Insurance Investment

kWh Analytics

The solar industry is going through an unprecedented amount of change, and opportunities to leverage its success are bigger than ever. In the last fifteen years, solar energy has expanded from a niche technology to an important asset class. According to a study by Bloomberg New Energy Finance, from 2004 to 2015, over a trillion dollars of capital has been deployed in solar project, at a compounded annual growth rate of 27%.

At Anthemis, our investment strategy has always been to focus on companies that pioneer technology-driven structural changes to the financial services industry. In our view, emerging financial markets need both a shared data language and risk appreciation to become prevalent asset classes.

Until now, these two critical components have been missing in the solar energy market. kWh Analytics was founded to bring new data to the solar industry. From inception, kWh has built a unique industry database and toolset to help investors understand and improve solar investment performance. The company is now launching a solar production insurance product, Powerlock, to protect asset owners and debt providers. kWh is already working with some of the biggest names in the asset management space as well as global insurance carriers.

Despite the huge growth of the solar energy sector, the industry is still a tiny fraction of global electricity production. With the cost of solar energy production declining, and an increasing understanding of the necessity to change our energy production mix, the solar energy market is poised for growth.

Richard Matsui and the kWh team have formidable experience in the solar energy market, having worked in the industry since its infancy. Anthemis is excited to support them by leading their Series A as they continue developing solutions that provide the necessary components to accelerate growth in this expanding asset class. Not only do we believe solar is a great business opportunity, it is also part of an important problem to tackle.

Insurance Mindset

InsurTech or CapitalTech?

There was recently a great post by Steve Evans over at Artemis on Swiss Re latest results and what they mean for the insurance industry. I encourage you to go and read it:

Among the many interesting points in this article, a few stood up:

There is a growing recognition, both within insurance or reinsurance and on the outside looking in, that the risk -> insurance -> reinsurance ->retrocession capital value-chain is too long and convoluted, with too many points where value is extracted by intermediation and therefore there are plenty of opportunities to disrupt this process.

At Anthemis, one of our core thesis since we started investing in Financial Services startups is that technology will have/is having a massive influence on how the traditional financial services stack is organized, driven by two main factors:

  • that technology impacts transaction costs (in a Ronald Case sense) and challenges vertical integration in banks or the role of traditional distributors / aggregators / match makers.
  • that digital distribution is not a channel but an integrated part of digital services built by companies with a different skillset / mindset.

To put it visually, the historically known paths from Capital to Applications/Services are increasingly challenged by a variety of solutions.

The insurance industry is uniquely different from the banking industry in the fact that it is, at core, largely fragmented in various layers (for a discussion on geographical differences around this:

risk -> insurance -> reinsurance -> retrocession

Each has players that have historically cemented their position around their moat (the following is oversimplifying things, there are much more nuances in the skills of each player, for example the risk capabilities of reinsurers):

  • Brokers by benefitting from recurring revenue after the steep working capital needs of acquisition (think Saas model effectively).
  • Insurers by leveraging historical loss data incurred at capital cost to build underwriting knowledge.
  • Reinsurer by growing large enough balance sheet to maintain the necessary diversification.

Enter digital technology and suddenly these moats starts to seem less important:

  • Digital distribution (especially if embedded with services) challenging the nature of customer acquisition.
  • Non insurance proprietary data as well as an increase in computational capabilities challenging traditional underwriting.
  • Transparency and electronic markets opening the gates for more alternative capital providers looking for uncorrelated returns.

It is therefore no surprise to read in the same article:

“Swiss Re has to have access to the risks it wants to underwrite,” Kielholz explains, going on to describe initiatives such as Global Partnerships, which develops opportunities with governments and supranational institutions, and its Life Capital Partners, which accesses life risks through distribution or by buying closed-books, as examples of this trend.

We’d also add the reinsurers corporate solutions work as an example of the firm seeking to more directly access risk and more efficiently deploy its capital.

Insurtech startups have a unique opportunity to be the key players of the reorganization of the insurance industry around these new efficiencies.

Some of our key focus at Anthemis are on startups:

  • that are working on making risk more granular and transparent to allow better matching to capital AND
  • that are working on reducing the path from risk to capital and/or expending access for capital.

