Rise of new Fintech business models – Emerging economies go lifestyle

Last week, I was interviewing a VC based out of Pakistan. I took away several insights from the conversation, however, there was one major highlight that would stay with me for a long time. There was a point where the distinction between Fintech businesses and business models was made.

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That distinction was further enriched when we discussed how many emerging economies created Fintech business models embedded within lifestyle businesses. At that point the realisation hit me – the realisation that emerging markets has seen more lifestyle businesses provide Fintech as a byproduct business service. However, in the UK, Europe and the US, we see several businesses explicitly tagged as Fintechs providing core Fintech services.

Therefore, I thought it would be good to write about lifestyle/non-Fintech businesses across the world, that are offering or looking to offer Fintech services. Let me start with Pakistan.

Pakistan rides on Bykea: Bykea started as a ride hailing app, moved to food delivery, and in due course has now started offering financial services to many of its clients.
As they are able to track a customer’s financial transactions they are able to assess if they can offer micro credit to them. Bykea upgrade partner drivers from a cash economy to creating their first ever bank accounts. First time customers can use cash for a booking, however, their model encourages cash top ups to an in-app wallet.

Indonesia Go-Jek and South East Asia’s Grab: Indonesia’s road traffic challenges seem to be alleviated, thanks to motorbike Uber models Go-Jek and Grab. Go-Jek has been around since 2011, and it is now as much a payment app as it is a ride hailing app. They have 1 million drivers, 125,000 merchants, and 30,000 other services, spread across 50 cities in Indonesia. Tencent and KKR are investors in Go-Jek.

Grab has presence widely across South East Asia, operating using the same model. Softbank Group and Microsoft are investors in Grab.

Africa – Energy, Farming and Fintech: In Africa, the lifestyle use cases in focus are in energy and agriculture. As solutions for both these value chains emerge, they often come with a financial inclusion business model integrated. Although, I can’t name them – recently I came across a firm, who provided Solar based last mile charging and other energy services to African villages. The payment for these services could be through M-Pesa or a mobile Wallet.

The other model followed by firms such as Banqu, Binkabi and Agriledger is to use Blockchain technology to track farmers’ transactions. As more and more transactions are registered, the farmer creates a economic identity, which can be used to check their credit worthiness by suppliers and financial service providers. These solutions can also provide wallets for these farmers to enable friction free international transactions.

China’s Leapfrogs: I have got addicted to talking/writing about Alipay and Wechat. While Alipay took over Fintech from an ecommerce base, WeChat began their conquest from a chat messenger business. The impact they have had within China, complemented by China’s drive towards AI and Blockchain has helped the nation’s credibility. The world can no longer perceive China as just a manufacturer of cheap goods. Thanks to their success, the data created from their ecosystem, can be used to create innovative business models.

India Telecoms and Fintech: Fintech in India cannot start or end without the talk of PayTM – the firm that won investments from Softbank, Alibaba group and Warren Buffett’s Berkshire Hathaway. However, several Telecoms providers in India have taken inspiration and started providing Fintech services. Airtel, one of the top telecoms provider in India offers a wallet and even a bank account.

Google launched their Tez app a couple of years ago, and has recently rebranded it to Google pay. They have seen good success. The other name that we can’t miss is Whatsapp – who have been trialling payments in India. At the moment, they are unable to go live with the feature due to regulatory pressures to store the data locally in India. However, the gist is that different players are providing financial services as an add-on business model.

Nordics and their Wrong-un: A wrong-un in cricketing terms, also known as a googly, is when a leg spinner (bowler) suddenly makes the ball spin the wrong way. The Nordics have led the world in creating cashless societies. However, more recently its the banks that are leading financial inclusion business models through federated economic Id creation.

Norway integrated the government Id to taxes and student loans, and that helped the e-Id concept take off. Today, Bank Id in Norway has 74% penetration, in Sweden it has 78% penetration, in Denmark NemID has 85% penetration and in Finland TUPAS has 87% penetration.

