The Evolving Role of Architects

The Evolving Role of Architects

In the last couple of weeks I’ve had the great opportunity to spend time with IT architects of various sorts both inside and outside of the insurance industry. The discussions have been illuminating and offer different visions and futures both for technology that supports insurers and for the future of the architecture function in insurers.

One of the main events that allowed for this conversation was a round table held in London with architects from insurers. The main topics were the relevance of microservices style architectures to insurance, the role of the architects in AI and InsurTech and the future role of architects at insurers. Another event that offered an interesting contrast was the inaugural London Software Architecture Conference which I'll call SACon below (Twitter feed).


I won't fully define microservices here but briefly it’s an approach to delivering software where each service is built as it’s own application which can be scaled independently from other services.

Microservices as a way of delivering software was the default approach at the SACon. There were sessions where architects sharing stories about why sometimes you had to work with a monolith or even making the case for not having the services in discrete applications. Meanwhile at the round table the monolith was the default still with the case being made for microservices in some parts of the architecture.

There are use cases where microservices make a great deal of sense, particularly in already distributed systems where a great deal of data is being streamed between applications. Here the infrastructure of microservices and the libraries supporting the reactive manifesto such as Hysterix and Rx* (e.g. RxJava) and indeed one insurer related their use of microservices to support IoT. Others discussed using this style of approach and the tooling surrounding these architectures to launch new products and increase change throughput but in all cases these were far from replacing the core architecture.

For now microservices is not the default for insurer software but is certainly a tool in the box. An observation or two from SACon from those looking to adopt: First it doesn’t solve the question of how big a service or a component is, something architects need to discuss and refine and; Second, microservices needs a great deal of automation to make work, a topic covered in our DevOps report to be published shortly.

Architects and AI

I have a background with training and experience both in computer science, AI and machine learning. One thing that I noticed going to the analytics conferences where AI is discussed is the absence of IT representation – plenty of actuaries, MI/BI folks, marketing folks – was this a place for architects?

Most insurers present at the round table had activity within the organisation for AI. For the most part only data architects are involved in this discussion – AI being distinct from business and applications architecture for now. It’s my opinion that AI components will form part of the wider applications architecture in the future, with AI components being as common place as programmed ones.

Architects and InsurTech

Here is an area where architects can more immediately contribute in a meaningful way both in reviewing opportunities and unique capabilities from InsurTech firms and in discussing integration where acquisition rather than investment is the goal.

The challenge here of course is the age old challenge for architects – to have a seat in the discussion the architect function needs to demonstrate the value it can bring and it’s internal expertise.

Finally, one amusing discussion I had was with a few architects from startups. As I discussed legacy systems they also related seeing legacy systems in their organisations – albeit the legacy systems were 2 or 4 years old rather than 20 or 40 years old. The intriguing thing here was the reasons for them becoming legacy were the same as insurers – availability of skills, supportability and responsiveness to changing demands. It may hearten architects at insurers that start ups aren’t immune to legacy issues!



The Rise and Rise of Analytics in Insurance

The Rise and Rise of Analytics in Insurance

As noted in our prior research insurance has always been an industry that relies on advanced analytics and has always sought to predict the future (as it pertains to risk) based on the past. (For research on advanced analytics in insurers see here, here and here).

As observed in the last post here analytics, AI and automation has been a key focus of InsurTech firms but do not assume that the investment is limited to newbies and start-ups. I have for a few years now been attending and following the Strata+Hadoop conferences and others focused on advanced analytics and the broad range of tools and opportunities coming out of the big data organisations. This last week I attended a conference focused on the insurance industry and was surprised to see the two worlds have finally, genuinely overlapped – just take a look at the sponsors.

As Nicolas Michellod and I have noted in the past, insurers have already been investing in these technologies but only those that have made the effort to speak “insurance”. What the conversations at Insurance Analytics Europe (twitter feed) demonstrated was a new focus on core data science tools and capabilities. This continued the theme from DIA Barcelona (twitter) earlier in the year.

The event followed InsTech London’s meeting (Twitter) looking at data innovation and it’s opportunities for Lloyd’s, the London market and the TOM initiative. Here the focus was on InsurTech firms that would partner on analytics, would sell data or would enable non-data scientists to benefit from advances in machine learning, predictive analytics and other advanced analytics disciplines.

