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As cars get smarter, the volume and granularity of real-time data generated is on the rise. With connected cars set to grow further, newer opportunities for digital innovation are springing up across the auto insurance value chain, bolstered by growing ecosystems. While consumers gain in increased convenience, AI-enabled connected platforms deliver cost savings and multiple […]
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I’ve noted in the past that InsurTech is not dissimilar to the fable of six blind men describing an elephant solely on touch- each man ‘sees’ the elephant from the perspective of his narrow exposure to a very large creature. One sees a rope because he has grabbed the tail, another a tree because he’s grabbed a leg, another a snake due to the feel of the trunk, and so on.
InsurTech is that similar situation- many firms ‘touching’ the initiative from a narrow perspective. Not blind, surely, but not from a vantage of ‘seeing’ the entire concept. Of course it would be very daunting to try to grasp the industry from all angles, and very expensive too.
So,
there are the individual firms describing their unique parts- underwriting,
pricing, distribution, administration, claims, agencies, customer acquisition,
etc. And designing and/or applying technology- artificial intelligence
(AI), machine learning, IoT, algorithms, data science, actuarial science,
behavioral economics, game theory, and so on. Using technology and new
methods to help them see their part of the beast that is insurance innovation.
We get caught up in the thinking that InsurTech is a discrete concept– because each involved player has his unique approach to defining how change will be effected (and we can’t have multiple terms to describe what the movement is.) In the end each is convinced the efforts being made in their firm are defining the term. A recent article penned by Hans Winterhoff, KPMG Director, 3 Lessons European Insurers can Learn from Ping An, provides suggestions for legacy insurers based on successes Ping An has had in the China insurance market. The author makes three apt points but as with simply grabbing the Beast’s trunk and calling the animal a snake, is Ping An’s approach to insurance innovation the best InsurTech perspective for mature insurance markets?
Can the best innovative methods be applied to incumbent markets if a carrier’s staff are not engaged adequately in the evolution?
Legacy markets are populated with customers who are content with the Beast that is insurance, and in spite of some years of InsurTech efforts the market penetration of innovative companies remains low. Not that these customers don’t deserve the latest and best methods (surely most would trade the bureaucracy and cost of existing health care for the ease of service provided by a Ping An kiosk), but change must also come from within insurance company organizations. If one looks at Fortune magazine’s best large employers by employee survey and finds two of the insurance market’s biggest employers, Allstate and Geico, not in the top 500 firms, one must consider absent employee engagement then innovative change may be inhibited for those major companies and their customers.
Virtually
every week there is a significant conference of InsurTech enthusiasts,
thousands of attendees per month, all seemingly with an idea of what InsurTech
is, where it’s going, and how they will capture innovation lightning in the
bottle they have designed. There are some very smart persons who are seen
as champions of the effort, and these persons publish/travel/post and remind
the industry of where it has been and where it’s going. They are adept at
describing the beast in terms that most can understand, and in terms that help
the holder of the ropy tail to see that there also is a snaky trunk, and that
the two parts are of the same beast.
What
is cool about how the InsurTech movement is evolving is that a solid
recognition is being realized by most (not all) that InsurTech is comprised of
multiple, important and integral parts, and even if your firm is not working
with idea A, it can leverage the knowledge in developing idea B. We pick
at the theories others espouse, nay say, comment, maybe even doubt or
criticize, but at the same time all the knowledge is to the common goal-
improving a product for the existing and as yet unidentified insurance
customers.
And
without belaboring the theme, we can be reminded that the elephant is not
InsurTech; the elephant is insurance. InsurTech is the trappings with which the
elephant is enhanced. And the elephant is the contractual agreement that
comprises insurance, and the elephant’s handler must be the customer.
Let’s
all describe the beast well from our unique perspective, with the understanding
that in the end the elephant’s handler- the customer- must be why we are touching
the beast at all.
Patrick Kelahan is a CX, engineering & insurance professional, working with Insurers, Attorneys & Owners. He also serves the insurance and Fintech world as the ‘Insurance Elephant’.
I have no positions or commercial relationships with the companies or people mentioned. I am not receiving compensation for this post.
Subscribe by email to join the 25,000 other Fintech leaders who read our research daily to stay ahead of the curve. Check out our advisory services (how we pay for this free original research).
Artificial intelligence, machine learning, data analysis,
ecosystem insurance purchases, online claim handling, application-based insurance
policies, claim handling in seconds, and so on.
There’s even instant parametric travel cover that reimburses costs-
immediately- when one’s planned air flight is delayed. There are clever new risk assessment tools
that are derived from black box algorithms, but you know what? Those risk data are better than the industry
has ever had! Super insurance, InsurTech
heroes! But ask many insureds or claim
handlers, and they’ll tell you all about InsurTech’s weakness, the kryptonite
for insurance innovation’s superheroes (I don’t mean Insurance Nerd Tony Cañas)- those being- long-tailed or unique claims.