Once these bank Ids became mainstream, thanks to collaborative consortium based approach from banks, new non financial services business models were created on top of that. There were life style use cases that could be possible, thanks to the economic Id boom.

It’s interesting to see how in more developed ecosystems, banks are driving lifestyle business models, and it is actually vice versa in emerging markets. Thanks to technology, one thing that’s common between the two is the customer focus. As long as that remains, even the wrong-uns can yield the right results.


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

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

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

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

Artificial intelligence, machine learning, data analysis,
ecosystem insurance purchases, online claim handling, application-based insurance
policies, claim handling in seconds, and so on. 
There’s even instant parametric travel cover that reimburses costs-
immediately- when one’s planned air flight is delayed.  There are clever new risk assessment tools
that are derived from black box algorithms, but you know what?  Those risk data are better than the industry
has ever had!  Super insurance, InsurTech
heroes!  But ask many insureds or claim
handlers, and they’ll tell you all about InsurTech’s weakness, the kryptonite
for insurance innovation’s superheroes (I don’t mean Insurance Nerd Tony Cañas)- those being-   long-tailed or unique claims.

If insurance was easy you wouldn’t be reading this.  That is simple; much of insurance is
not.  Determining risk profiles for
thefts of bicycles in a metro area- easy. 
Same for auto/motor collision frequency/severity, water leaks, loss of
use amounts, cost of chest x-rays, roof replacement costs, and burial costs in most
jurisdictions.  Really great fodder for
clever adherents of InsurTech- high frequency, low cost cover and claims.  Even more complex risks are becoming easier
to assess, underwrite and price due to the huge volume of available data
points, and the burgeoning volume of analysis tools.  I just read today that a clever group of UK-based
InsurTech folks have found success providing comprehensive risk analysis
profiles to some large insurance companies-  Cytora
that continues to build its presence.  A
firm that didn’t exist until 2014 now is seen as a market leader in risk data
analysis and whose products are helping firms who have been around for a lot
longer than 5 years (XL Catlin, QBE, and Starr Companies)  Seemingly a perfect fit of innovation and
incumbency, leveraging data for efficient operations.  InsurTech.

But ask those who work behind the scenes at the firms, ask
those who manage the claims, serve the customers, and address the many
claim-servicing challenges at the carriers- is it possible that a risk that is
analyzed and underwritten within a few minutes can be a five or more year
undertaking when a claim occurs?  Yes, of
course it is.  The lion’s share of
auto/motor claim severity is not found within the settlement of auto damage, it’s
the bodily injury/casualty part of the claim. 
Direct auto damage assessment is the province of AI; personal injury
protection and liability decisions belong in most part to human interaction.  Sure, the systems within which those actions
are taken can be made efficient, but the decisions and negotiations remain outside
of game theory and machine learning (at least for now).    There have been (and continue to be)
systems utilized by auto carriers in an attempt to make uniform more complex
casualty portions of claims ( see for example Mitchell) but lingering ‘burnt fingers’
from class action suits in the 1980’s and 1990’s make these arms’ length tools trusted
but again, in need of verification.

Property insurance is not immune from the effects of
innovation expectations; there are plenty of tools that have come to the market
in the past few years- drones, risk data aggregators/scorers, and predictive
algorithms that help assess and price risk and recovery.  That’s all good until the huge network of
repair participants become involved, and John and Mary Doe GC prices a rebuild
using their experienced and lump sum pricing tool that does not match the
carrier’s measure to the inch and 19% supporting events adapted component-based
pricing tool.  At that intersection of ideas,
the customer is left as the primary and often frustrated arbiter of the claim
resolution.  Prudent carriers then revert
to analog, human interaction resolution.  Is it possible that a $100K water loss can
explode into a $500K plus mishandled asbestos abatement nightmare?  Yes, it’s very possible.  Will a homeowner’s policy customer in Kent be
disappointed because an emergency services provider that should be available
per a system list is not, and the homeowner is left to fend for himself? The
industry must consider these not as outlier cases, but as reminders that not
all can be predicted, not all data are being considered, and as intellectual
capital exits the insurance world not all claim staff will have the requisite
experience to ensure that which was predicted is what happens.