While this trend of democratising advanced analytics was discussed by analytics heads and CDO’s at the analytics conference the focus was much more on communicating value, surfacing existing capability and tools within the organisation and to put it bluntly, getting better at managing data.

In short – AI, Analytics, Machine Learning, Automation – these were all hot topics at InsurTech Connect and similar events but for the insurers out there – don’t assume these are purely the domain of InsurTech. Insurers are increasingly investing in these capabilities which in turn is attracting firms with a great deal to offer our industry. For those big data firms that ruled out insurance as a target market a couple of years ago – look again, the appetite is here.

As a techy and AI guy of old I am deeply enthused by this focus and excited to see what new offerings come out of the incumbent insurers and not just InsurTech.

Do have a look at the aware machine report and the blog too. We’re increasing our coverage in this area so if you have a solution focused on this space please reach out to Nicolas, Mike or myself so we can include you and for the insurers look out for a report shortly.


Changing the Landscape of Customer Experience with Advanced Analytics

Changing the Landscape of Customer Experience with Advanced Analytics

That timeless principle – “Know Your Customer” – has never been more relevant than today. Customer expectations are escalating rapidly. They want transparency in products and pricing; personalization of options and choices; and control throughout their interactions.

For an insurance company, the path to success is to offer those products, choices, and interactions that are relevant to an individual at the time that they are needed. These offerings extend well beyond product needs and pricing options. Customers expect that easy, relevant experiences and interactions will be offered across multiple channels. After all, they get tailored recommendations from Amazon and Netflix – why not from their insurance company?

Carriers have significant amounts of data necessary to know the customer deeply. It’s there in the public data showing the purchase of a new house or a marriage. It’s there on Facebook and LinkedIn as customers clearly talk about their life changes and new jobs.

One of the newest trends is dynamic segmentation. Carriers are pulling in massive amounts of data from multiple sources creating finely grained segments and then using focused models to dynamically segment customers based on changing behaviors.

This goes well beyond conventional predictive analytics. The new dimension to this is the dynamic nature of segmentation. A traditional segmentation model uses demographics to segment a customer into a broad tier and leaves them there. But with cognitive computing and machine learning an institution can create finely grained segments and can rapidly change that segmentation as customer behaviors change.

To pull off this level of intervention at scale, a carrier needs technology that works simply and easily, pulling in data from a wide variety of sources – both structured and unstructured.

The technology needs to be able to handle the scale of real-time analysis of that data and run the data through predictive and dynamic models. Models need to continuously learn and more accurately predict behaviors using cognitive computing.

Doing this well allows an carrier to humanize a digital interaction and in a live channel, to augment the human so they can scale, allowing the human to focus on what they do best – build relationships with customers and exercise judgment around the relationship.

Sophisticated carriers are using advanced analytics and machine learning as a powerful tool to find unexpected opportunities to improve sales, marketing and redefine the customer experience. These powerful tools are allowing carriers to go well beyond simple number crunching and reporting and improve their ability to listen and anticipate the needs of customers.

US patents in 2015 – who are the leaders?

US patents in 2015 – who are the leaders?
I thought this chart from the firm Statista was interesting and topical given my post from last week. What particularly caught my eye was their observation that IBM is number one for the 23rd straight year. In addition, over 2,000 of their patents focus on cloud computing and cognitive computing, both areas of particular interest to insurance and the broader financial services industry. And for those that wonder (like me), Apple was in 11th place, just 18 patents short of 10th.   Infographic: Top 10 U.S. Patent Recipients | Statista You will find more statistics at Statista

