Alternative Data gained more trust and ML algorithms got good training

data

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.

The conversation around the interaction between data, algorithms and humans is evolving.

Once again, A crisis like no other!

The COVID19 induced crisis of course, provides us with more data and experience on the topic of data, algorithms, investing etc.

Once again, A crisis like no other, is actually the title of an AQR article at the end of March. The 20+yrs old quant king has suffered after a very difficult 2018 (the so-called Red October) which led to large outflows and over 1000 layoffs-  Investors pull billions from quant king AQR as performance slumps – Financial News.

There has never been a better time to bring up the conversation on whether Data (fundamental, conventional and alternative) made a difference during this crisis. Did algorithms help and in what way? And what about the interaction of humans with Data and algorithms?

Fundamental macro data is essential, and this will never change. What can and should change is that it time lag and that not all data sources are trustworthy. We need to build a real-time information system aggregating trustworthy data, that starts with fundamental data and extends to the currently coined `alternative data`. Eventually, we will get rid of the alternative data term, all together.

Lykke recently launched, the Open Initiative project which is a competition with a CHF50k funding award. One of the four different thematics of the competition is focused on building a Real-time information system. Lykke proposes a Wikipedia like system (in certain aspects) with revenue streams built on Blockchain and accessed via APIs. This vision merits a separate article.

In May, I reached out to experienced Quants in different positions and discussed with them about data, machines and humans through the crisis and in the new normal. The demand for alternative data, as expected, spiked during this crisis because everybody needed to access the situation in real-time and needed real-time measures. Investors, traders, portfolio managers, pensions, who were already using some kind of alternative data, needed more and relied heavily on high-quality real-time data. The reliance on such alternative data and on actionable techniques to access exposures and make intelligent predictions, skyrocketed. New entrants in the alternative data space, flocked as they needed to manage risks and exposures.

Those in the field managing risks, uniformly confirmed the increased need to manage thematic and narrative risks. In plain words, humans needed to understand their exposure to airlines or to China during the crisis and in the new normal. They needed to ask the data and the machines what the impact of COVID19 would be in real estate. And on and on….

Humans continue to be in charge of narrating the topics of interest or at stake. The machines need to be able to offer actionable insights and forecasts.

There is an increasing need for real-time and continuous re-assessment of this kind of complexity through lots of high-quality real-time data of all sorts. Machine learning and adaptive trading algorithms that reflect and retrain gave confidence to humans in certain asset classes (e.g. commodities) during this highly uncertain period.

Building trust between humans and machines has always been essential and will continue to be. The recent crisis was a painful but valuable experience that built more trust in humans as to what machines can currently do and with what inputs.

There has been an improvement in actionable techniques that allow humans to extract signals towards generating alpha by combining high-quality real-time data and adaptive algorithms. There has been a better and larger offering from providers, of data, insights, algorithms.

To write this article I looked into and spoke with people in the following companies.

Ravenpack is a leading data analytics platform headquartered in Spain. I was using their free Coronavirus dashboard during the lockdown which had lots of alternative indicators (panic, media hype, fake news etc). You can check out their research around sentiment impact and sentiment investment strategies – here. Ravenpack highlights that negative sentiment has more predictive power for asset prices than positive sentiment.

S&P Global market intelligence is a leading player in alternative data and actionable techniques. They measure people’s flows and money flows and other data ahead of delayed economic data reporting. During the crisis, their free Quantamental Research offered several insights. For example, reports on how COVID19, the weather temperature, and real estate prices (REITs) were connected. Two years ago, S&P Global acquired Kensho. One of my first articles on DailyFintech in April 2015, profiled Kensho: Warren is like Watson and Siri for analysts, investors and traders.

The Book of Alternative Data, by Wiley coauthored by Alexander Denev and Saeed Amen will be available in July. It is a book covering ways of leveraging alternative information sources in the context of investing and risk management.

Sentifi is a Swiss company that was focused on sentiment data and has now built an enterprise analytics platform using AI and sentiment data.

RAM is a macro hedge fund in Geneva that emphasizes the complexity and alpha generation potential of combining structured and unstructured data (see article here).

Macrosynergy is a UK macro hedge that is currently acting as a fiduciary quant house for long term institutional investors. They are combining fundamental data and insights with algorithmic trading.

Predictive Layer is a Swiss company focused on predictive Analytics and forecasting applications. Their algorithms adapted to this crisis and offered insights that only machine learning can produce in a timely fashion.  Their value add was clear in the commodities space during this crisis.

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The next generation of re-bundling with embedded finance is on its way

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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. 

The rebundling of scattered digital financial services is underway at different stages in the various subsectors. Rob advisors adding consumer banking services, digital lending platforms adding investment offerings, mobile-only banks adding insurance and investment services, and on and on. Embedded finance (sorry Ron Shevlin for using this buzz word), platformification of banking, or creating an Asian Internet of Finance Western edition; in one form or another these trends are unstoppable. By now, they are the normal.