If you are building such a company, don’t hesitate to reach out.

Lending Market Watch Personal Finance

Is a European consumer credit model coming to the US via ecommerce?

If you are a retail customer in most European countries, you are quite familiar with this sight:

Selling credit or selling televisions?

If you are from the US or the UK, chances are this will not be a recognizable image. There is a fundamental difference in the way retail consumer credit is built and distributed between the US/UK and Central Europe, which can be summarized as (I am oversimplifying):

  • USA/UK > revolving credit through credit card (financial institution or merchants)
  • Rest of Europe > installment credit in store or through payment card (financial institution or merchants), often linked to a specific purchase. The interest cost is either carried by the customer or the merchant.

In that perspective the launch of companies such as Affirm, Bread in the US, as well as the geographic extension of Klarna are quite interesting. Effectively, they are introducing in the US installment credit at checkout, a very European product dominated in its traditional form by companies often linked to banks such as BNP Paribas Personal Finance, Santander Consumer Finance, Credit Agricole Consumer Finance, except the new players are starting from e-commerce and mobile e-commerce checkout.

The daily life of an ecommerce site

If there is one think that ecommerce website hate, its the Cart Abandonment Rate. It basically means you had everything right from acquisition to product mix, to site/app experience but when comes the time to pay, the user drops out. Making the checkout experience seamless is a constant concern and making any change / adding additional steps is a risky move. This will be a challenge for the emerging players who have to prove offering credit brings more users than it deters in the checkout process.

On a broader scope, the impact of Fintech on the American Credit Landscape is one of the most interesting trends in the recent years. Whether it is the new crop of credit at checkout players or the marketplaces lenders such as Payoff* or Lendingclub, they all participate in Credit Transformation, moving consumers from Revolving Credit to Fixed Term Credit. Compounded by Millenials’ attitude toward Credit Cards , I can’t wait to see what consumer finance will look like in the coming years.

Are we at the beginning of a move beyond Credit as we know it?

Insurance Investment Lending Market Watch Mindset Payments Personal Finance

2016 and beyond in fintech: a few thoughts

valthorensLooking at 2016 with the experience of the past 7 years in financial services. it will be a pivotal year for financial services. In many ways we are coming to the end of a phase, that started with the world‘s the most important financial crisis since 1929. The FED hike is upon us, after having experienced one of the most destructive slow downs, of which the effects are still very much acute through the world. While banks are still experiencing the effect of the crisis notably through restrictive regulation and a continuing string of financial scandals, their public messaging is one of innovation and change. For the raft of new players in financial services, the funding environment has never been as good and some striking successes are showing the way forward. A new wave of technology, from blockchain to AI and omnipresent sensors is shaping a new world (hopefully not brave).

2016 will see a strong competition in the pure banking space. In Europe especially, a crop of new banks and alternative banks will push strongly into the market. European digital banks such as Fidor are expanding beyond their core market.The UK regulator’s move to lowering the barrier to entry in becoming a bank will come to reality with Atom Bank, Tandem, (…) establishing themselves as a brand to customers. Alternative solutions based on prepaid born in various european countries are also expanding beyond borders, with SEPA as a core foundation to propose bank like services. With PSD2 looming, traditional banks have the opportunity / incentive to more easily expand collaboratively with startups.

What about the alternative lenders, roboadvisors and other digital financial services players? Having spent the last few years building a trusted brand as well as consolidating a customer base, it is highly possible they will start leveraging this to expand horizontally into other markets, whether collaboratively or by launching their own services. For example, blended remittance and multi currencies current accounts are highly complementary services for clients with attachments to multiple countries.

This collaboration between emerging players will most likely extend beyond end customers, towards balance sheet management, especially for emerging banks looking at matching assets to their new found deposits. Looking beyond the pure European and US context, in a developing world that is increasingly interconnected, via diasporas and large economic regions, the winning platform of the coming decades appears to be the messaging platforms: Wechat, Line, Facebook Messenger and WhatsApp are all showing significant growth, engagement and increasingly financial services integration. 2016 may be the year where we will see Facebook becoming more integrated with financial services, leading with seamless blending of messaging, commerce and payment first and potentially P2P transactions next.

