Thursday, 13 October 2016

Where is the innovation in Individual life and annuity?

I had the pleasure of attending an amazing event last week in Las Vegas. The InsureTech Connect event drew over 1,500 people, from insurers to vendor to investors. Given the unprecedented size of an inaugural event, I was very impressed with how well the event worked. The sessions were good, but for me, the opportunity […]

from Insurance Blog

Friday, 7 October 2016

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.

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 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.


from Insurance Blog

Tuesday, 4 October 2016

“All that glitters is not gold”: Four concepts, four potential insurtech responses

As a few of us head to InsureTech Connect in Vegas this week to explore what the world has to offer in insurtech, I feel the need to keep my feet firmly on the ground and not to get too caught up in all of the glitz and glamour of both the location and the trendy start-up scene with its sea of beards.

“Bah, humbug!”, I hear you taunt in response.

Although I love the insurtech scene and welcome the fresh ideas, enthusiasm and willingness to be bold it brings (….and it’s way overdue and our industry needs a really good shake-up), I am mindful that history warns us that we should maintain an air of caution at this stage in any tech market’s development.  As the saying goes, “all that glitters is not gold” and there will undoubtedly be winners and losers (perhaps making Vegas all the more appropriate for the location).

Also, until wider market commentary around insurtech switches from the investment going in towards the value coming out of the start-ups (with real numbers on stealing market share, run-away customer demand, and incredible returns), we simply won’t know which way the market will move…if at all.

So, where will I be looking for the signs of a fresh gold seam and what might be an appropriate response for an insurer’s ‘insurtech strategy’?  From my perspective, there are four areas to focus upon:

  • Distribution. Undoubtedly, this is the area under the greatest threat of change through mobile, embedded micro-transactions and a change in demographics.  If you’re a traditional agent or direct writer, watch-out. If you’re an insurer on the other hand, your biggest challenge is likely to be the “speed of pivot” between current traditional and new channels that emerge. As a primary insurer, market scanning, operational agility and partnerships are likely to be critical elements of your insurtech strategy.
  • Automation, Analytics and AI. For decades, the industry has been running on robust (at least ‘robust’ for some of the time) transactional systems. For the bold, we’re now at a point where a substantial chunk of the operating model could arguably be replaced by not much more than an algorithm surrounded by a much smaller team of people to handle the customer touch-points. “Cloud native”, analytically driven micro-service architectures are the direction of travel. In markets exposed to aggregators, we have already seen some evidence of these characteristics being adopted by new entrants to the market.  As an incumbent, the challenge remains an age-old one of internal operational transformation and overcoming cultural inertia. Here, an insurtech strategy may be one of partnership in order to catalyse a change.
  • New propositions.  New risks, new data sources and, with them, new services.  Whether cyber-risk, the sharing economy or IoT enabled services, there is a lot of ground to cover here.  Out of these, new risks and use of new data sources appear to show the greatest promise in the near-term, and within the normal remit of an everyday insurer’s strategy. The IoT requires a different response. Although very very hot, it is a slower burn than other proposition related areas, primarily due to differing rates of sensor adoption, sensor installation economics, the absence of standards, the “what’s in it for me?” end-user proposition and the number of parties to engage, each with different agenda and requiring co-ordination. That said, it’s inevitable that it will become ever more pervasive across the industry. The bigger question, however, is what will the insurance industry’s role be in shaping it? Any insurer interested in the IoT needs to have effective partnership strategy with adjacent industries at its core.
  • New risk-bearing models. The word ‘disruption’ is overused in our industry, often without a solid understanding of what it truly means (for example, I’ve lost count of the number of times I’ve seen it used to describe a neat technology ‘widget’ that performs just one step in an end-to-end process).

Simply speaking, in order for an industry to be disrupted, one of two things needs to happen. Either new technology needs to open-up a significant jump in productivity (rendering the old ways of doing things as obsolete) or there emerges an effective substitution for the need being satisfied (with the consumer switching as a consequence).  Anything else could be argued as just normal competition and shpuld be expected.