So what does the next generation of re-bundling look like and has it started already?

Last January, I wrote a post that seemed very much out there (and to some maybe it still sounds awkward) that looked at the existing silo between Food and Finance.

Food and Finance blurring through technology

With the COVID-19 lockdowns still fresh in our minds, managing uncertainty, enhancing our immune system, and the connection between our finances and our health (physical and mental) are very clear.

The current setting makes it very easy to understand Paolo Sironi`s dense FMT theory. Just focus on the main underpinning of FMT: Finance is about managing uncertainty. Of course, this is not reversable. Meaning managing uncertainty is not only about finances. Our physical and mental health are a big part of dealing with uncertainty. In my earlier piece I took the scientific views of biomedical gerontologists and of philosophers who foresee `the last days of death` and linked them to what Hippocrates and the Chinese have been preaching for centuries:

Food could become our best medicine

The research and innovation on this front, is already underway. At the upcoming Decoding the nature of future conference in Paris (Fall 2020), several important topics will be discussed. I zoom into the big topic of linking Food with Health and the trends around customized products for the future. AlimAvenir, a French specialist in the food innovation, has published a study addressing four main big topics

  1. Feeding 9.8 billion people in 2050: alternative proteins?
  2. The kitchen of the future: digitally assisted home cooking or robots?
  3. Food and health: are customized products the future?
  4. Transparency, traceability and future imperatives

Under the 3rd topic, of my interest, they look at customized food, the role of microbiota in certain pathologies that are big issues (like obesity, diabetes, gastro diseases etc). They conclude that smart IOT devices will allow advancements in this direction. They see growth in preventative medicine.

They also see reducing uncertainty, repricing financial contracts based on genetic data. They suggest that this decade will also force us to decide on how genetic data is shared. Technology aims to improve our genetic data (in this framework through customized food) so that a mortgage provider (be it a bank, or a non-bank) can customize the tenor of the mortgage accordingly. With better or improving genetic data, the tenor of the mortgage can lengthen.

Customized food can improve genetic data which in turn can improve conditions in financial contracts.  

I am starting to see a world of programmable financial contracts whose clauses are cryptographically linked to our genetic data.

In turn this data is dynamic with customized food being one of the ways we can improve it. Rejuv the spinoff from SingularityNet is one example of this kind of innovation. It is built on the Singularity Net protocol which is a decentralized protocol for benevolent AI agents (see previous post here). Rejuv connects individual health data securely, with researchers, clinics and the AI agents, to improve health via lifestyle adjustments. Tokens are used to reward members.

The silos between customized food and finance, are starting to blur in these new ways. The triangular connection I see, is now the broader Longevity sector to finance. In addition to customized food as medicine to improve genetic data, we can foresee precision, preventative, personalizes, and participatory medicine – the so called P4 – linking to finance.

Managing uncertainty in life is first about improving our physical and mental health. So starting with that as the core offering and creating a marketplace with analytics and services that improve diagnosis, prognosis, and suggest customized `journeys` (treatments), looks like the way a 2030 financial platform-ecosystem should look like.

Managing health uncertainty, advising and optimizing that aspect of our life bundled with debit, credit, insurance, and investments, is the way to go.

Customized health and wellness journeys improve our longevity (length and quality of life) which leads to a more pressing need to manage jointly our finances.

 One example of innovation in this direction is the Longevity Bank, soon to be launched, by longevity scientist experts. I met Dmitry Kaminsky, cofounder of Longevity Bank, in Davos and included the initiative (No. 33) in `Celebrating the WEF 50th anniversary with 50 bytes from Davos 2020`. Their motto is Health is the New Wealth.

The challenging link between genetic data, health data, financial data and corresponding analytics on each of these areas, is the next re-bundling wave. The opportunity is big and the complexity is breathtaking.

Margaretta Colangelo and Dmitry Kaminsky, the authors of the Longevity Industry book, call the Longevity industry, the Biggest and Most complex industry in human history. I think this statement will have no challengers. Yet we are marching towards a rebundling with finance.

Stay tuned.

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.

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`You Can Marcus`

Goldman Sachs is one to watch.

It is an example of how sticky a banking brand name is – It has shredded off scandals in the past and the recent Malaysian state-run fund scandal seems no different. Sack Goldmans – a 2010 slogan – did not stick.

Goldman Sachs is an example of how an incumbent builds a Fintech business positioned in the value stack below its established competence – an investment bank getting into retail banking and wealth management for mass affluent & the hoi polloi.

Goldman Sachs is an example of how an incumbent financial institution can grow Data pools by offering free access to its analytical tools SecDB – explained in my article in the 2018 WealthTech Book  `Empowering Asset Owners and the Buy Side`.