The best data point analysis cannot fully anticipate how
businesses operate, nor how unpredictable human actions can lead to claims that
have long tails and large expense.  Consider
the recent tragedy in Paris with the fire at the Cathedral of Notre Dame.  Certainly any carriers that may be involved
with contractor coverage have the same concerns as all with the terrible loss,
but they also must have concerns that not only are there potential liability coverage
limits at risk, but unlike cover limits, there will be legal expenses
associated with the claim investigation and defense that will most probably
make the cover limits small in comparison. 
How can data analysis predict that exposure disparity, when every claim
case can be wildly unique?

It seems as underwriting and pricing are under continued
adaptation to AI and improved data analysis it is even more incumbent on companies
(and analysis ‘subcontractors’) to be cognizant of the effects of unique claims’
cycle times and ongoing costs.  In
addition, carriers must continue to work with service providers to recognize
the need for uniform innovation, or at least an agreed common denominator tech
level.

The industry surely will continue to innovate and encourage those InsurTech superheroes who are flying high, analyzing, calculating and selling faster than a speeding bullet.  New methods are critical to the long-term growth needed in the industry and the expectation that previously underserved markets will benefit from the efforts of InsurTech firms.  The innovators cannot forget that there is situational kryptonite in the market that must be anticipated and planned for, including the continuing need for analog methods and analog skills. 

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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).

Fintech for fintech’s sake

When global funding for an industry like fintech reaches $111 (3h 51m) (3h 51m) billion, like it did in 2018, it should come as no surprise that financial businesses are emerging to service fintechs themselves.

As most founders can attest to, accessing the full spectrum of personal banking services without a steady salary, or negotiating access to a business line of credit without a consistent revenue stream, are both difficult. Ironically they are two problems founders themselves often set out to solve for other businesses. Until their products are in market however, they are in the same painful banking boat as everyone else.

Businesses like Brex have now emerged to solve that problem. The fintech, backed by ex-PayPal founders Thiel and Levchin, helps startups get credit cards, without personal guarantees or a traditional approach to credit risk assessment. Instead, the card issuer looks at the quality of the investors and VCs who have backed the company and the amount of money raised.

This week Brex announced a deal with Barclays Investment Bank, raising a $100 (3h 28m) (3h 28m) million debt facility backed by Brex’s corporate charge card receivables.  The funds will be used to scale its offering into different verticals.

And the story could well end there, but it doesn’t, as Brex isn’t only about disrupting credit cards and startup banking. It’s also having a crack at co-working, launching a members’ only lounge, the Oval Room. It’s a weird mash-up between an airline lounge, bar, WeWork and bank branch, and it’s kind of cool.

Meaningful non-tech connections with other humans is what the next generation wants. If Brex can be part of achieving this, and fuel dreams and enable entrepreneurship along the way by oiling the wheels of startup finance, it could be on to a winner.

What Brex I think gets, and what banks and incumbents cannot authentically deliver, is that new experience. There is far too much baggage and business case hurdles that need to be met to stand-up these sorts of alternative offerings inside mainstream financial businesses.

Community is the most powerful vehicle to grow a business, and community in it’s truest and stickiest sense, isn’t found online.

Daily Fintech Advisers provides strategic consulting to organizations with business and investment interests in Fintech. Jessica Ellerm is a thought leader specializing in Small Business and the Gig Economy and is the CEO and Co-Founder of Zuper, a new superannuation startup in Australia.

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

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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).

Blockchain Front Page: Can Crypto Debit Cards turn Bitcoin into Real Money?

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Last week our theme was “SEC reducing signal to noise ratio for ICOs.

