The insurance earth is flat. Digital conferences have helped prove that theory.

Being a last minute addition to two Digital Insurance Innovation Conference Africa 2020 panels has proven the Flat Earthers’ hypothesis- the earth is not a spherical barrier to virtual navigation, and taking the concept one step further, insurance innovation has loosed its bindings from its earthly coil and barriers raised by regulatory frontiers.   Patrick […]

The post The insurance earth is flat. Digital conferences have helped prove that theory. appeared first on Daily Fintech.

XBRL: scrapping quarterlies, explaining AI and low latency reporting

Here is our pick of the 3 most important XBRL news stories this week. 1 FDIC considers scrapping quarterly bank reports The Federal Deposit Insurance Corp. is moving to boost the way it monitors for risks at thousands of U.S. banks, potentially scrapping quarterly reports that have been a fixture of oversight for more than […]

The post XBRL: scrapping quarterlies, explaining AI and low latency reporting appeared first on Daily Fintech.

AI- hot water for insurance incumbents, or a relaxing spa?

The parable of the frog in the boiling water is well known- you know, if you put a frog into boiling water it will immediately jump out, but if you put the frog into tepid water and gradually increase the temperature of the water it will slowly boil to death.  It’s not true but it […]

The post AI- hot water for insurance incumbents, or a relaxing spa? appeared first on Daily Fintech.

The revised pessimistic projection for Digital wealth AUM does not make sense

Efi Pylarinou is the founder of Efi Pylarinou Advisory and a Fintech/Blockchain influencer – No.3 influencer in the finance sector by Refinitiv Global Social Media 2019.

Consulting practices call for 5yr predictions on all sorts of topics. The so-called Robo Advisor subsector in investing has not escaped these studies.

Back in 2016, was when Vanguard was making its first leapfrogging attempts in a space that Betterment and Wealthfront had brought to market. Personal Capital was also shaping up the hybrid version of `digital investing`. Deloitte, CB insights, Aite Group and others were predicting assets under management by 2020 (which at the time, seemed far away for all of us).

Predictions ranged between $ 2.2 trillion and $ 3.7 trillion in assets to be managed by Robo-Advisory services by 2020 and $16 trillion by 2025.

Permit me to take the mean of the range predicted for 2020 (trillions of USD are being transferred from the government to the `people` anyway as we speak) and round it up to $3 trillion for 2020.

2020 – Well we are in the second quarter of 2020 and we are just reaching $1 trillion. Urs Bolt, highlighted the latest report by BuyShares that says we are heading to $987billion. So, we are at 1/3 the 2016 prediction even though the S&P500 is up 30+% and the Dow is up 28+%, since Jan 2017.

 

What is more remarkable is that the current 5yr outlook compiled by BuyShares and based on Statistica data, predicts that in less than 5yrs the AUM will grow x2.4 times, reaching $2.4trillion (Statistica).

Screen Shot 2020-06-15 at 11.13.33Screen Shot 2020-06-15 at 11.13.47

At first site, it may seem to you an optimist outlook. However, it is actually a heavily discounted view from that set out back in 2016 when the sector started attracting more VC investments and incumbents. The first predictions were from 0-$3trillion in less than 5yrs and then from $3trillion-$16 trillion in the second 5yr phase (x5+ times).

And now this study is saying,

let’s cut the 5+ times growth rate in AUM to more than half. And let’s cut the AUM managed over the next 5yrs by 85% (we had said $16trillion and now we say $2.4trillion).

Let’s step back and look into the mirror as if it is 2025. Of course, digital onboarding and automated asset allocation offered currently via ETFs will be 100% an option everywhere and probably free.

The more interesting and meaningful question is about the evolution of the ETF market itself which has been the bread and butter of all the digital investing offerings (lumped under the `robo-advisor` umbrella be it with or without human advisory services); and whether artificial intelligence will actually transform digital investing.

1⃣ Will `robo-advisors` continue to build their businesses mainly using ETFs? Their low-cost core value-add has been interchangeable seen as a win for passive investing and mainly via indices.