Goldman Sachs is an example of how an incumbent financial institution can grow Data pools by partnering with Apple on a credit card – Apple has 900 million devices and it is expected that the Apple Card will bring 21 million users to GS by year end[1].

Goldman Sachs is a publicly traded company that is trading right now below book value and there are more than enough GS analysts out there to get estimates on the revenues from the different GS `consumer banking` new initiatives.

For now, Goldman Sachs has been building up aggressively deposits (the usual way of offering above-market deposit rates when entering a new market). The 3yr old deposit business has accumulated now $46billion across the US and the UK! The expected growth is in the order of $10billion per year going forward.

Marcus has issued $5billion in personal loans. These are unsecured loans that naturally, may worry shareholders, who typically get nervous easily (even though this is crumbs when taken in context).

The credit card part of the Goldman Sachs business is newer and could also grow at double-digit annual rates. Goldman Sachs knows well that credit card lending gets favorable regulatory treatment – less capital is required against this kind of debt – and as long as this holds it is a win-win situation. Why? Simply because Goldman Sachs will get their hands on valuable data from retailers and their shoppers, in order to process the Apple credit card application.

Goldman Sachs hits two birds with one stone. It gets to issue consumer debt on a global scale with lighter capital requirements, and it gets to process new, valuable consumer data globally.

The Apple & Goldman Sachs card economic terms are not known. Even if they are not that juicy for Goldman Sachs and even though the GS logo is on the back of the Apple card; the consumer data access and processing from 40 countries that this brings to the table is invaluable.

The Apple & Goldman card will grow an important global data pool for Goldman Sachs to leverage in its planned WealthTech offering.

In case you haven’t noticed, Marcus has been moved into the Goldman Sachs asset management unit, which will be renamed the consumer and investment management division. The October 2018 memo says that Marcus has plans to “launch a broader wealth management offering.”

A global consumer outreach is being built in preparation of this broader wealth management offering. And for all those concerned about a growing unsecured loan book, Goldman has great risk management experience and could with great elegance securitize part of this debt, once there is enough to do so. Elizabeth Dilts and Anna Irrera, raise this point too in ` Goldman’s Apple pairing furthers bank’s mass market ambitions`.

Marcus is a brand whose heritage is in risk management and investment banking. They will use these competences to manage growth in their retail-focused wealth management offering. This is a huge advantage compared to Fintechs that started with unbundling a specific financial service (be it loans, or deposits, or investments) and is now, growing by rebundbling additional services (e.g. adding robo-advisors to loans, or deposits to trading, ect).

I have no positions or commercial relationships with the companies or people mentioned. I am not receiving compensation for this post.

I have written about Marcus several times.

Just after the launch of Marcus in late 2016, Will Goldman become a verb? Watch the Marcus ads!

Just after the Marcus rebranding and UK launch in Fall 2018, Welcome Marcus to the rebranded Goldman asset mgt division and to the UK

Screen Shot 2019-04-25 at 10.01.03.png

I must however, confess that I have no idea how to interpret the new Marcus Campaign ‘You Can Money’.  Is this an example of new Fintech language? If you have other such rarities, please send them to me, as I collect them. Maybe we can tokenize them, with the hope that they become the next non-fungible craze.

[1] A Seeking Alpha article that includes several links, for anyone who wants to dive into more details https://seekingalpha.com/article/4251792-buy-goldman-sachs-apple-card

Sources: CNBC, Barrons, Financial Brand, Crowdfundinsider, The economist

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

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).

Ready for a dynamic, digital, and unstable world, like in nature?

Last week Christine Lagarde moderated a panel with two Central bankers (European Central Bank and CB of Kenya), an incumbent (JPMorgan) and a disruptor (crypto fintech company Circle). The topic was “Money and Payments in the Digital Age.”

CCN covered the panel discussion with a narrative of `In crypto we trust`. Coindesk covered it with a rhetorical question narrative of `In Math we Trust?`.

It is already six months since I covered Blockchain from a policy angle in `In the EU Blockchain Resolution we Trust`. Building Trust through disintermediation is the line of thinking behind the Blockchain Resolution which is still a work in progress. Europe continues to be the thought leader at the policy level with this initiative which has immense potential. During the same period, I had the privilege of attending the talk of Dr. Zhang on the topic “In Math we Trust” and moderating a session with him at the LCX Blockchain Series, in Vaduz, Liechtenstein. Dr. Zhang, was a renowned Chinese American scientist, a physics professor at Stanford and I remain inspired by his narrative.[1]

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The powerful origin of the narrative `In Math we could Trust`

Let’s go back to the Greeks where thought leadership of all theoretical and foundational concepts started. Dr. Zhang spoke about Archimedes, his Eureka moment which permitted gold to become a medium of exchange. He spoke about the 2nd law of thermodynamics which states that the natural world is mostly in disorder and rarely in order (consensus state). In nature, order and consensus can only exist in subsystems. And when this happens it happens at a cost. In physics parlance, in order to reach order and consensus in nature, there needs to be some entropy (disorder) produced and dumped outside the subsystem for it to reach consensus.