Our theme for this week is “Can Crypto Debit Cards turn Bitcoin into Real Money?”

At the end of February, Shift announced it was shutting down its Coinbase debit card. In message to customers it explained that it hopes to relaunch in the future, but for now U.S. residents are left with few crypto debit card options.

A few days ago, Coinbase announced they were partnering with PaySaf, to launch a new Coinbase Card, and fill the gap left by Shift shutdown. Initially, the new card will available only in the UK and let users instantly convert their crypto balance to fiat.

A hundred years ago, no one could even imagine that it would be possible to buy things using a plastic card, instead of paper banknotes or coins. Now, we can’t even imagine our lives without debit or credit cards. To go one step further, now we are even funding our debit cards with cryptocurrencies.

Most Americans don’t carry cash. In an average week, roughly 3 in 10 adults make zero purchases using cash. Those who do carry paper money, have less than $50 in their wallets.

Cash isn’t king anymore, but neither is Bitcoin… at least not yet.

Using cryptocurrencies in the real world is difficult. The biggest challenge that  cryptocurrencies face is how to integrate into the real world. There are not many merchants that will accept cryptocurrencies, and even though technologies like Lightning Network will change this, today using your Bitcoin to pay for products and services is hard. We’ve seen some progress, but we’re still a long way to go before cryptocurrencies on par with fiat currencies. For now, the biggest obstacle for the mass adoption of cryptocurrencies, is that people don’t consider them as real money, because they can’t spend them anywhere, anytime.

Could crypto debit cards change this perception?

Crypto debit cards were hot in the first half of 2017. Their value proposition was simple: users wanted to spend their cryptocurrencies to buy things in the real world, but merchants didn’t want to accept them. Crypto debit card companies built platforms, that let users automatically use their cryptocurrencies, when they swiped their debit cards.  The only difference was that the necessary funds for the transaction, were withdrawn from a cryptocurrency wallet and converted into fiat.

When Visa cut WaveCrest, the main issuer for most crypto cards, nearly all them stopped working. Many, like TenX and WireX, suspended their services. Others worked, but only in a limited number of countries and currencies.

Bitcoin’s Lightning Network is already comparable with Apple Pay. Bitcoin’s payment system is superior to the conventional international payments and wire transfers. Technical improvements to Bitcoin’s network are almost certain to make it the world’s main payment system.

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Datalight conducted a fairly large-scale study, which was entirely devoted to the prospects of Bitcoin to become a global payment system. They believe that the Bitcoin payment network will surpass the current giants Visa and MasterCard in the next 10 years.

Potentially, crypto debit card companies are an endangered species. Due to the complexities of operating a crypto-fiat business, regulatory requirements, the changing stance of Visa and Mastercard, and Bitcoin’s Lightning Network, crypto card companies could have a hard time in the future.

Even though crypto debit cards have been the best solution for crypto-to-fiat spending so far, it’s not an ideal one. Anonymity has always been one of the most important Bitcoin features and absolute privacy is simply impossible here.

For now, they are an important stepping stone. There is no doubt that merging crypto with debit cards is a powerful driver for mainstream adoption. Crypto cards help legitimize cryptocurrency, since they work just like a Visa or Mastercard, that most of us have and use everyday.

For cryptocurrencies to make the leap from an traded asset to valid real-world currency and payment method, we need to think of it as real money, money we can use to pay for things. Crypto debit cards can help bridge this gap.

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Ilias Louis Hatzis is the Founder & CEO at Mercato Blockchain Corporation AG. He writes the Blockchain Weekly Front Page each Monday.

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).