As highlighted in my first point above, it’s evident that distribution is facing an increasingly turbulent time.  It is also clear that some technologies may enable a leap in productivity once implemented in the extreme (and not just for a single process step). However, for me, the court is still out for the substitution of the main risk-carrying entity itself.

However, one area that threatens this position is P2P (both at the front-end with insureds and the back-end with methods of alternative risk transfer). Even though it appeals to the more geeky and technical side of me, the barriers to adoption at scale just feel a little too high currently – whether market education related or regulatory (as, if executed poorly, a misselling scandal may result).

Furthermore, market efficiency is probably still better served through the current market structure than P2P owing to the ‘law of large numbers’, albeit implemented on better technology and with greater transparency. After all, there is a reason why mutual insurers have been merging or converting to public companies around the world.

That said, I’m willing to be proven wrong and will be looking eagerly for firms / evidence to demonstrate otherwise. In this area, although the brave will venture out regardless, an appropriate insurtech strategy for the more cautious feels like a classic ‘watch, learn, and be ready to pounce’ with a ‘Fast Second’ strategy.

For insurers reflecting on their engagement strategy for insurtech, the common thread across all but one of the areas above is the need for effective partnerships between insurers and start-ups. As Mike Fitzgerald observes in Insurer Start-up Partnerships: How Maximize the Value of Insurtech Investments:

“Both sides face challenges. Industry incumbents face the burden of their legacy systems, their aversion to failure, and a habit of extended decision cycles. Newcomers lack the capital to underwrite risk, do not understand the regulatory environment, and cannot scale easily." 

There is value (and hopefully gold) to be gained from both sides in engagement.

Finally, while interest in insurtech is high, any insurer ought to be maintaining a watch on activity, providing that a strong bias towards value being delivered is taken (as opposed to money going in).

So, in summary, that’s what I’ll be focused on over the next few days – the hunt for value around these four themes.

from Insurance Blog

Tuesday, 27 September 2016

Life Insurance Automated Underwriting – A 25 Year Journey

Automated underwriting has come a long way in the last 25 years. It may be surprising that there was automated underwriting 25 years ago. At that time, it was called ‘expert’ underwriting. The idea was right, but the timing was wrong. The underwriting engines were black box algorithms; there was no user interface; data was fed from a file to the system; programming was required to write rules; and specialized hardware was necessary to run the systems. Not surprisingly, this attempt at automating underwriting was dead on arrival.

The next major iteration occurred about ten years later. Automated underwriting systems included a user interface; rules were exposed (some programming was still required to change the rules); data interfaces were introduced to collect evidence from labs and the medical inquiry board; underwriting decisions could be overridden by the human underwriter; and workflow was provided. Some insurers chose to take a chance on this new technology, but it was not widely adopted. There were two strikes against it: cost and trust. The systems were expensive to purchase, and the time and costs involved in integrating and tailoring the systems to a specific company’s underwriting practice could not be outweighed by the benefits. The lack of benefits was partially because the underwriters did not trust the results. Many times this caused double work for the underwriters. The underwriters reviewed the automated underwriting results and then evaluated the case using manual procedures to ensure the automated risk class matched the manual results.

Moving ahead fifteen years to today, changes in the underwriting environment place greater demands on staff and management. Staff members are working from home, and contractors are floating in and out of the landscape, all while reinsurers are knocking on the insurer’s door. There are now state-of-the-art new business and underwriting (NBUW) systems that address the challenges associated with the new demands. The solutions do not just assess the risk but provide workflow, audit, and analytics capabilities that aid in the management process. Rules can be added and modified by the business users; evidence is provided as data so that the rules engine can evaluate the results and provide the exceptions for human review. Subjective manual random audits of hundreds of cases evolve into objective, data-driven perspectives from thousands of cases. Analytics provide insights on specific conditions and impairments over the spectrum of underwritten cases to provide a portfolio view of risk management. Underwriting inconsistencies become easy to find and specific training can be provided to improve quality.