Let’s tie this to the computing world. In distributed computing, the Fischer-Lynch-Patterson theorem is the analog of the 2nd law of thermodynamics and proves that there is No deterministic algorithm that can be a master algorithm for the system to reach consensus. So, once again science like in nature, proves that to reach consensus we need to pay a cost. This is where the Proof of Work, an old cryptographic concept, comes into play.

One way we can reach consensus regarding transactions is by using Proof of work. This is a way, to reach consensus on the Temporal Order of transactional data. The cost we pay is the amount of electricity we burn to solve the puzzle (which is on the other hand easily verifiable). Consensus on time-stamped verification of transactional data, can be reached through this process that dumps entropy (electricity in the case Bitcoin Blockchain) outside the system.

Our world historically has been oscillating between centralization and decentralization.

big bangLooking back in history for more evidence: The circuit switch technology created the then seemingly indestructible monopoly of ATT. This monopoly was only destroyed form the decentralized TCP/IP protocol that gave birth to the internet and to the gradual adoption of VOIP. As the internet became the dominant technology, several other monopolies grew out of the content generated on it; e.g. Google and Facebook. And now, we are in the beginnings of what Paul Nunes coins as the next Big Bang disruptionBlockchain is threatening the powerful giants built on the first open source protocol, the internet, with a wave of data decentralization.

The internet has evidently increased connectedness. However, its design is not a collaborative one. The world that is built on top of this open protocol, the internet, is not a world that is more fair and that builds trust. The “trading” or any exchange of information on the web, is not collaborative. The central entities, the Googles and Facebooks, are the ones that are organizing the information and the data on the web. The first, step in the process of decentralizing the web, is to break these data monopolies.

Blockchain is a decentralized mechanism in which trust is built-in with mathematical formulas. As Plato preached, mathematics is the ONLY internally consistent language. As Nick Szabo preached, in his God protocols, mathematics is the language of God. God in this context is the entity that acts in the interest of everybody.

Blockchain protocols are presenting us with an opportunity to build on protocols with built-in consensus mechanism governed by math. Mathematics governance guarantees fairness and trust.

Dr. Zhang argued in this speech that we humans have developed languages and law in our attempts to organize and collaborate in societies and reach consensus on various issues. He now believes that we are stepping into the most advanced era in which Mathematics will be trusted in order to reach consensus. Admittedly from all the sciences (social, political, physics etc.) mathematics is the branch of knowledge with the highest level of consensus and in which we trust.

Dr. Zhang emphasized that we live in a world that is based on theoretical mathematics that were developed with no real-world application in mind and are now being used in all sorts of experimentations as we are in the early stages of the blockchain development. From hash functions to more such `abstract first` math concepts.

  • Public/private key based on elliptic curve
  • Cryptographic hash function
  • Zero-knowledge proof. Zk-snark and Zk-stark
  • Secure multi-party computation, differential privacy
  • Formal verification
  • Homomorphic encryption
  • Dag, directed acyclic graph: money grows on trees!

Source: from Dr. Zhang`s talk; see full video here.

The choice we have is to `Trust in Math`

 Look at the 2nd law of thermodynamics, nature, and the lessons from the earlier tech disruption waves. Once we embrace the dynamic, digital, and unstable world we live in; we will realize that we have a great opportunity to embrace theoretical mathematics in designing governance and the Internet of value.

It will be a trustworthy design with inherent instabilities as in nature and as outlined in the 2nd law of thermodynamics. We have to move away from the belief that forced consensus mechanisms like regulations can provide stability.

[1] I delayed this post because of the unfortunate and sudden passing away of Dr. Zhang late last year.

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 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).

How does One Consume an Ocean of Data? A Meaningful Sip at a Time

So many data, so many ways to use it, ignore it, misapply it, co-opt, brag, and lament about it.  It’s the new oil as suggested not long ago by Clive Humby, data scientist, and has been written of recently by authorities such as Bernard Marr in  Forbes wherein he discusses the apt and not so apt comparison of data and oil.  Data are, or data is?  Can’t even fully agree on that application of the plural (I’m in the ‘are’ camp.)  There’s an ongoing and serious debate on who ‘owns’ data- is possession 9/10 of the law?  Not if one considers the regs of GDPR, and since few industries possess, use, leverage and monetize data more than the insurance industry forward-thinking industry players need to have a well-considered plan for working with data, for, at the end of the day it’s not having the oil, but having the refined byproduct of it, correct?