Insurance companies are embracing technology — for investment

Insurance companies are embracing technology — for investment
Celent frequently observes that many insurers, particularly in the Life space, are running aging, if not antique, software systems. They rely heavily on mainframe systems, often in languages such as COBOL that are becoming more difficult to support. The positive news is that our research shows continued growth, if modest, in IT budgets with modernization and innovation a frequent focus. With this as the foundation, it is interesting to see continued growth in insurance company’s venture capital arms in financial services oriented technology, or Fintech. Industry research shows an incredible growth path in Fintech start-ups, from a modest 400 or so in 2010 to over 12,000 in 2014. While the numbers are not yet in, we expect the 2015 numbers to continue this dramatic growth path. The insurers with venture capital arms are too numerous to list, but are a who’s who in the industry. Examples include AXA Strategic Ventures, MassMutual Ventures, American Family Ventures, and Transamerica Ventures. While many of the examples are US-based, it is a global phenomenon. A great example is Ping An Ventures, a subsidiary of the Chinese insurance company Ping An. Celent tracks many of the insurance related investments and we see several focus areas. One is in financial management and modeling, such as Roboadvisors, across both Life and Health. Good examples include Northwestern Mutual’s acquisition of Learnvest and AXA Strategic Ventures and MassMutual Venture’s investment in Limelight Health. MassMutual is also the parent company of Haven Life, a fully online sales organization dedicated to Life insurance. Other hot areas, not surprisingly, include analytics and the ever popular Internet of Things. The most recent investment, announced just yesterday, is AXA Strategic Ventures’ investment in Neura. Neura’s tagline is “Enrich your products with personalized insights from the lives of people who use them”. While a little heavy on the buzzwords, the basic view is that Neura analyzes data about you and recommends personalizations based on that information. The basic premises appears to link the Internet of Things, such as your Fitbit, to your social media presence, to your calendar and more. There are, of course, other companies overlapping this space (with 12,000 new companies, you would expect competition), such as Vitality and The competition is encouraging, as it fosters continuous innovation. As the Millennials now outnumber Baby boomers (at least in the US), new technologies to engage them in insurance can be game changers. I am particularly intrigued with the technology companies, like these, that are focusing on changing the entire approach to Life insurance. The life insurance sale has always been focused on a negative experience – death of a love one. No one wants to talk about dying, and everyone wants to believe they will live many more years. When I talk to people that are just reaching an age where they really need life insurance, I get push back, and a lot of it, about everything else more important in their lives. My response that they need to protect their family often falls on deaf ears. By changing the discussion from “you are going to die”, to “how can we help you live longer”, we are opening up a much more comfortable discussion. In addition, this is a generation that will share everything on social media, to the point of embarrassment, so asking for more information to make their experience more intimate should be fairly easy. The investments and technology are exciting. It is wonderful to see insurance organizations finally catching the technology wave, after lagging for so long. Whether it be the Internet of Things, Usage based insurance, Micro insurance, behavioral underwriting or more, the staid insurance industry is breaking out. Some technologies are even a bit fun, such as the expanded usage of drones. Now before I get you too excited about the reinvention of insurance, I suggest you read a counterpoint to this post, from my colleague Donald Light, entitled A long time ago in a galaxy far far away, I went to a two hour meeting to reinvent insurance. He makes some very valid points about the managing our excitement. Another colleague, Craig Beattie, shares a similar bit of skepticism in his post What if… the insurance industry didn’t innovate? I guess I am forever the optimist and want to believe the excitement and change is real.

Freud in a box – the Aware Machine

Freud in a box – the Aware Machine
In the week since the release of the Celent report, Machine Intelligence in Insurance: Designing the Aware Machine, I have been involved in several fascinating discussions around a new level of personalization in insurance. An insurer called me to ask if there are any vendors providing intelligent machine services that can analyze social posts of a person and slot them into one of several pre-described personas. It was fascinating to contact some of the vendors involved in the report and find out just how far along they are in using intelligent machines to personalize down to the unit of the individual! At the same time, my colleague, Zil Bareisis on the Celent Banking team, blogged about a new type of personality test, Personality Insights powered by IBM’s Watson. According to the description of the system, the test “uses linguistic analytics to extract a spectrum of cognitive and social characteristics from the text data that a person generates through blogs, tweets, forum posts, and more.” Interestingly, it claims to be able to reach conclusions just from a text of 100 words. (Zil’s blog is here: Don’t be surprised if your bank knows not just who but also what you are in the future.) Following Zil’s lead, I copied an extract from the Aware Machine report into the system to find out what Personality Insights said about me. The results: “You are inner-directed, skeptical and can be perceived as insensitive. You are imaginative you have a wild imagination. You are philosophical: you are open to and intrigued by new ideas and love to explore them. And you are independent. You are relatively unconcerned with taking pleasure in life: you prefer activities with a purpose greater than just personal enjoyment. You consider achieving success to guide a large part of what you do: you seek out opportunities to improve yourself and demonstrate that you are a capable person.” After I got over my initial reaction (which was to shout “No! That’s not me!”, especially about the “insensitive” part), my analyst instincts observed that my result contained a great deal of overlap with Zil’s profile. This indicates how broad the analysis is based on such a limited sample. The experience made me want to load a lot of additional data about myself into the system to see how personalized the results could get. And this is the main take-away for me about these systems – that they are trying to reach areas for which we have not generally applied automation (understanding the personality of our selves/our customers) using unstructured data. More experimentation and refinement will increase the value of both the results and our understanding of how to use them.