If 2015 was the awakening of the entrepreneurs, investors and incumbents to the potential disruption of insurance by digital first players, 2016 may well shape up to be the coming of more true game changing challengers in the space. In reality, most of the investment and company creation has gone through changing distribution to digital means, focusing mainly on two main trends: the modernization of broker first markets, such as the German or Swiss markets (following the UK market) where digital acquisition is seen as a cost efficient, scalable way to attract customers and the reorganization of health insurance in the US market following the Obamacare reform. What is maybe more interesting is the coming wave of startups looking to challenge the core business model of insurance companies, an early example of which is Oscar. These new players are leveraging lower cost, scalable infrastructures, sometimes through mutual insurance mechanisms.Increasingly, a smart use of the full insurance stack including innovative insurance companies and nimble reinsurance players allows them to succeed at lower scale. Another trend that will shape up 2016 is the change from insuring people for their things to insuring their things for them partly driven by the increasing importance of the internet of things and the facts that objects are more and more blending with the underlying services they provide.

If 2014 was seemingly the year of Bitcoin, with its pricing toping above $1,000. 2015 was the year of transitioning from currency to infrastructure. 2016 will be the year of blockchains as infrastructures for smart contracts. Financial services use cases in clearing and settlement will continue to dominate the headlines with an increasing number of pilots and low scale production releases. Financial services being multi parties by definition, they are a prime market for decentralized trusted software but the use cases go well beyond pure transactional financial services and the coming year will see an increased such projects. Large scale projects are also by definition multi parties and can benefit from a smart contract infrastructure. And lets not forget the elephant in the room, our civil infrastructure which needs to reinvent itself for the digital age.

Finally, taking a longer view, it will be important to focus on two complementary trends. Artificial Intelligence and omnipresent data. Financial services core functions are based on managing capital scarcity and information asymmetry. With the amount of data increasing at high speed, notably through widespread, cheaper and more detailed sensors and the capability to process it progressing in parallel through the use of machine intelligence, the core market of financial services will be affected. The autonomous car is the perfect metaphor for it: what is the insurance market for a sensor full, crash avoiding vehicle? But similarly, what new markets will this create for financial services?

Insurance Investment Lending Market Watch Mindset Payments Personal Finance

Discussing Machine Learning and Financial Services

It feels like fear around artificial intelligence is slightly receding and that the discussion has evolved toward what it can actually do now and its application in the current crop of technology companies. I, for one, welcome this new step in Machiavellianism from our machine overlords, well played, well played….

Robot pretending not to be able to open a door

Machine learning is about learning, measured as an improvement of an output (From the good read:

“ A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.”

Machine learning comes in various shapes but broadly there are important distinctions between:
supervised learning, in which the computer is given example inputs and desirable outputs, with the goal for the machine to learn how to map the two.
unsupervised learning, in which the inputs are not characterized and the machine has to learn what characteristics of the inputs are linked to the desirable outputs.

It is also important to understand that desirable outputs are effectively formulated as algorithms. They are very diverse and not only depend on the problem but also the approach taken to solve it. For example the graph below gives a good overview of potential solutions to a regression analysis problem.

So how does a machine think? Very differently from us as its input are more constrained data sets and its outputs are driven by equations. For example, this is how a computer views a picture of a cat on a carpet in order to be able to classify it as such.


So what can you do with a machine able to understand inputs and find correlations? A lot:


This map is already outdated increasingly, all businesses will leverage machine learning in some form and the infrastructure to do so is increasingly easier to access:
– IBM recently launched APIs to access Watson
– Google open sourced one of its core sofware Library for Machine Learning : Tensorflow
– Microsoft, just after Google announced its own open source release
– Machine learning is an increasing part of Amazon’s computing offer

It makes sense for these large players to open source a big part of their research and benefit from a broader development community. As we discussed above, an essential part of machine learning is gained from data and training, two components that remains firmly proprietary.

In Financial Services, in my mind there are short term two main cases for machine learning: one connected to the interface between humans and finance, the other to optimizing the analysis of vast pools of data.