Tim Stack of technologies solutions company, Cisco, has blogged that 5 quintillion bytes of data are produced daily by IoT devices.  That’s 5,000,000,000,000,000,000 bytes of data; if each were a gallon of oil the volume would more than fill the Atlantic Ocean.  Just IoT generated bits and bytes.  Yes, we have data, we are flush with it.  One can’t drink the ocean, but must deal with it, yes?

I was fortunate to be able to broach the topic of data availability with two smart technologists who are also involved with the insurance industry, Lakshan De Silva, CTO of Intellect SEEC, and Christopher Frankland , Head of Strategic Partnerships, ReSource Pro and Founder, InsurTech 360″.  Turns out there is so much to discuss that the volume of information would more than fill this column- not by an IoT quintillions’ factor but a by a lot. 

With so much data to consider, it’s agreed between the two that
understanding the need of data usage guides the pursuit.  Machine Learning (ML) is a popular and
meaningful application of data, and “can bring with it incredible opportunity around
innovation and automation. It is however, indeed a Brave New World,” comments
Mr. Frankland.  Continuing, “Unless you
have a deep grasp or working knowledge of the industry you are targeting and a
thorough understanding of the end-to-end process, the risk and potential for hidden technical debt is real.” 

What?  Too much data, ML methods to
help, but now there’s ‘hidden technical debt’ issues?  Oil is not that complicated- extract, refine,
use.  (Of course as Bernard Marr reminds
us there are many other concerns with use of natural resources.)  Data- plug it into algorithms, get refined ML
results.  But as noted in Hidden
Technical Debt in Machine Learning Systems
, ML brings challenges of which
data users/analyzers must be aware- compounding of complex issues.  ML can’t be allowed to play without adult
supervision, else ML will stray from the yard.

From a different perspective Mr. De Silva notes that the explosion of
data (and availability of those data) is, “another example of disruption within
the insurance industry.”  Traditional methods
of data use (actuarial practices) are one form of analysis to solve risk problems,
but there is now a tradeoff of “what risk you understand upfront”, and “what
you will understand through the life of a policy.”  Those IoT (or, IoE- Internet of Everything,
per Mr. De Silva) data that accumulate in such volume can, if managed/assessed efficiently,
open up ‘pay as you go’ insurance products and fraud tool opportunities.

Another caution from Mr. De Silva- assume all data are wrong unless you prove it otherwise. This isn’t as threatening a challenge as it sounds- with the vast quantity and sourcing of data- triangulation methods can be applied to provide a tighter reliability to the data, and (somewhat counterintuitively,) with the analysis of unstructured data with structured across multiple providers and data connectors one can be helped to achieve ‘cleaner’ (reliable) data.  Intellect SEEC’s US data set alone has 10,000 connectors (most don’t even agree with each other on material risk factors) with 1,000s of elements per connector, then multiply that by up to 30-35 million companies, then by the locations per company and then directors/officer of the company. That’s just the start before one considers effects of IoE.

In other words- existing linear modeling remains meaningful, but with the instant volume of data now available through less traditional sources carriers will remain competitive only with purposeful approaches to that volume of data.  Again, understand the challenge, and use it or your competition will.

So many data, so many applications for it.  How’s a company to know how to step
next?  If not an ocean of data, it sure
is a delivery from a fire hose.  The
discussion with Messrs. De Silva and Frankland provided some insight.

Avoiding Hidden Debt and leveraging clean data is the path to a “Digital Transformation Journey”, per Mr. Frankland.  He recommends a careful alignment of “People, Process, and Technology.”  A carrier will be challenged to create an ML-based renewal process absent involvement of human capital as a buffer to unexpected outcomes being generated by AI tools.  And, ‘innovating from the customer backwards’ (the Insurance Elephant’s favorite directive)  will help lead the carrier in focusing tech efforts and data analysis on what the end customers say they need from the carrier’s products. (additional depth to this topic can be found in Mr. Frankland’s upcoming Linked In article that will take a closer look at the challenges around ML, risk and technical debt.)

In similar thinking Mr. De Silva suggests a collaboration of business facets to unlearn, relearn, and deep learn (from data up instead of user domain down), fuel ML techniques with not just data, but proven data, and employ ‘Speed of Thought’ techniques in response to the need for carriers to build products/services their customers need.  Per Mr. De Silva:

“Any company not explicitly moving to Cloud-first ML in the next 12 months and  Cloud Only ML strategy in the next two years will simply not be able to compete.”

Those are pointed but supported words- all those data, and companies need
to be able to take the crude and produce refined, actionable data for their operations
and customer products.

In terms of tackling Hidden Debt and ‘black box’ outcomes, Mr. Frankland
advises that points such as training for a digital workforce, customer journey
mapping, organization-wide definition of data strategies, and careful application
and integration of governance measures and process risk mitigation will  collectively act as an antidote to the two
unwelcome potential outcomes.