2⃣ Will the 12% of the $4.7trillion ETF market (based on 2018 year-end data, see here) grow and to what extent?

3⃣ Will active ETFs grow given the current macro environment? ANTs are just emerging and are a step back from the transparency trend and the zero-commission trend. ANTs are active non-transparent and on average their expense ratio is 70bps. Their position reporting is much better than mutual funds (quarterly). Their cost-adjusted and risk-adjusted-performance will have to be seen going forward. They are currently only 2% of the ETF space (see here).

4⃣  Will artificial intelligence finally take over the asset allocation and the decision of switching between direct indexing or stock picking or momentum.

A few facts to consider:

  1. The ETF space grew sustainably in 2019. Statistica reports $6.18trillion by year end of 2019. That is a 30% increase. Of course, by the end of Q1 2020, the ETF global industry experienced a c. 16% drop ($5.4trillion) which was 100% due to the drop-in asset values. ETFs actually experienced in Q1 net inflows of c. $120billion. These were inflows during the major March selloff. Source
  2. An update on my calculations of the assets under management by digital wealth services points to a c.30% increase (by 2019 year-end), which matches the ETF increase. Source
  3. The actual role of artificial intelligence in all the Digital wealth offerings, is still minimal. Even the large incumbents with sizable digital wealth AUM, like Merill Edge or Vanguard, are still in the initial phase of digital transformation in wealth management. Vanguard actually has done very little on the needed digital integration front. Merill Lynch is probably ahead with its new CEW – Client Engagement Workstation – that integrates market data, client information, account servicing tools and some narrow artificial intelligence tools (chatbots).

For 2025, we should be making predictions of the extent that Artificial intelligence will be making better decisions for my asset allocation than I do, or my private banker, or my financial advisor, or my digital wealth service provider.

What has gone wrong in Fintech that pushed the original projections of $16trillion AUM in 2025, to $2.4trillion?

Where are the trillions of currencies that are being transferred, going to end up?

Isn’t the digital transformation of the mutual fund industry what will happen over the next 5yrs? Whether it is through DLT as an infrastructure of the mutual fund administration or by the tokenization of fund structures or the disintermediation of the European banks who dominate mutual fund distribution or all of the above? And wont all this lead to an exponential growth of the `Digital wealth` AUM?

New readers can see 3 free articles before getting the Daily Fintech paywall. After that you will need to become a member for just US$143 a year (= $0.39 per day) and get all our fresh content and our archives and participate in our forum.

The post The revised pessimistic projection for Digital wealth AUM does not make sense appeared first on Daily Fintech.

50 Diverse Takeaways from Davos WEF2020

50th

I am transparently stealing Ben Pring`s format[i] to honor the 50th anniversary of the World Economic Forum’s (WEF) annual meeting in Davos, with my own 50 diverse takeaways. Some are my own big picture opinions from spending two intense full days in Davos and participating in two side events. Others are takeaways from the diverse speakers that I had the privilege to listen to. And some are my Tech innovation picks again from the events I participated.

  1. Great Greta Thunberg joined the status quo and is now confronted with all the difficulties that grown-up celebrities face. I am not supportive of this at all. I suggest `Leave the kid alone`, she has done enough already.
  2. The “Davos Manifesto” is clear and in alignment with Great Greta`s message. Businesses will continue to be the way we create wealth (of all kinds) but now all adults can role up their sleeves and look at ways to maximize Stakeholder value.
  3. Let’s pass on the torch that Great Greta held, to adult activists.