One of my long standing pet peeve is the lack of innovation in building User Experience for Financial Services. Our representation paradigm is still too close to the accounting standard when only a very few % of the population receives the necessary education to understand those. Natural language interfaces are a rising tide for Financial services, because of their ability to abstract from pure financial data through a more intuitive user interface. This is true both for input and output. Two startups are a good example of this in my mind: Kensho and Narrative Science.

Facebook’s experimentation with M, is a strong indication of the future interface for financial services. Whether existing banks and financial services providers have the skill-set and ability to build and train these agents is an open question. Especially as AI has a strong lock-in effect.

On the data front, one of the key use of machine learning lies first with the ability to analyze at scale and speed data that would require an army of humans. A good example is satellite data. Companies such asDescartes Labs have the ability to determine crop type in satellite pictures of fields across massive sets of information. The other use case will most likely be understanding correlations in the large pool of digital breadcrumbs that people and companies increasingly create. Trading data, Credit scoring, because of their data rich outputs are prime targets. In credit scoring, one of the key concerns will be to make sure that these self learning algorithms comply with non discrimination laws.

Increasingly, if you are working on innovation in financial services and not actively looking into machine learning, you are probably doing it wrong.

Insurance Investment Lending Market Watch Mindset Payments

Eris Industries Launches Eris 0.10

Late last year, Anthemis invested in Eris Industries. Eris was known in the Bitcoin world for having worked on DOUG, a smart contract based replacement for the Bitcoin foundation (still an interesting concept in these Bitcoin governance days). We were attracted by the quality of the team and their vision. See my blog post at the time.
Eris Industries was one of the early companies to see the potential value of permissioned ledgers and understood the need to make the creation of distributed application accessible to most developers. But at core, Eris Industries’ passion and our enthusiasm for them come from their focus on smart contracts, pieces of code with unique attributes running on a Blockchain. Distributed applications built with smart contracts have the potential to change how corporations are built, how federated organization are run and to make contracts (such as securities in the financial services world) partly autonomous entities. In my view, its one of the most exciting space in technology, because of its potential impact across multiple industries and its longer term its potential impact in redefining how our society is working (a key aspect in Carlota Perez’s theory of technology and economic cycles). The fact that Bitcoin could go from a white paper shared on an obscure forum to what is it now is just a small example of what distributed applications could do.
However most great things don’t come from planned execution but from the creative destruction of innovation. A key driver of that is entrepreneurial endeavor, especially driven by democratized, open sources tools, one of the key fundamentals of why software is eating the world – to take Marc Andreessen’s words. For distributed applications to succeed, we need thousands and thousands of people building stuff this way and for that to happen we need tools to support them. With the Eris 0.10 platform, Eris Industries is building one of those tools, an application framework to build smart contract based, distributed software on Blockchains, any type of Blockchains. Because blockchain development is important, Eris Industries will also continue its contribution to the open source Tendermint project. Researching consensus solutions is another key driver for the success of blockchains in general (permissioned or permissionless).
Some of the key KPIs in an early technology cycle are simple: lines of code and developers:
– The Eris 0.10 is available on Github, the Eris Industries team has dedicated channels to support developers and documentation/tutorials are regularly updated. Read this series of tutorials on Solidity for example.
– Several developers have started to experiment and build on the Eris platform, from large software companies, to banks, insurers and startups. Everledger is one example.
For the blockchain industry to move beyond the newspapers headlines, consultant reports and generic {we could do that} use cases we need more code to be written. Its “interesting” to write about permissioned car access, automated securities settlement, distributed Uber competitors but its way cooler to start building these. We hope Eris Industries will be one of the key partners to do so.
Insurance Market Watch Mindset

Is the Insurance Industry about to change radically?

While Insurance is a massive industry, for example net premiums for life and P&C insurance the US are estimated worth around $1,000,000,000,000 (yes that is twelve zeros) I believe it has just been barely affected by the massive software driven changes that other industries are experiencing.

At core, the Insurance industry is impacted by the same underlying trends as the Banking Industry:

Online comparators have brought unheard of transparency in pricing. In markets such as the UK, 56% of consumers declared having used a price comparison website in the last 2 years. This is no exception that Google started its move in the insurance space there. In the US, a growing number of companies are looking to play this role, positioning themselves as broker with examples such as Coverhound and Policy Genius.