Data wrangling- doable, or not? 
Some examples in the market (and there are a lot more) suggest yes.

HazardHub

Consider the volume of hazard data available for consideration within a jurisdiction
or for a property- flood exposure, wildfire risk, distance to fire response
authorities, chance of sinkholes, blizzards, tornadoes, hurricanes, earthquakes
or hurricanes.  Huge pools of data in a
wide variety of sources.  Can those
disparate sources and data points be managed, scored and provided to property
owners, carriers, or municipalities? 
Yes, they can, per Bob
Frady
of HazardHub, provider of
comprehensive risk data for property owners. 
And as for the volume of new data engulfing the industry?  Bob suggests don’t overlook ‘old’ data- it’s
there for the analyzing.

Lucep

How about the challenge sales organizations have in dealing with customer requests coming from the myriad of access points, including voice, smart phone, computer, referral, online, walk-in, whatever?  Can those many options be dealt with on an equal basis, promptly, predictably from omnichannel data sources?  Seems a data inundation challenge, but one that can be overcome effectively per Lucep, a global technology firm founded on the premise that data sources can be leveraged equally to serve a company’s sales needs, and respond to customers’ desires to have instant service.

Shepherd Network

As for the 5 quintillion daily IOT data points- can that volume become meaningful if a focused approach is taken by the tech provider, a perspective that can serve a previously underserved customer?   Consider unique and/or older building structures or other assets that traditionally have been sources of unexpected structural, mechanical or equipment issues.  Integrate IoT sensors within those assets, and build a risk analytics and property management system that business property owners can use to reduce maintenance and downtime costs for assets of any almost any type.  UK-basedShepherd Network has found a clever way to ‘close the valve’ on IoT data, applying monitoring, ML, and communication techniques that can provide a dynamic scorecard for a firm’s assets.

In each case the subject firms see the ocean of data, understand the
customers’ needs, and apply high-level analysis methods to the data that
becomes useful and/or actionable for the firms’ customers.  They aren’t dealing with all the crude, just
the refined parts that make sense.

In discussion I learned of Petabytes,  Exabytes, Yettabytes, and Zottabytes of data.  Unfathomable volumes of data, a universe full, all useful but inaccessible without a purpose for the data.  Data use is the disruptor, as is application of data analysis tools, and awareness of what one’s customer needs.  As Bernard Marr notes- oil is not an infinite resource, but data seemingly are.  Data volume will continue to expand but prudent firms/carriers will focus on those data that will serve their customers and the respective firm’s business plans.

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).

Life & Health Insurance is critical to our lives. Here is why.

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I’ve always been a Life guy when it comes to Insurance.  While there is a lot of interesting stuff on the Property and Casualty space (P&C), there is always interest (and a special place in my heart) for Life. It’s where I started and grew up in this industry and where I plan to focus for years to come.

A few weeks ago, I was speaking with my friend Andrew Johnston, (Global head of InsurTech Research for Willis Re).  He informed me that the Q2 2018 Quarterly Briefing (which is put together by Willis Re and Willis Towers Watson Securities, with data and graphs from CB Insights) would be focused on Life and Health. This got me very excited and I asked if I could review the report earlier this week before it was publicly released (under embargo of course), so I could be the first one to write about it.

Of the many research publications on Insurtech (not the daily/weekly ones I previously shared), the Insurtech Quarterly Briefing is one that I look forward to the most.

I look forward to it because it is a great combination of 1) numbers/investments 2) thought leadership, and 3) quality company profiles (aka – if a company is in this report, I know they are a quality company).  

This week, I summarize some key findings from the report along the lines of these three areas.  I do encourage you to read the full report here.  I’ve also included a link to all the previous Quarterly Briefings at the end of this article for reference.

Q2 Investment in Insurtech

Funding throughout the quarters

Investments by country

The quantitative analysis can be found toward the end of the report.  Some highlights from these numbers can be found below (taken from the report):

  • There were 71 InsurTech deals with a total value of $579 million
  • The deal count was 8% higher than in Q1 2018, with total funding amount down 20%
    • While deal volume is up from Q1, total funding is down 20% due to a lack of high-dollar transactions, like the seven $30+ million transactions we saw in Q1 (vs. the two $30+ million transactions in Q2 2018)
  • 71 transactions in Q2 2018 represents the highest transaction volume of any quarter to date
  • For Life and Health (L&H), the 27 transactions announced in the quarter were the highest amount since Q2 of 2017 and the second highest on record
  • 43% percent of P&C and 56% of L&H transactions in Q2 2018 involved B2B companies, compared with 35% and 47%, respectively, of all transactions since 2013
  • With a total of 34 investments, Q2 2018 set a new record for the volume of technology investments by (re)insurers and represents an increase of 26% and 6% from Q1 2018 and Q2 2017, respectively
  • Investment from international markets remains strong; transactions outside of the U.S. account for 58% of total transactions since 2013 and 62% in the quarter
  • There were 22 strategic partnerships between (re) insurers and technology companies in the quarter, which equaled the same amount seen in Q1 2018

Further, as noted in Willis Towers Watson Securities’ CEO, Rafal Walkiewicz’s forward, ‘Life & Health InsurTech has attracted more than $5 billion in funding over the last five years, 20% more than P&C over the same time period’.  Further, L&H ‘funding rounds leading to an average funding round size that is 45% larger than the average for P&C.’