 

  1. The WEF was originally known as the European Management Forum and only 16 years later when it expanded its scope, was it renamed to WEF. Watch the WEF roadmap here.
    Efi Pylarinou is the founder of Efi Pylarinou Advisory and a Fintech/Blockchain influencer – No.3 influencer in the finance sector by Refinitiv Global Social Media 2019. You get 3 free articles on Daily Fintech. Get all our fresh content and our archives and participate in our forum, by becoming a member for just US$143 a year
  2. Even Davos was supportive of Greta. There was less snow and it was too warm. However, that did not reduce the number of men & women wearing light color fur coats and (women) bright color heavy makeup and hanging out on the sidewalks of the Promenade.
  3. Pop-up stores, as “corporate showrooms” on the Promenade are growing.
  4. Last year there were two Teslas showcased outside the Morozani hotel. Honestly, I expected to hop on a driverless car this year, but I was disappointed.
  5. Female dominated panels were all the rage. We were even got offered a special `Women in Fintech`trophy at TechParkDavos (amazing event).
  6. Background and geographic diversity in panels was also rising.
  7. For the first time, the balance of the agendas of the ever-growing Davos side events was greatly tilted towards Sustainability rather than Blockchain.
  8. During the two days in Davos, I did not hear 4IR once. The dominating themes were around ethical regulation of the technologies.
  9. WEF Davos is becoming more about social issues rather than businesses, which gives us hope for explicit and intentional innovation on the Societal front.
  10. Surprise on the Promenade: A “cannabis-tech” expo for the first time. No comment.
  11. Ben Pring says that `Wellness” advisors from Beverly Hills have started showing up in Davos. I did not see them. However, the Blockbase Davos space was really well attended. Linking spirituality, arts and technology. Worth visiting.
  12. WEF Davos was echoing with links between Ecology, Economy and Consciousness.
  13. Goldman Sachs`s announcement to no longer take any company public unless there is at least one diverse board member, was very in vogue with the Davos narrative. Maybe they will be the ones taking Ripple to IPO (an unexpected hint during Davos) as their board is really diverse.

Artificial Intelligence

  1. AI will have huge applications in the real economy. We humans will be training lots of micro-robots to perform tasks (micro-robot training).
  2. AI will have huge applications in optimizing transportation.
  3. Only 20% of large corporations have made a difference by using AI.
  4. 54 million people are wasting their time with jobs that AI data management can do better.
  5. “AI is going to become more and more accessible to everyone. It is not going to be only for the privileged neither controlled by the big companies.” says Jürgen Schmidhuber
  6. `X+AI will be the norm` says Ben Pring
  7. `Self-improving AI will change everything`says Jurgen Schmidhuber, founder of Nnaisense.
  8. #AI is like babies – they need lots of #Data, lots of #Information. They need their parents teaching them. AI needs us, humans” says Jurgen Schmidhuber
  9. Did we ask for this?: AI & IOT can help moms monitor whether their kids brush correctly their teeth
  10. Did we ask for this?: AI & IOT can help men & women apply moisturizer exactly on the areas that need it.

DATA:

  1. The old adage `Location, location, location` is now transformed into `Availability, availability, availability` which means the power is in the accessibility and data.
  2. $4 trillion USD is spent by corporates every year to prepare data to be used by AI algos.
  3. Global Data Excellence is global leader in Data excellence management that maximizes business value with a clear focus on the social value mission.
  4. Trigyan offers an innovative data platform that is domain neutral to link data at scale and real-time – Glide (Graphically Linked Integrated)

Platforms are the only Sustainable business model (a pick of the ones I met in Davos during the WEF)