.In our report, Underwriting Investments that Pay Off, Karen Monks and I found that the differences between insurers who are minimally automated and those that are moderately to highly automated are substantial.  For minimally automated insurers, the not in good order (NIGO) rates are four times higher, the cycle times are 30% longer, and the case manager to underwriter ratio is almost double compared to the metrics for the moderately to highly automated insurers. This outcome may not reflect your specific circumstances, but it is worth preparing a business case to understand the benefits. With the advances in the systems and the advantages provided for new business acquistion, there are few justifications for any company not to seek greater automation in their underwriting.  

To learn more about the adoption of current NBUW systems and the functionality offered in them, please read our new report, What’s Hot and What’s Not, Deal and Functionality Trends and Projections in the Life NBUW Market or join our webinar on this topic on Thursday, September 29.  You can sign up here.



from Insurance Blog

Wednesday, 21 September 2016

The Muslin is off the Lemon — Lemonade Launches

Today’s announcement by Lemonade provides an example of what actual disruption in insurance looks like. Disruption — the term is overused in the hype around innovation. In Celent’s research on innovation in insurance, we see that what is often tagged as disruptive is actually an improvement, not a displacement, of the existing business model.

The information released describes how Lemonade seeks to replace traditional insurance. Yes, they have built a digital insurance platform. Beyond that significant feat, they seek to replace the profit-seeking motive of their company with one based on charitable giving, acting as a Certified B-Corp (more info on B-Corps). They are also using the charitable motive as the guide to establish their risk sharing pools, thus creating the peer-to-peer dimension. Unlike other P2P efforts, Lemonade goes beyond broking the transaction and assumes the risk (reinsured by XL Catlin, Berkshire Hathaway and Lloyd’s of London, among others).

However, like other P2P models, such as Friendsurance, Lemonade faces a real challenge regarding customer education. The Celent report Friendsurance: Challenging the Business Model of a Social Insurance Startup — A Case Study details the journey of the German broker along a significant learning curve regarding just how much effort was required to teach consumers a new way to buy an old product.

The next few weeks will surface answers to they second-level questions about this new initiative such as:

  • How/if their technical insurance products differ from standard home,renters, condo and co-op contracts;
  • What happens to members of a risk sharing pool when the losses exceed funding;
  • Will the bedrock assumption, that a commitment to charity will overcome self interest and result in expected levels of fraud reduction?

It is refreshing to see some disruption delivered in the midst of all the smoke around innovation. Celent toasts Lemonade and welcomes this challenge to business as usual!


from Insurance Blog

Vrooom: New Federal Guidance Should Accelerate Development of Autonomous Cars

September 20 was a good day for the development of autonomous cars. The Feds, as embodied by the Department of Transportation and the National Highway Traffic Safety Administration (NHTSA), have issued guidance and principles for the development of autonomous cars.

There are two key takeaways:

  1. By issuing guidance, rather than regulation, the Feds are trying to facilitate, but not control, the technological developments that will lead to street-ready autonomous cars
  2. The guidance makes some common sense delineations between what the federal government should do and what states should do
  • The feds want one national standard for how manufacturers conduct driverless car R&D–following a 15 part safety assessment protocol (covering data recording, system safety, human:machine interface, etc.).
  • The feds want the states to focus on vehicle licensing and registration, traffic laws, and motor vehicle insurance and liability

If actually followed (are you listening California?) the political and regulatory environment should speed the day when a consumer can walk into a dealer, and be driven out by a shiny, brand new autonomous car.

That day will be good for car buyers, for manufacturers, and for society as a whole.

However, for insurers that day will also hasten the decline of auto insurance—per the recent Celent report The End of Auto Insurance: A Scenario or a Prediction.

from Insurance Blog

Tuesday, 20 September 2016

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.

from Insurance Blog