What can be derived from these stats?

Firstly, seeing more investment in Insurtech outside of the U.S. should come as no surprise, especially from countries like China, India and the UK.  China has actually leapfrogged a few other countries to go second after the US last quarter – perhaps a sign of things to come?

Secondly, a record number of investments from (re)insurers should also come as no surprise.  After all, just take a look at the number that have VC arms now.

Lastly, I was pleasantly surprised to see that there has been more investment into L&H vs. P&C since 2013.

At the many conferences and events I have attended, as well as the daily articles I read, there seems to be a slightly higher focus on what’s happening in the P&C space vs. the L&H.  I’ve even had some people from the P&C side tell me they think the L&H side is boring as compared to P&C.  

I would agree there are more products in P&C to enhance and innovate  (travel, home, renters, auto, business, gig economy, etc etc).

However, with L&H, we have the opportunity to help people live longer and healthier lives.  

How boring can that be? (italics inserted in place for cynicism I can’t express through just writing…)

Data, Customer Centricity and Advisory Services

3 pillars

Regardless of the line of business of focus for Insurtech initiatives, how to harness new sources of data, build more customer centric products and provide services above and beyond just paying claims are on the agenda of all (re)insurers and entrepreneurs.  

For L&H, as Mr. Walkiewicz points out, ‘the complexity of change occurring within the Life & Health insurance value chain is much greater than in other insurance subsectors and the potentially positive impact on the quality of life for the consumer is much more profound.’  

All three of these pillars can help to enhance the L&H value chain to help individuals live longer and healthier lives.  

ecosystem

Use of Data

Data areas of interest

As with other lines of business, there is a lot of data already existent within the L&H processes and more and more data becoming available.

The need and amount for medical information in order to provide preventative advice as well as to pay claims is very high and very messy.  How can this information be shared between doctors and patients to provide better care? How can this information be shared between hospitals and (re)insurers to pay faster claims?

There are new data sets being brought to the foray from use of wearables and genomic reports.  How will this data be used for underwriting (taking into account legal and ethical considerations)?  How will this data be used to provide better advice and engagement to customers?

And how will all of this data be incorporated into existing (legacy) systems, processes and analytics that the company undergoes?

dacadoo is one company helping with this, by creating a Health Score, similar to a Financial Credit Score.  Their solution helps with engagement of customers by providing personalized feedback on their lifestyle.  The Health Score also provides Insurers with a different data set to help them with underwriting and ongoing monitoring of premium rates based on the individual’s health choices.  

Atidot is a company helping on the data and predictive analytics side.  They are one of the only providers I have seen specifically focused on Life products.  Their solution targets three groups of individuals – CFOs and Actuaries, Sales/Distribution teams and Retention/Customer Care teams.  They help all of these teams with providing better insights on their in-force book of business as well as information to reach out to their policyholders (in the case of a cross-sell/up-sell or potential lapsation).  For full disclosure, Atidot has been a client of mine this year.

Customer Centric Products

products area of interest

For all Insurance products, the way in which consumers determine their needs (especially when purchasing digitally) can definitely be improved.  Further, the products that they buy can be more flexible in nature.

In P&C, we have seen UBI products, especially for Auto, which offer policyholders the opportunity to pay for the exact amount of miles that they drive.  The use of telematics will also help in providing policyholders discounts for driving better (and potentially premium hikes for driving poorly!).

For L&H, these types of products (UBI based) are a bit more difficult to imagine.  However, using the advanced data and analytics as described above can help with providing policyholders and Insurers with more information as individuals progress through their lives.  

As such, the process for determining the amount of coverage an individual needs at the purchase of their policy as well as throughout the term of their policy are of utmost importance.  Further, products should be built in such a way that are more flexible for policyholders and less onerous if they have a change in needs (i.e. limited additional invasive underwriting).

Anorak is a fully automated, fully digital Life insurance advisory platform looking to tackle the meaningful protection gap of nearly 8.5 million individuals in the UK who are currently uncovered or under-covered by Life insurance.  The process starts with a ‘check-up’, which is like a needs analysis for the individual on their current situation.  Once this is done, ‘impartial advice’ is provided on what type of cover the individual may need. Then, three policy options are provided to the individual.  Their product offers an API which can be integrated into price comparison sites, agencies, online retailers and more.