  1. Capital Markets: LoanBoox is a Swiss debt capital market platform digitizing capital markets for the municipality sector and eyeing much more.
  2. Banking: Pelerin is a blockchain-powered core banking operating system. No fractional reserve banking, rather a marketplace approach to digital assets.
  3. Digital ecosystem: The 1 billion retired people are a virtual continent that needs to be serviced accordingly. Dmitry Kaminskiy is the coauthor of the upcoming book Longevity Industry and covered tech in this the longevity space.
  4. Digital ecosystem: YesWeTrust is a Swiss-based unique ecosystem growing a community around health, sustainable investing, and a marketplace for sustainable consumption.
  5. Digital ecosystem: Beyond Animal is another Swiss-based unique ecosystem focused on the sustainable economy. A networking platform, a crowdfunding platform for sustainability business, and a AI-powered supply chain assistant.
  6. Digital ecosystem: Graypes is another Swiss AI-powered ecosystem that evaluates business ideas. Funds them and offers a network to grow them.
  7. Sovereignty: Liberland is a micronation powered by blockchain and other technologies that setup a rep office in Zug after the WEF.
  8. Ria Persad spoke at TechParkDavos: She is the founder of Statweather, a 10yr old award winning company providing state of the art weather prediction systems and risk mgt. She bootstrapped the company with stay home moms working part-time!
  9. NASA and Diversity: The NASA Artemis program, will send the first woman and next man on the Moon by 2024, using innovative technologies to explore more of the lunar surface than ever before. Cindy Chen spoke at Diversity in Blockchain about Artemis and the new female spacesuits.
  10. Data scientists: NASA Datanauts is a community for data newcomers, introducing and advancing data science skills, and creating a vibrant data problem-solving community.
  11. Edge technologies: Marco Tempest showed us a VR live application that combines science and illusion. A kind of camera that as you speak on stage, autonomously projects you in a VR enhanced way (a real-time transformation to a magical virtual world).
  12. The transformation via AI is leading the world from Automation to Autonomous; said Nicolai Waldstrom, CEO & Founder, VC BootstrapLabs
  13. Evan Luthra, is a truly native entrepreneur and angel investor, who has founded the iyoko.io ecosystem supporting people and innovative ventures.

Davos Events I attended or were on my list but never made it:

  1. Follow SwissCognitive – The Global AI Hub, the host of the TechParkDavos, and Yusuf Berkan Altun, the organiser of TechPark Conference Davos 2020.
  2. Follow Diversity in Blockchain and their sponsor BloomBloc who is focused on the sustainable supply chain for agriculture.
  3. Follow the World Innovation Economics events; a platform for sharing ideas, discussing and exploring ways to resolve real-world challenges using Innovations.
  4. The Digital Economist hosted the book launch of Bridgital Nation : Solving Technology’s People Problem co-authored by Natarajan Chandrasekaran and Roopa Purushothaman (Tata). A dream application of AI in India.
  5. The Digital Economist roundtable with MIT Professor Alex ‘Sandy’ Pentlandfocused on co-creating solutions to driving technological convergence into the new digital economy. Using the SDG framework to improve the state of the world through social entrepreneurship.
  6. The Prosperity Collaborative presented insights around building an efficient and fair taxation system by harnessing innovative technologies, hosted by GBBC. Alex `Sandy’ Pentland and John Werner and Tomicah Tillemann were involved.
  7. The Planetary Supernodes annual gathering (an invitation-only event) took place at Blockbase Davos. The mission is to energize and activate thriving projects of planetary impact that engender a sustainable future.

[i] Ben Pring leads Cognizant’s Center for the Future of Work and is a co-author of the books What To Do When Machines Do Everything and Code Halos: How the Digital Lives of People, Things, and Organizations Are Changing the Rules of Business. – FIFTY THOUGHTS FROM DAVOS 50

The post 50 Diverse Takeaways from Davos WEF2020 appeared first on Daily Fintech.

Is it Artificially Intelligent or Naturally Stupid? Let’s ask Apple

Earlier this week, there was an allegation that the credit scoring engine behind Apple card was biased. It emerged from the twitter account of David Heinemeier Hansson (@dhh). He raised the issue that his wife had been given a credit limit 20 times lower than his. David has about 360K followers on twitter, and the […]

The post Is it Artificially Intelligent or Naturally Stupid? Let’s ask Apple appeared first on Daily Fintech.

Flood risk AI is now a reliable tool- is there a desire to put it to work?

Flood insurance has been a wallflower at the coverage dance- an eager participant but not able to find a suitable partner.  Innovation efforts have found suitable risk prediction partners for carriers- FloodMapp, Hazard Hub, and Previsco among others- but is the flood insurance market ready? Politics, inertia, customer preferences and regulation might keep the music […]

The post Flood risk AI is now a reliable tool- is there a desire to put it to work? appeared first on Daily Fintech.

FinServ in the age of AI – Can the FCA keep the machines under check?