But that transparency is starting to expand beyond just pricing but how an insurance policy covers an individual’s risk at granular level. In mass market, insurance products, while relevant to the risk profile of the individual from the perspective of the insurer, tend to lack detailed granular level from the perspective of the customer. Companies, such as Trov (disclosure: Anthemis is an investor), are helping to create this customisation in the Home Content insurance by allowing coverage of key items vs having a policy with a coverage set with averaged amounts. Metromile is another example in the motor insurance industry.

Impact of Digital on Distribution
The Insurance Industry, due to its specificities, has evolved to a reseller model (whether captive or independent). However with online distribution channels becoming increasingly primary channels, will the insurance agent suffer the same fate as the bank branch? As digital distribution channels prefer scale, it can be expected that markets with a large number of small broker players will face intense consolidation going forward, as shown by the rapid consolidation of the UK insurance market. The success of Direct Distribution models in some countries, including China, can also be be a hint to what is coming next. Will the brokers be disintermediated? Or will they find new value add activities to justify their existence?

Screen Shot 2015-05-05 at 22.43.43

Source: MCKinsey Global Insurance Industry Insights

As we have seen in other industries, it is very difficult to turn itself into a digital first company. It may sound cliché but the Innovator Dilemma effect is playing at full when most of your current revenue base depends on traditional distribution / management methods. We are just starting to see the emergence of new digital first carriers, with companies like Oscar leading. Oscar’s mobile experience not only reduces its operational cost but helps redefine the link between carrier, customer and physician and potentially affect risk levels. It also provides a flexible platform that can adapt to emerging new technologies such as wearables.

Screen Shot 2015-05-05 at 22.56.36

Oscar’s partnership with Misfit

From a Venture Capital perspective it is an expensive proposition with an important part of capital required for regulatory requirement but taking a longer term view, these balance sheet companies may turn more attractive in a regularised rate environment.

Beyond, if you think of insurance as a class of exotic financial products, is there a possibility for larger scale disintermediation similar to banks and alternative lending possible for carriers and alternative insurance funds.

Big Data
Insurance is in many ways the historical big data business and in trendier places, one might be able to call actuaries data scientistsOne of the historical asset of insurance is in its past loss data. However with the exponential growth of sensors in our world new sources of data emerge (see Metromile as well)


Source: KPCB

Additionally companies such as Google, Facebook or Amazon are more likely to have attracted top data talents than traditional carriers. A good example is the success of Climate Corporation (a former Anthemis portfolio company, founded by an ex-Google employee and acquired in 2013 by Monsanto. By leveraging publicly available weather data including the large network of weather stations maintained by NOAA in the US and combining it with a modern big data infrastructure built from the ground up, Climate Corporation was able to provide instant weather insurance quotes.

In the B2B insurance world, for the most part, Lloyds of London still relies heavily on email and excel. When databases exist, they are often siloed from each others and sits in technology that makes live manipulation of data very difficult or impossible. Companies such as Quantemplate (disclosure: I am a non executive director and Anthemis is an investor) bring data efficiency to players that have been struggling with it. The ability to manipulate complex data has the potential to reinforce an algorithmic approach on risk in these markets.

However, perhaps even more than banking, I believe the insurance industry will be facing fundamental challenges in the near future. One way to think about it is perhaps to start again from the definition of insurance itself.

Insurance is a form of risk management primarily used to hedge against the risk of a contingent, uncertain loss. (Wikipedia)

The combination of an increased number of sensors and computational abilities has the ability to largely impact existing pools of risk. The car industry is a typical example. Statistics tend to show that the human factor a.k.a the driver is the first (by a large percentage – 94% in the US) cause of accidents. Looking further into the statistics the first two reasons are recognition error and decision error for a total of 75%. While fully automated cars may be sligthly further away in the picture, machine assisted driving is on the verge of becoming available in the mass market. Whether via radars or sensors, self braking mechanisms are being deployed by car manufacturers.


To put this in perspective, motor insurance represent about 30% of overall premiums for P&C insurance in Europe. With lower collision risks, premium will be expected to lower as well (and the additional cost of electronics onboard will not compensate for the difference). One of the underlying question is whether with lowered premiums and less variability per driver, the insurance could be embedded in the cost of the vehicle itself. At what point do insurance and guarantee start to look more and more the same?

