- a practical understanding of how the very basic assumptions underlying traditional insurance products are changing and what impacts this will have
- firsthand information of how insurers can respond, gained through a series of experiential, structured exercises
- networking opportunities with peers which allow for comparisons with like organizations
October 12, 2015 by 1 Comment
What magic does an insurer need to keep up with all the change occurring in our industry? Every firm we talk with is aware of the many challenges currently faced….telematics, digital, social networking, predictive modeling, etc. And yet, more changes are on the way. Our short list includes the Internet of Things, peer-to-peer risk pooling, extended lifespans (maybe never-ending), and artificial/machine intelligence. What should an organization do now to respond and to prepare? What magic is necessary to make it all happen? Celent is pleased to announce our seminar designed to provide answers to how insurers move forward with innovation on practical terms. It will be in London, on 03 February, 2016. The venue, appropriately, will be The Magic Circle, Centre for the Magic Arts. Through a combination of presentation and hands-on workshops, the session will provide attendees with:
August 11, 2015 by 2 Comments
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.
May 27, 2015 by Leave a Comment
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.
May 8, 2015 by 1 Comment
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. 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.
July 23, 2014 by Leave a Comment
The announcement today (http://www-03.ibm.com/press/us/en/pressrelease/44431.wss ) of the use of IBM’s Watson platform by USAA demonstrates several of the current research themes at Celent. The move is an excellent case of innovation at the intersection of brand, risk management and technology. First and foremost, this is another example where USAA is delivering on its brand promise – to improve the lives of active duty and veteran military members and their families. The company will use Watson will to answer the questions of service military members who are transitioning to civilian life. An firm’s brand promise is at the foundation of the Celent customer experience model. It is the key characteristic that signals the evolution from a customer relationship management (CRM) to an experience approach. Second, this development is an illustration of an increased focus on prevention and risk mitigation. Traditionally, insurance has been a backward-looking, financial indemnification product (we pay you when there is a loss). This approach shows how insurers will innovate to apply technology to help insureds more effectively manage the risk in their lives (reduce or, avoid risk, altogether). This redirection will occur in commercial, as well as personal lines (see previous post on this blog: “My Risk Manager is an Avatar”). Finally, this is a business application of a computing approach that, up to now, has been closely held in the laboratory, in select pilot accounts, and in a custom, controlled environment (such as Jeopardy!). It will be fascinating to see what we humans, and the machine, Watson, learn in from this insurance debut.
May 22, 2014 by Leave a Comment
In the world of Commercial Insurance there exists the very curious role of Risk Manager. I mean curious in the sense that successful risk managers appear to have superpowers. They are charged with taking the actions necessary to avoid or reduce the consequence of risk across an entire enterprise. Their knowledge must extend deeply into a variety of subjects such as engineering, safety, the subtleties of the business of their employer, insurance (of course), physics, employee motivation and corporate politics / leadership. Their impact can be wide-ranging, from financial (eg., dollar savings from risk avoidance / mitigation) to personal (the priceless value of the avoidance of employee death or injury). Sadly, the tyranny of economics restricts the access that businesses have to continuous, high quality risk management. Full-time risk managers are prevalent in huge, complex, global companies. These firms often self-insure, or purchase loss sensitive accounts and the financial value of a risk management position (or department) is clear. The larger mid-market firms can afford to selectively purchase safety consultant services, their insurance broker might perform some of these tasks (especially at renewal), and their insurers may have loss control professionals working some of these accounts. However, for the majority of small businesses, risk management at the professional level is not affordable. Over the past year, I have toyed with different ideas about how to automate this function in order to bring the value of a risk manager to the small commercial business segment. My attempts were always unsatisfying (and one reason I have not blogged this idea before). However at The Front End of Innovation conference last week in Boston, a presentation by Dr. Rafael J. Grossmann (@ZGJR) crystallized the vision. I can now clearly see how existing technology can be combined to create a Risk Manager Avatar. Dr. Grossmann is a trauma surgeon who practices in Maine. In addition to the normal challenges of his profession, he is one of only four trauma surgeons servicing a very wide area. Although sparsely populated, the challenge of distance and time complicates the delivery of medical services. Dr. Grossmann presented his vision of a medical avatar, a combination of technologies which will perform 80% or more of the routine medical cases in a consistent, timely, and cost effective manner. Combining the technologies of mobile, voice recognition, virtual reality, artificial intelligence, machine learning and augmented reality forms a new silicon entity – a medical doctor avatar. He also introduced a company, sense.ly, that is now working to deliver similar services (video here: http://www.sense.ly/index.php/applications/). If such systems can deliver medical services, then why not risk management? For example, given permission, a system would monitor the purchases of a small company and identify when the historical pattern changes, eg., when the company begins to buy new types of materials. Using predictive algorithms, the pattern can be compared against others to evaluate if there is likelihood that the company is now performing new business operations. The avatar could then contact the small business owner to consult on options (endorse policy, retain risk, cease operations). It could also escalate the issue to an underwriter to evaluate more complex options. Someone will build a Risk Management Avatar. The question is, who will do it first?