 

Entrepreneurs who use Utility Tokens to reduce CAC (Customer Acquisition Cost) will create the most valuable Security Tokens

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TLDR. The big coming wave is Security Tokens, but the backlash against Utility Tokens is overdone. On Monday, Daily Fintech analysed the recent moves by the SEC to provide regulatory certainty to Utility Tokens. This is a big deal. Until now, entrepreneurs faced a regulatory environment where everything was a security, because in that world there was nothing else. Now entrepreneurs can use a Security Token to raise capital and a Utility Token to reduce their Customer Acquisition Cost (CAC).This is a a big deal for entrepreneurs. It is only exciting for investors who have equity in the ventures created by those entrepreneurs. That is how it should be. A Utility Token is a great business building tool; it is not primarily an asset. If you buy a Utility Token, it may appreciate in value, but you buy it in order to use it and any price appreciation is a side benefit.

This post is an update to the chapter on Investing in Utility Tokens in The Blockchain Economy digital book.

This post describes:

  • The SEC rules governing Utility Tokens.
  • Laws change over time and vary by jurisdiction.
  • Four reasons why other jurisdictions will probably follow the SEC rules.
  • Utility Tokens can be used to improve CAC/LTV, which is a critical metric driving valuation. 
  • Invest in Security Tokens of ventures that offer great Utility Tokens.
  • Two ways that a Utility Token is different from a traditional crowdsale.
  • The future cryptocurrency landscape will have 4 different types of assets.

The SEC rules governing Utility Tokens

The SEC rules were analysed in Ilias Louis Hatzis’s Daily Fintech post on Monday.  For convenience the key rules (defined in a No Action letter for the Utility Token of a company called TJK) are copied below:

  • “Token holders won’t be granted an ownership stake in the company.
  • Any funds raised from the token sale will not be used develop the platform or app.
  • When the tokens are sold,  they must be usable immediately for their intended functionality.
  • Transfers of the TKJ tokens are restricted only to TKJ wallets. External wallets are not allowed.
  • TKJ tokens will be priced at 1 USD per token. Each token will essentially function as a pre-paid coupon for TurnKey’s air charter services. If TurnKey wants to buy back the token (coupon), it must do so at a discount (less than 1 USD).
  • The token must be marketed in a way that emphasizes its functionality, and not its potential to increase in value, over time.”

Laws change over time and vary by jurisdiction

The Legacy Finance world has Debt and Equity. The Blockchain Economy has Utility and Security Tokens. You can tokenise Debt (just like you can tokenise Equity or any other asset) but that does not change the fundamental characteristic of that asset.

Debt is illegal in Islamic Finance (for more please read this). There are workarounds that dress up debt to look like equity, just like there are workarounds that ICOs used to dress up a security to make it look like a utility token. This perspective is useful when you look at the legality of Security vs Utility tokens ie laws change over time and vary by jurisdiction.

Four reasons why other jurisdictions will probably follow the SEC rules

Yes, the SEC only has jurisdiction over one market – America, but here are the four reasons why other jurisdictions will probably follow the SEC rules:

  1. America is still the biggest single market.
  2. SEC is known as a tough regulator that is not afraid to take cross border action.
  3. SEC has defined some clear rules. So entrepreneurs can plan around these rules.
  4. There is no single regulatory market in Asia, which is the growth engine of the 21st century.

There will be minor markets that differentiate by being easier on Utility Tokens, but unless they also offer a large investor pool, that will be “noise on the line”. Europe’s legislation/regulation will be interesting to watch. Unless Europe takes a differentiated position soon, the market will follow the SEC rules.

Utility Tokens can be used to improve CAC/LTV, which is a critical metric driving valuation 

CAC/LTV = Customer Acquisition Cost/Life Time Value.

You can use this to evaluate the value of both Banks and Fintechs, as we described in this post from 2015. In fact just about any company can be evaluated using CAC/LTV.

Both CAC and LTV are complex in their own right, but it is the interaction between the two that is so often confusing or difficult.

Customer Acquisition Cost (CAC) is the metric to evaluate Marketing efficiency.

Churn is the kryptonite of Superman Marketing. The problem with Churn it is not directly under the control of Marketing. This is where Product is key. Another way of saying Churn is “if customers think the product sucks, all that expensive Marketing is wasted”. Churn means customers cancel the service and then Marketing have to win new customers, which is far more expensive than retaining them.