As software is having more and more impact on our life, the question on risks attached to it, whether digital reputation, or impact of software failure in the real world is becoming more and more important. Will risk shift to software provider away from individuals and traditional companies. For example, should Google or the user be insured for a Google Car?

With more and more sensors accompanying us daily and measuring the way we move, eat and feel, is the level of contingency on an individual’s health shifting? There are several experiments by insurance companies in leveraging wearables for prevention in health plans.  Oscar is running a specific program with Misfit and Vitality‘s insurance program has a strong focus on prevention. From a  strategic point of view it makes sense for insurers to run this type of programs as they might benefit from an indirect self selection process, assuming people wearing wearables are more health conscious than the average population and have less exposure to certain risk (an equivalent of the original Progressive strategy in car insurance).

Alongside sensors the progress in genetic testing (speed and costs as well as the increased ability to run large statistical research) will also ask for a clarification of the role of insurance / and who should provide it (educating myself on this topic so leaving it at that for the moment)

If there is an industry that has no doubt on the reality of climate change, that’s the insurance industry. One of the key topic of the Geneva Association (one of the leading think tank of the insurance industry) is Extreme Event and Climate Risk. For a large part the insurance industry is relying on their historical data to derive their model around risk. However if conditions are changing rapidly, these models have a risk of becoming less relevant. This is forcing the insurance industry to reevaluate the way the work, for example fostering the creation of Open Source models and platforms. The OASIS loss modelling framework, fostered by Climate-KIC and the UK Knowledge Transfer Network, is an example of this type of approach. More generally, the emergence of cross industry data sources, models and the infrastructure to support it bear the promise to massively affect the way the brokerage and insurance industry is operating.

Finally, some of the early models of insurance were based on the sharing of risks and rewards by a community of people (in the mutual insurance model, policy holders co-own their insurance company- Benjamin Franklin “launched” this model in the US). In the last years we have seen, via the Internet and mobile now that online communities have reached a scale of unheard scale. Combining the two to reinvent the mutual insurance is therefore bound to happen.

The early players in this field for example Friendsurance and Guevara, have both started from the brokerage spectrum of the industry (as it is a much easier entry point from a regulatory / capital point of view). My way of framing what they do is that it is a form of arbitrage on deductibles. By pooling an equivalent of a low deductible insurance subscription but effectively subscribing to a high deductible insurance product, they are creating a reserve pool of cash for the group to manage first claims. Pool creation and distribution of risk among pools, effective claim management, customer acquisition journey are the type of challenges these models are facing.

The emergence of blockchain / distributed application technologies has the potential to massively increase innovation in this space. A Blockchain based mutual insurance could not only distribute risks and ownership among members but also some of its infrastructure and logic including capital contribution. If you are thinking of doing something in that field, Eris Industries (disclosure: I am a non executive director and Anthemis is an investor) are the people you should talk to!

The Insurance Industry is about to change radically and rapidly, creating a massive opportunity for innovative companies to emerge and improve massively on the way insurance is built, bought and experienced.


The Distributed Application Software Stack

Eris Industries has launched today the first components of its distributed software stack. Congratulations to the amazing team behind Eris on this first step, its the beginning of an incredible journey.
I am not going go over technical details on Eris (read and to start). You can also find a lot of information on their various blogs , , and I will just summarise a few thoughts on why I think what they are building matters.
– So far the internet has favoured the creation of monolithic highly scaled platforms. While they compete with each others, there is undeniably a moat in massive tech infrastructure and data aggregation. This is not necessarily a bad thing, but it was until very recently very difficult to think of an alternative model that would support a similar scale without building a monolithic platform. Distributed architectures (while not new) have shown with the emergence of Bitcoin that blockchain technology could support such endeavour.
– Building efficiently organised distributed organisations is often limited by the necessity of building a central platform to coordinate the interactions of their various members. In Financial Services, Mastercard, Visa are examples of such platforms. It is often only available for organisations with enough resources to start building these platforms and enough embedded margin to create their business model.
– These problems are often compounded when an organisation’s goal is to create such a distribution at the user level, for example recognising each user level of participation as experimented in the Assembly model.
In order to start experimenting with building organisations, services that smartly and efficiently support the creation of distributed organisations at various level (between users, between companies, between countries ?), an architecture that allows the distribution of the data and the transparency of the underlying logic will play a fundamental role. This architecture does not prevent third parties to act as an intermediary but changes the way they can operate. By distributing the running cost of the platform to its various participants, they can experiment with new business models that don’t rely on their ownership of the data and the infrastructure.
So what happens next? Eris industries has built a software stack to support the creation of such organisations. As with any early phase, the experimentation that we hope will happen through developers leveraging the platform will be the groundwork for the creation of the services, organisations, companies of the distributed future. I am looking forward to see what they will build.
Insurance Market Watch Mindset