The Aware Machine in insurance

The Aware Machine in insurance
The topics of artificial intelligence, machine learning, deep learning, and cognitive computing have made their way into the popular business press. An insurance professional trying to stay informed of emerging technology may struggle to see the application of these technologies to their industry. A Celent new report, Machine Intelligence in Insurance: Designing the Aware Machine provides an explanation of this space and its opportunities in insurance. It describes a model named “The Aware Machine”,  identifies the characteristics of high-value problems best suited for such platforms, and suggests specific use cases to serve as proof-of-concept experiments. The use cases include:
  • Analysis of legal circulars for impact: Continuously identify which regulatory changes will have a material impact. Involves teaching a system insurance law and providing it with a continuous feed of changing regulations.
  • Medical case management: Optimize treatment plans to increase recovery, return to work rates
  • Identification of underwriting leakage: Analyze insurance contracts at the clause level and compare them with each other across lines of business to enforce consistency of intent. Continuously monitor new contracts to ensure that appropriate wording is used.
We invite readers of this blog to submit their own candidates in the comments section and check back for updates. Let’s crowdsource suggestions and get some proof of concept experiments underway!  

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

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

Robotics, bots and chocolate teapots

Robotics, bots and chocolate teapots
Increasingly in operational efficiency and automation circles we’re hearing about bots and robotics. As a software engineer in days past and a recovering enterprise architect I have given up biting my tongue and repeatedly note that, “we have seen it all before.” I’ve written screen scrapers that get code out of screens, written code to drive terminal applications and even hunted around user interfaces to find buttons to press. The early price comparison websites over a decade ago used these techniques to do the comparison. These techniques work for a while but are desperately fragile when someone changes the name of a button, or a screen or a screen flow. However, they can help. I recall a while ago a manager lamenting ‘the solution’ was about as useful as a chocolate teapot. A useful 10 minutes hunting for this video of a chocolate teapot holding boiling water for one whole pot of tea made the point for me. Sometimes all you need is one pot of tea.
Tea poured from a chocolate tea pot

Tea poured from a chocolate tea pot

So it’s not new, some bots may be fragile and with my “efficiency of IT spend” hat on (the one typically worn by enterprise architects) stitching automation together by having software do what people do is an awful solution – but as a pragmatist sometimes it’s good enough. Things have moved on. Rather than a physical machine running this with a ghost apparently operating mouse and keyboard we have virtual machines and monitoring of this is a lot better than it used to be. Further machine learning and artificial intelligence libraries are now getting robust enough to contribute meaningfully smart or learning bots into the mix that can do a bit more than rote button pressing and reading screens. In fact this is all reminiscent of the AI dream of mutli-agent systems and distributed artificial intelligence where autonomous agents collaborated on learning and problem solving tasks amongst other things. The replacement of teams of humans working on tasks with teams of bots directly aligns with this early vision. The way these systems are now stitched together owes much to the recent work on service oriented architecture, component orchestration and modern approaches to monitoring distributed Internet scale applications. For outsourcers it makes a great deal of sense. The legacy systems are controlled and unlikely to change, the benefits are quick and if these bots do break they can have a team looking after many bots across their estate and fix them swiftly. It may not be as elegant as SOA purists would like but it helps them automate and achieve their objectives. The language frustrates me though, albeit bots is better than chocolate teapots. I’ve heard bot referred to as a chunk of code to run, a machine learning model and a virtual machine running the code. I’ve even heard discussion comparing the number staff saved to the number of bots in play – I can well imagine operations leads in the future including bot efficiency in their KPIs. Personally, I’d rather we discussed them for what they are – virtual desktops, screen scraper components, regression models, decision trees, code, bits of SQL were appropriate, etc. rather than bucket them together but perhaps I’m too close to the technology. In short bots may not be a well-defined term but the collection it describes is another useful set of tools, that are becoming increasingly robust, to add to the architects toolkit.