Life Time Value is not static. LTV is all about getting the balance right between cross selling, upselling and low churn – too much selling to customers may increase churn. If LTV goes down, you have to reduce CAC. Product strategy, pricing, marketing, customer service all have to be in alignment.

The story of Banking in the 20th century can be summed up as Low Churn. We are statistically more likely to get divorced than change banks. There was no point in changing Banks, because the difference between banks was marginal. The Fintech disruption changes that. Now customers have more real choice and regulation is seeking to protect consumers from lock-in strategies that make it hard for them to switch.

Crowdsales are a great way for companies to sell a service aka reduce CAC. It is Internet Marketing 101. Crowdsales have been around for a while, but Utility Tokens enable Crowdsales on steroids.

Two ways that a Utility Token is different from a traditional crowdsale.

  • The buyer has the comfort that if they no longer want to use the service they can sell their Utility Tokens. If everybody wants to sell their Utility Tokens because their service is no good, token holders will lose. If the service is great but the token holder’s  life situation changes they can sell their Utility Tokens.

 

  • The buyer feels more committed to the success of the venture. Some of that commitment is psychological and some of it is quite practical. A Utility Token is like a Loyalty Coin (more than it is like a Security) but it is a Loyalty Coin with some fungibility (you can sell it for cash if the venture/service is a success and demand exceeds supply).

Invest in Security Tokens of ventures that offer great Utility Tokens.

If a venture offers a Utility Token that is successful in the market, that venture is likely to have good CAC/LTV metrics which eventually translates into equity value held in Security Tokens. I say “eventually” because market mismatching can last a long time ie price does not always equal value or vice versa.

The future cryptocurrency landscape will have 4 different types of assets

An Altcoin Pump & Dump is about cornering a very small market. Cornering a big market – like say Gold or Bitcoin – requires a lot of capital. You can corner an Altcoin quite cheaply and then pump & dump your way to fortune. If you are trading Altcoins and not pumping & dumping, then you are the sucker at the table. This is Penny Stocks 2.0 – watch Wolf of Wall Street for an entertaining guide to this sort of market.

So there is good reason why the SEC clamped down hard. Most Altcoins should be regulated into the dust. 

The fact that Bitcoin & Ethereum got a get out of jail free card from the SEC, puts them in much stronger position versus their challengers. The future cryptocurrency landscape will have 4 different types of assets:

  • Decentralised, permissionless cryptocurrencies with market traction and a free pass from regulators – Bitcoin & Ethereum today. 
  • Challengers to above (maybe there is a pony in there).
  • A large number of early stage ventures listing as Security Tokens where the standard rules of early stage ventures apply (market, product, team, funding, timing, valuation etc).
  • Utility Tokens issued by those early stage ventures that have little value other than to users of that service. There are likely to a very large number of these. 

Bernard Lunn is a Fintech deal-maker, investor, entrepreneur and advisor. He is CEO of Daily Fintech and author of The Blockchain Economy.

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).

Unravelling the Unicorn Madness – as the Silicon Valley bug bites London

A Unicorn is a tech startup that has grown past $1 Billion in valuation. The term “Unicorn” to refer to these firms was first coined by Aileen Lee, a Silicon Valley investor, in 2013. Since then the count of Unicorns has increased to about 300 at the start of the year. Silicon Valley has boasted 9 of the 29 Fintech Unicorns across the world.

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This week, the news on the streets is that London would go past Silicon Valley in the Fintech Unicorns tally. London already has 7, and there are a good few companies in the pipeline raising funding to get past Silicon Valley’s 9. Let us look at the irrational exuberance of the London Fintech market and the funding it received.

London received 39% of European Venture Capital funding. The revenues of Fintech firms in London increased from $100 Million to about $230 Million in the last 12 months. Fintech in London is also the fastest growing job sector. Monzo and Tandem got headlines earlier this week due to their new funding rounds. Monzo is receiving capital from Y Combinator and a few other Silicon Valley investors, and Tandem has closed an £80 Million funding round.