Zz0yZGVlNWFjNzUyNjgwYjFmMDc2NzMyNWM0MGQyZTYzMA==

Image Source

I landed in the UK about 14 years ago. I remember my initial months in the UK, when I struggled to get a credit card. This was because, the previous tenant in my address had unpaid loans. As a result, credit agencies had somehow linked my address to credit defaults.

It took me sometime to understand why my requests for a post paid mobile, a decent bank account and a credit card were all rejected. It took me longer to turn around my credit score and build a decent credit file.

I wrote a letter to Barclays every month, explaining the situation until one fine day they rang my desk phone at work to tell me that my credit card had been approved. It was ironical because, I was a Barclays employee at that time. I started on the lowest rungs of the credit ladder for no fault of mine. Times (should) have changed.

Artificial Intelligence, Machine Learning, Deep Learning, Neural Networks and a whole suite of methodologies to make clever use of customer data have been on the rise. Many of these techniques have been around for several decades. However, only in recent times have they become more mainstream.

The social media boom has created data at an unforeseen scale and pace that the algorithms have been able to identify patterns and get better at prediction. Without the vast amount of data we create on a daily basis, machines lack the intelligence to serve us. However, machines rely on high quality data to produce accurate results. As they say, Garbage in Garbage out.

Several Fintechs these days are exploring ways to use AI to provide more contextual, relevant and quick services to consumers. Gone are the days when AI was considered emerging/deep tech. A strong data intelligence capability is nowadays a default feature of every company that pitches to VCs.

As AI investments in Fintech hit record highs, it’s time the regulators started thinking about the on-the-ground challenges of using AI for financial services. The UK’s FCA have partnered with Alan Turing Institute to study explainability and transparency while using AI.

Three key scenarios come up, when I think about what could go wrong in the marriage of Humans and Machines in financial services.

  • First, when a customer wants a service from a Bank (say a loan), and a complex AI algorithm comes back with a “NO”, what happens?
    • Will the bank need to explain to the customer why their loan application was not approved?
    • Will the customer services person understand the algorithm enough to explain the rationale for the decision to the customer?
    • What should banks do to train their staff to work with machines?
    • If a machine’s decision in a critical scenario needs to be challenged, what is the exception process that the staff needs to use?
    • How will such exception process be reported to the regulators to avoid malpractice from banks’ staff?
  • Second, as AI depends massively on data, what happens if the data that is used to train the machines is bad. By bad, I mean biased. Data used to train machines should not only be accurate, but also representative of real data. If a machine that is trained by bad data makes wrong decisions, who will be held accountable?
  • Third, Checks and controls need to be in place to ensure that regulators understand a complex algorithm used by banks. This understanding is absolutely essential to ensure technology doesn’t create systemic risks.

From a consumer’s perspective, the explainability of an algorithm deciding their credit worthiness is critical. For example, some banks are looking at simplifying the AI models used to make lending decisions. This would certainly help bank staff understand and help consumers appreciate decisions made by machines.

There are banks who are also looking at reverse engineering the explainability when the AI algorithm is complex.  The FCA and the Bank of England have tried this approach too. A complex model using several decision trees to identify high risk mortgages had to be explained. The solution was to create an explainability algorithm to present the decisions of the black box machine.

The pace at which startups are creating new solutions makes it harder for service providers. In recent times I have come across two firms who help banks with credit decisions. The first firm collected 1000s of data points about the consumer requesting for a loan.

One of the points was the fonts installed on the borrowers laptop. If the fonts were used in gambling websites, the credit worthiness of the borrower took a hit. As the font installed indicated gambling habits, the user demonstrated habits that could lead to poor money management.

The second firm had a chatbot that had a conversation with the borrower and using psychometric analysis came up with a score. The score would indicate the “intention to repay” of the customer. This could be a big opportunity for banks to use in emerging markets.

Despite the opportunities at hand, algorithms of both these firms are black boxes. May be it’s time regulators ruled that technology making critical financial decisions need to follow some rules of simplicity or transparency. From the business of creating complex financial products, banks could now be creating complex machines that make unexplainable decisions. Can we keep the machines under check?


