard. Having a vehicle drive itself on the road is in fact easier, albeit most humans don’t benefit from lidar sensors, ultrasonics and eyes in the back of their head (alright, bumper).

It is good to see AI on the rise again – I loved the topic ever since getting into programming and getting involved in a cognitive psychology course some years ago. I recall writing an expert system in Pascal back in the 90s.

I am concerned, as the insurance industry should be, by a new AI winter. Self driving cars and vehicles have the potential to make the roads safer for all. We will when we see them, imbue them with more power than they have – this is human nature. We will, in the not too distant future, hear people say things like, “the car likes to give cyclists a lot of room on the road” or “the car prefers to take this corner at a fair speed” – imbuing a complex machine with sensors and programming with preferences, desires and likes – human qualities.

When the first death comes we will ask how could it do such a thing. When an automated car is put in a position where it must decide between a set of actions – each leading to injury, we will hear people discuss why it chose to do what it did, people may say, “it did the best it could” or worse, “no person would ever have done that, this is why machines shouldn’t be able to choose.” The latter of course revealing the human construct, an unspoken contract – our expectation that smart or intelligent systems will operate like us, share our values, our culture, that we can predict their actions in our context. This is the greatest threat to AI and always has been – the expectation, the contract that the new intelligence will be like human intelligence. Some winters are due in part to that contract being broken, to these systems not living up to the expectation and making inhuman mistakes.

There are a set of tools available now that are not intelligent but they are smart and they are powerful. We would be remiss in our duty to our customers and shareholders if we do not leverage them. Manage expectations about these powerful tools and understand the very real limits that exist on them. If we can do this we may benefit from the AI boom and avoid another AI Winter.

Will we see an AI apocalypse? Ironically it’s not the human like intelligence that may be our greatest threat but simpler intelligences. A human like intelligence could empathise, could act in acordance with values and could be relatively predictable (in a human way). There are many stories across science fiction of smart robots that act like insects and replicate, in fact that only make copies of themselves, that pose a great threat to any civilisation. They are not intelligent, they don’t want to kill off all life in the galaxy – they just turn all the available resources into copies of themselves which would have that effect. We are much closer to building that threat frankly (with drones, 3d printers, etc.), than a super intelligence that decides all human life is worthless. For now though – I expect these things to stay firmly in the space of science fiction. I include this discussion here because it does demonstrate a key difference between smart with unintended consequences and ‘intelligent’ – a lesson worth bearing in mind for those adopting AI.

Finally – will we see robots cleaning our homes by 2020? Well roomba is out there and sort of does that. Stairs and steps are still a huge challenge to robots. Frankly differentiating furniture, pets, clutter, magazines, rubbish, dust and recycling in a moving environment is still a very complex issue.

As in insurance, I think smart things will make cleaning easier and assist those who invest but there’ll be a role for human intelligence in ensuring the pets aren’t recycled and the customer ultimately gets the service they expect.