However, this is just how growth has manifested itself. There are some fundamental changes to the Venture capital mindset that has caused this Unicorn madness. There are abundant sources of funding these days. The number of platforms that a tech startup can leverage to get funding is increasing on daily basis.

Incubator and accelerator programs inspired by the successes of Y Combinator, Seedcamp etc., are numerous. There are several entrepreneurs who have exited and started to give back to budding start ups as Angels. This used to be the case in Silicon Valley, and London’s entrepreneurs are no different. Over the last 12 months, I have come across atleast 20 firms that have received angel funding from founders of more established or exited tech firms.

Family Offices and even Pension funds these days make direct investments into the tech startup world. Many of them shy away from traditional Venture capital model due to the fees involved.

That has increased the flow of capital directly into private tech firms. Also, the size of late stage funds like Softbank’s fund, and Sequioa’s $8 Billion fund means, firms are adequately funded at a later stage too.

If all these options weren’t enough, in the UK, we have the EIS/SEIS schemes that offer very attractive tax benefits for investors into tech startups. Most HNIs and UHNIs are keen to ensure they utilize these tax schemes. Crowdfunding platforms help, and more recently, the ICO and STO methods of raising capital globally have had their effect as well.

Apart from these financing options, the monopoly that some of the Silicon Valley start ups have taken in their markets, is now used as a model of growth. Once the product market fit is identified, firms these days throw money at growth – crazy growth. This results in market dominance, and that itself becomes the barrier to entry for competitors.

Gone are the days where technology, business models, and even operational excellence differentiated the great from the good.

This growth often means, firms have no respect for operational excellence, or very little intent on achieving a viable business model. They only focus on growing fast, raising more at higher valuations and achieving a Unicorn status. Even VCs these days are judged based on the Unicorns in their portfolios.

This growth at any cost and irrational valuation models had caused the dot com bubble to burst about 20 years ago. And this is definitely not another “the recession is coming” post. But it is important to understand that Unicorn status doesn’t mean much anymore. For an early stage angel investor, an increase in valuation from say $2 Million pounds (when they invest) to when the firm hits $1 Billion in valuation, makes a big difference. But in the broad scheme of things, this is just an artificially created tag often used for branding.

Investors and firms riding this wave of irrational exuberance need to time their exit right. If the correction blindsides them, it may be another financial crisis. It’s sad that London’s Fintech has gone down this path that Silicon Valley firms have traveled for years. It’s superficial and doesn’t feel right.


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

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

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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).

SME lender financial engineers should look to Africa for inspiration

Credit models can be the live or die, make or break moment for a start-up lender.

Get one assumption wrong (or a number of them), and suddenly you have a serious arrears problem. One that can tip you into a death spiral, no matter what size your book is.

It’s something many fintech business lenders, despite the jazzy websites, and flash looking marketing, don’t implement well, from an infrastructure perspective. Instead many simply base their pricing on market forces. Of course, not many would tell you that to your face. Or their investors, for that matter.

Financial engineers are the sought after holy grail hire for a fintech lending startup. Not to mention founders than understand the importance of them. And while many of these engineers in the western world know their way around a balance sheet and P&L blindfolded, they would struggle in other markets, where the credit indicators of a business are significantly different. For someone with global ambitions, this local level of credit decision nuances makes this a serious challenge.

This week African lender Branch International raised $170 (5h 54m) million from big name funders Foundation Capital, Visa, B Capital, Andreessen Horowitz, Formation 8 and Trinity Ventures.

$100 (3h 28m)M goes to finance the growing book, and $70 (2h 26m)M is equity.

What makes Branch International interesting is something that makes all developing economy lenders interesting – their approach to assessing risk via what we in the west would consider ‘non traditional’ means. That is smartphone data, text messages, GPS information, who you call and who’s in your contact list, plus many more. It’s all a bit Black Mirror, but potentially significantly more powerful and insightful than any other rudimentary financial data point, like a consumer credit score. If it increases access to credit, surely that’s a good thing?