Innovation in the insurance market: QuanTemplate

Its now official, Anthemis has led the latest investment round in QuanTemplate and I could not be more thrilled about it.

QuanTemplate is a marketplace, communications and enterprise workflow technology that offers a secure web-based platform for trading risk, regulatory reporting and creating financial models for use across the (re)insurance market. Through QuanTemplate, underwriters and brokers can conduct all operational activities required to trade in the $4.7trillion insurance market while optimizing their risk in real-time, all the time.

To put it simply, QuanTemplate brings modern data analysis and reporting as well as communication to an industry still mostly relying on Office Tools, emails and hard to use internal systems (in the best case). Through the QuanTemplate platform, the risks of the companies and financial institutions that are driving the world economy are analysed, presented, shared and traded.

Quantemplate screenshot

There are 3 reasons why I believe QuanTemplate has the potential to be one of the leading startups for the insurance industry:

1/ The team. Adrian Rands and Marek Nelken have an incredible experience in the insurance / re-insurance market as well as financial services core technology. Just looking at the product shows they have a distinct vision of the needs of the industry and their ambition for the platform is very large. It is not often you find a team both able to speak to business customers and  build its own frameworks (See Mafic and X-Stitch)

2 / The market. Insurance in general is a market ripe for disruption. The Insurance industry has been so far largely insulated from the disruptive forces that have started to impact the other financial services industry. This is in my view largely due to the moat provided by the historical data insurance is based on, as well as regulatory barriers. For the most part, the B2B insurance market is run on Excel, Word and emails. Quantemplate focuses on the current issues and needs of the industry but builds for its more liquid future.

3/ The vision.The digitisation of the B2B insurance market could have a major impact on the industry. Better data management and modelling approach will affect the economics of the business as well as the capacity to measure risk holistically. A more standardised platform could increase liquidity in the market by allowing new players looking to diversify their investment opportunities to enter.

QuanTemplate’s reception within key players of the industry has been very positive and I am looking forward to working with the QuanTemplate team on an exciting journey.

Investment Market Watch Mindset

Innovation in the Asset Management industry

While other sectors attract more mainstream press attention (payment, retail banking), the asset management industry is also deeply affected by “software eating the world”.

Asset Class Competition
Traditional asset management companies are increasingly facing competition on both sides of the performance scale. On the “Beta” end of the scale, ETFs are steadily becoming one of the most successful innovation in recent decades. By automatically tracking indexes, they offer investors the possibility to obtain market performance at a lower cost than a typical managed funds.


Additionally, more players are appearing making ETFs investment easier and more meaningful for private investors. Companies such as Betterment (disclosure, Anthemis is an investor & I am a customer) make investing in a diversified portfolio of ETFs easy and provide additional services such as automated rebalancing and tax optimisation.

On the other end of the scale (Alpha), access to alternative assets has become easier with the JOBS act. By lifting the ban on general solicitation and making crowdfunding easier, the JOBS act is opening access (for accredited investors for now) to the Venture Capital and Hedge Funds asset classes. Platforms such as Angelist, Seedinvest or Fundersclub (with differences in each models) are making startup investment easier to more people and are solving key pain points such as keeping cap table reasonable. In the hedge fund world, companies like Artivest are experimenting with opening up access to established funds to more accredited investors.