If insurance was easy you wouldn’t be reading this. That is simple; much of insurance is
not. Determining risk profiles for
thefts of bicycles in a metro area- easy.
Same for auto/motor collision frequency/severity, water leaks, loss of
use amounts, cost of chest x-rays, roof replacement costs, and burial costs in most
jurisdictions. Really great fodder for
clever adherents of InsurTech- high frequency, low cost cover and claims. Even more complex risks are becoming easier
to assess, underwrite and price due to the huge volume of available data
points, and the burgeoning volume of analysis tools. I just read today that a clever group of UK-based
InsurTech folks have found success providing comprehensive risk analysis
profiles to some large insurance companies- Cytora –
that continues to build its presence. A
firm that didn’t exist until 2014 now is seen as a market leader in risk data
analysis and whose products are helping firms who have been around for a lot
longer than 5 years (XL Catlin, QBE, and Starr Companies) Seemingly a perfect fit of innovation and
incumbency, leveraging data for efficient operations. InsurTech.
But ask those who work behind the scenes at the firms, ask
those who manage the claims, serve the customers, and address the many
claim-servicing challenges at the carriers- is it possible that a risk that is
analyzed and underwritten within a few minutes can be a five or more year
undertaking when a claim occurs? Yes, of
course it is. The lion’s share of
auto/motor claim severity is not found within the settlement of auto damage, it’s
the bodily injury/casualty part of the claim.
Direct auto damage assessment is the province of AI; personal injury
protection and liability decisions belong in most part to human interaction. Sure, the systems within which those actions
are taken can be made efficient, but the decisions and negotiations remain outside
of game theory and machine learning (at least for now). There have been (and continue to be)
systems utilized by auto carriers in an attempt to make uniform more complex
casualty portions of claims ( see for example Mitchell) but lingering ‘burnt fingers’
from class action suits in the 1980’s and 1990’s make these arms’ length tools trusted
but again, in need of verification.
Property insurance is not immune from the effects of
innovation expectations; there are plenty of tools that have come to the market
in the past few years- drones, risk data aggregators/scorers, and predictive
algorithms that help assess and price risk and recovery. That’s all good until the huge network of
repair participants become involved, and John and Mary Doe GC prices a rebuild
using their experienced and lump sum pricing tool that does not match the
carrier’s measure to the inch and 19% supporting events adapted component-based
pricing tool. At that intersection of ideas,
the customer is left as the primary and often frustrated arbiter of the claim
resolution. Prudent carriers then revert
to analog, human interaction resolution. Is it possible that a $100K water loss can
explode into a $500K plus mishandled asbestos abatement nightmare? Yes, it’s very possible. Will a homeowner’s policy customer in Kent be
disappointed because an emergency services provider that should be available
per a system list is not, and the homeowner is left to fend for himself? The
industry must consider these not as outlier cases, but as reminders that not
all can be predicted, not all data are being considered, and as intellectual
capital exits the insurance world not all claim staff will have the requisite
experience to ensure that which was predicted is what happens.
The best data point analysis cannot fully anticipate how
businesses operate, nor how unpredictable human actions can lead to claims that
have long tails and large expense. Consider
the recent tragedy in Paris with the fire at the Cathedral of Notre Dame. Certainly any carriers that may be involved
with contractor coverage have the same concerns as all with the terrible loss,
but they also must have concerns that not only are there potential liability coverage
limits at risk, but unlike cover limits, there will be legal expenses
associated with the claim investigation and defense that will most probably
make the cover limits small in comparison.
How can data analysis predict that exposure disparity, when every claim
case can be wildly unique?
It seems as underwriting and pricing are under continued
adaptation to AI and improved data analysis it is even more incumbent on companies
(and analysis ‘subcontractors’) to be cognizant of the effects of unique claims’
cycle times and ongoing costs. In
addition, carriers must continue to work with service providers to recognize
the need for uniform innovation, or at least an agreed common denominator tech
level.
The industry surely will continue to innovate and encourage those InsurTech superheroes who are flying high, analyzing, calculating and selling faster than a speeding bullet. New methods are critical to the long-term growth needed in the industry and the expectation that previously underserved markets will benefit from the efforts of InsurTech firms. The innovators cannot forget that there is situational kryptonite in the market that must be anticipated and planned for, including the continuing need for analog methods and analog skills.
Patrick Kelahan is a CX, engineering & insurance professional, working with Insurers, Attorneys & Owners. He also serves the insurance and Fintech world as the ‘Insurance Elephant’.
I have no positions or commercial relationships with the companies or people mentioned. I am not receiving compensation for this post.
Subscribe by email to join the 25,000 other Fintech leaders who read our research daily to stay ahead of the curve. Check out our advisory services (how we pay for this free original research).