As a person that likes to focus on the needs of and suitability of products being recommended to an individual, I love what Anorak is offering.  

Ladder Life is a digital MGA that offers online Term Life Insurance.  Their tagline of ‘Life Insurance Just Got Easier’ can be seen through their quote and application process:

  • Consumer answers a brief set of questions (many responses are avoided through supplemental data).
  • Consumer receives an instant insurance quote with no obligation to purchase.
  • If supplemental information is needed, Ladder sends a medical professional to complete an exam (free of charge to the customer).

In most cases, the need for blood and urine samples for underwriting has been eliminated.  

Further, they offer a solution to allow policyholders to adjust their coverage as needs change (called ‘laddering’), without paperwork, meetings, phone calls, cancellation or penalty fees.

For those of you who have ever sold or bought Life Insurance, I hope you would agree that the process and flexibility outlined above are better than some of the ‘traditional’ methods.

Lastly, they have just launched the Ladder API and a partnership with Sofi, helping to provide a more extensive offering to the individuals in Sofi’s ecosystem.

Regard has designed products to address coverage gaps and rising out-of-pocket expenses that many customers in the U.S. face as more employers shift to high deductible Health plans.

This is a huge and increasingly larger problem for the U.S. health market.  

According to the report, Regard differs from many other InsurTech agencies in three important ways: (1) emphasis on institutional distribution needs as the starting point for product and technology vs. a direct to consumer strategy; (2) its position at the nexus of new specialty insurance products and proprietary technology vs. traditional products with off the shelf IT; (3) its ability to participate in premium income through risk retention in addition to MGA commissions compared to a commission-only revenue model.

Digital Advisory Services

advisory services area of interest

Insurance is in an age where we are transforming from a collector of premiums and payor of claims, to a service provider that helps individuals manage and prevent risk in their day-to-day lives.  

The solutions that are becoming available in the L&H space are helping with this to enable policyholders to connect with their doctors and hospitals as well as have 24/7 services through the use of AI and machine learning.  This will help to make L&H products more interactive for their policyholders with an ultimate aim of living longer and healthier lives.

Boundlss provides a AI-powered health assistant for L&H insurers.  Their platform helps to support individuals with their wellness goals and acts as a ‘personal trainer/health coach’ for users.  

The Boundlss platform collects, analyzes and aggregates data from over 400 wearables and mobile apps, allowing insurers to gain new insights into their member populations and their behaviors.

Further, if the AI engine does not provide a response that is sufficient for the individual, a human coach can be brought seamlessly into the conversation.  

Oscar is a fully licensed Health Insurer operating in New York, New Jersey, California, Texas, Ohio and Tennessee.  I’ve covered Oscar before when writing about my own Health Insurance purchase and I believe they are a company to watch for the years to come.  With their recent rise from Alphabet, you can ensure that building a digital ecosystem will continue to be at the forefront for this company.  I liken the ecosystem they are building to that of Ping An and believe they have begun to build a company that is focused equally on advisory services as it is on delivering a quality product.    

‘The Insurtech Grand Prix’

It would be remiss of me if I did not mention the thought leadership piece in the Briefing by Greg Solomon, Head of Life & Health Reinsurance at Willis Re International.  Greg is based in Hong Kong.   

In this, he uses the analogy of the Grand Prix to identify initiatives that are:

  • ‘In the Driver’s Seat’ – Insurers taking a lead in initiatives to better distribute and interact with their policyholders with examples coming out of South Africa and Asia.
  • ‘Rear-wheel drive’ – developments outside of the L&H sector that are changing the dynamic of how products are distributed and built for customers.  Examples here include the joint healthcare venture with JPMorgan & Chase, Berkshire and Amazon, online search and aggregator sites
  • ‘Emerging Tech: Towing Them Along’ – different technologies such as Machine Learning, AI, Blockchain, cryptocurrency and genetic testing, that could change/enhance many elements of the L&H value chain.  
  • ‘Along for the ride’ – tools like wellness platforms that help Insurers be alongside of their policyholders throughout the life of their policy.  

Summary

From this summary, I hope for two things:

  1. That you are inclined to read the whole report!  You can find the link here. There is a lot more information in there, especially deep-dives on the companies mentioned including interviews from their management team.
  2. That I was able to change some of the P&C folks’ opinions of L&H from boring to at least interesting!

I am pleased to see the amount of investment and transformation in the L&H space.  As with all other lines, it is long overdue.

The opportunity for our industry to be part of helping individuals live longer and healthier lives is extremely exciting for me (and should be for you, too).   

Previous Quarterly Briefings

Q1 2017

Q2 2017

Q3 2017

Q4 2017

Q1 2018

Stephen Goldstein is an experienced Insurance executive and Insurtech dealmaker with a core focus on growing revenue, launching go to market initiatives and advising industry leaders.

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