Other assets classes are also opening up and proving competitive. Lendingclub has provided attractive returns to individual investors who would never have had the possibility to directly invest in consumer lending with the necessary diversification. Realtymogul opens up direct access to large real estate investments.

Note: with companies seemingly taking longer to become listed and receiving more later stage investments (notably via secondary deals through platform such as SecondMarket), how much of their increase in value is extracted before they reach the listed markets? 

Scrutiny on Performance

Morningstar has played an important role in making the fund management industry more transparent and it is no surprise to see it as an active investors in the financial services transparency field. MorningStar recently acquired HelloWallet for $52.5M and previously acquired ByAllAccounts for $28M (all transactions in 2014!)

While generic performance comparison has been more open for some time, how it applies to each person’s investments universe is a more recent trends. Companies such as Billguard are already providing antivirus-like services for your personal accounts. FeeX aims to do the same with managed portfolio fees. Looking beyond fees, startups such as Riskalyze can help identify the risk profile of each client and rate their current investments accordingly. Platforms such as Blueleaf (an Anthemis investment) drill down to each funds underlying assets to verify detailed exposure across a customer’s multiple investments. Both Riskalyze and Blueleaf are advisors focused, empowering financial advisors to provide more transparent performance to their customers.

Broken distribution?

The distribution of financial services is facing a major shift over the next years. As showed by Brett King, the traditional brick and mortars infrastructure is fading away


Number of visits per month / year

Younger generation are less and less engaged with the physical distribution of financial services. In growing urban areas they are also less likely to engage in transactions that require branch interactions (mortgage etc). This leaves an opportunity for startups such as Betterment or Wealthfront to fill the void left by banks and traditional players. It is limiting to define these businesses as just online, their capacity to craft a digital experience in line with the expectations of the younger generations is unmatched by traditional financial services players.

Additionally we are seeing an evolution in investment behaviours of Millenials that potentially in conflict with traditional asset management: 

Affluent millennials hold 52 percent of their money in cash and 28 percent in stocks, compared with 23 percent and 46 percent for older people, a UBS survey released in the first quarter found. The study focused on 21- to 29-year-olds with $75,000 in income or $50,000 in investable cash, and 30- to 36-year-olds with $100,000 in income or assets.

as the Blooomberg article explains: 

“We call them Recession Babies,” said William Finnegan, a senior managing director at MFS Investment Management in Boston, drawing a parallel to “Depression Babies” who avoided banks and investing after the 1929 crash. “If the cumulative return of the past five years didn’t convince you that the stock market might be an OK place to be for a long-term investor, I’m not sure what else is going to. These folks have been scarred.”

I don’t think it is right to think Millenials are just risk averse. After all we are talking about people investing in crowdfunding platform such as Kickstarter and a generation expected to have shorter job tenures than previously. We may see an overall shift of having both a highly conservative and highly speculative risk profile, with little left in the middle. 

However the Financial Advisors industry seems to be mostly focused on the current high value client base, mainly retirement focused. How many financial services company will follow Merril Lynch and name a director of financial gerontology?

Commoditization of Analysis

One of less talked about fundamental change on the analyst industry is the influence of the XBRL format (I wrote a first post on this topic in 2010: still valid imv: With the SEC making it mandatory for companies to report their accounts in machine readable format (include notes), performing core financial analysis and building baseline models will become a commodity. Trefis is a good example of how machine readable financial information can make model building different.


Additionally, with the improvement in distributed computing and AI, new models appears that are removing the pain from complex analysis. Companies such as Kensho with Warren makes complex correlation analysis a breeze (one of the most impressive demos I have seen recently). Signals are also becoming “free” with companies such as Estimize providing unique insights into Buy Side analysts expectation of stock performance, beating Wall Street analysts 69.5% of the time (now extending to economic forecasts and M&A deals).

In this new environment, how will asset managers differentiate and create above average returns? When more and more data becomes available, is information asymmetry gone as a differentiator? Is AmPro competition a growing threat? “Older” companies such as Covestor have been created on that basis and new competitors such as Motif also offer the opportunity for anyone to pick stocks and pitch their investment ideas. One of the main drivers of the new coming competition is in my view the lower transactions costs, new players coming out of the Robinhood (a zero fee broker) trading API will be interesting to follow.