Arunkumar Krishnakumar is a Venture Capital investor at Green Shores Capital focusing on Inclusion and a podcast host.

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 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).


 

 

 

The post FinServ in the age of AI – Can the FCA keep the machines under check? appeared first on Daily Fintech.

Numerai a small cap AI Blockchain gem

Blockchain and AI are the most trending technologies. Blockchain for Finance and AI for Finance ventures are also increasing. The combination is hoped to fuel the autonomous financial infrastructure that will host all kinds of intelligent applications in capital and financial markets.

LiveTiles-Blockchain-Infographic-E

LiveTiles brought to my attention 20 AI Blockchain projects with a great infographic. As I have profiled a few of them in 2017 at the protocol layer and the data-finance verticals, I decided to catchup with Numerai. They had grabbed my attention 2 years ago in this primer I wrote: The Big Hairy Audacious Goal of Numerai: network effects in Quant trading

Screen Shot 2019-06-02 at 16.59.44Numerai is creating a meta-model from all the Machine Learning (ML) algorithms developed by “the crowd” with cryptographic data. Numerai aims to offer a platform that generates alpha in a novel way. It wants to structure a rewarding mechanism for its traders that not only eliminates the typical competitive and adversarial behavior between them but actually, penalizes them.                              Efi Pylarinou

Numerai was and is a bleeding edge venture. It remains the only hedge fund built on blockchain and using ML and data science in a novel way. The novelty lies in changing the incentive and compensation structure of the fund manager.

Numerai launched no ICO. The NMR token was awarded to the thousands of data scientists for creating successful machine-learning based predictive models.  Once the data scientists are confident of the predictive ability of their model, they can stake their NMR and earn additional NMR if they are correct.

Numerai involves a staking mechanism.

In March, Numerai reported that $10million had been rewarded up to date. NMR tokens were distributed via airdrops initially. At launch on 21st February 2017, 1 million Numeraire tokens (NMR) were distributed to 12,000 anonymous scientists.  Thereafter, NMR  tokens were awarded as rewards to users of its platform. Bear in mind, that if a participant stakes NMR and their model doesn’t perform, the staked tokens are burnt.

According to Numerai, the NMR token is one of the most used ERC20 tokens. By end of 2018 reporting 25,000 stakes of NMR.

Numerai II.pngSource

Almost 200,000 models submitted by data scientists around the world for a competition to crowdsourced the best prediction models.

Screen Shot 2019-06-02 at 18.52.49Source from Chris Burniske`s talk at Fluidity Summit in NYC.

Numerai in March raised $11mil from investors led by Paradigm and Placeholder VCs. Numerai is a very rare case because this fundraising is not for equity but for NMR tokens.

Numerai token is a utility token and investors just bought $11million of NMR tokens.

The funds raised will primarily be used to drive the development of Erasure, a decentralized predictions marketplace that Numerai launched.

What does this mean in plain worlds?

Numerai was not a protocol but rather an application  – a hedge fund. Erasure will transform it into a protocol. This has several significant implications.

  • NMR becomes a token on the protocol and can be used to build all sorts of applications on top of Erasure.
  • Numerai becomes decentralized. The NMR smart contract will no longer be controlled or upgraded by Numerai but by NMR token holders. So, NMR becomes a governance token.
  • Numerai will have no authority on the supply of NMR tokens.

A protocol is born out of the app Numerai – its name is Erasure. Erasure is much broader than a hedge fund, as all sorts of prediction and data markets can be built on the protocol. The vision is to always to be a token that is actually used. Which brings to the spotlight the lack of transparency around data measuring use of protocol and Dapp tokens.

 Footnote: Numerai at launch was backed by Fred Ehrsam, Joey Krug, Juan Benet, Olaf Carlson-Wee and Union Square Ventures.

Efi Pylarinou is the founder of Efi Pylarinou Advisory and a Fintech/Blockchain influencer.

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 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).

Not so fast, InsurTech- long-tailed and unique claims are the Kryptonite to your innovation super power

Nothing to fear, InsurTech Man! It’s just a busy claim!

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. 

image source

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).