These data points don’t translate as elegantly into small business, but there is surely some room for experimentation here. Assessing credit risk in SME land is infinitely difficult, and continues to make it a risky play for new entrants, and a costly one for borrowers.

The financial engineers of the future, and founders looking for an edge should be closely watching this space with interest.

Daily Fintech Advisers provides strategic consulting to organizations with business and investment interests in Fintech. Jessica Ellerm is a thought leader specializing in Small Business and the Gig Economy and is the CEO and Co-Founder of Zuper, a new superannuation startup in Australia.

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)

The `robos` in the front-office – takeaways from Swiss innovators


Watch the gap; between the Attention Economy and financial services.

 Market forces are fiercely at work to start closing this gap. I shared insights around this reality and ways that financial services players can participate successfully in this transformation, during my opening talk at the annual event by the Bank Innovation Competence Center[1], at Unil, HEC Lausanne. I also listened to different perspectives regarding `Les robots au front-office`.

Actual experiences and learnings from:

  • A Swiss private bank with a global outreach – Julius Bear
  • A Swiss online Bank with an in-house robo solution – Swissquote
  • A Swiss cantonal bank innovating through Fintech collaborations – BLKB/True Wealth B2B
  • A Swiss bank using chatbots – PostFinance/ELCA.

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Julius Baer, a 10yrold pure wealth manager[2], has already deployed a Digital Investment Advisory suite – called DIAS – in their Luxembourg operations and is in the process of adopting it in its home base. This is a technology stack deployed to empower the Relationships managers of JB so that they can focus on relationships, offer customized insights and reduce the burden of the ever-growing regulatory requirements.

Undoubtedly, the unbundling of financial services that have been instigated by standalone Fintechs, has essentially commoditized several aspects of financial services. Wrapping value-added advice around products and transactions is inevitable and that is what JB is aiming at. For now, and from JB`s experience, there has been no JB customer that has left from private banking to go to a standalone robo-advisor.

 Swissquote, the Swiss tech born online bank, has developed its own `robo` offering. Their heavily quantitative approach is well known from the suit of their financial products and services. An online automatic but discretionary approach to investing was a very natural extension of their successful e-trading business. One of their first learnings was that personalization is needed for the automation process. Through a close collaboration with Neuroprofiler, a Swiss-born behavioral finance risk profiler out of the Kickstart accelerator, they offer a dynamic automated risk profiling with gamification elements[3].

Swissquote`s robo is used by some of their end clients but also by asset managers and financial advisors that use the Swissquote technology. The two main learnings are that one of the main in-house uses of their robo capabilities, is from existing customers that leave cash in their accounts without doing anything. Think, for example, a customer using the Swissquote e-trading platform that has often cash that is not at work.

Evidence that robo-advisors can be of value-add to customers that leave cash sitting in their accounts due to inertia.

Swissquote is continuously improving their offering by experimenting with Big Data and AI that can enrich the interaction with customers beyond and in addition to their dynamic risk profiler. Think of an algorithm that sends an sms asking `Dear Efi, ahead of BREXIT, would you want to consider switching off the robo algorithm?`.

Digifolio, is the BLBK robo advisory offering powered by the B2B technology of True Wealth, a Swiss robo that also runs its own B2C offering. BLKB is the most innovative Swiss cantonal bank with an early online mortgage offering and a digital earthquake insurance offering. Digifolio was launched in the summer of 2017 (with a minimum requirement of 5k CHF). One of their main learnings up to now, is also that success can be clearly attributed to the effectiveness of Digifolio to move existing customers from cash in their account, into investing.

As early as 2015, I had introduced the concept of `Unadvised assets`[4] and since, have been looking at ways that Fintechs can `nudge` and grab the piles of cash.

3yrs ago, my 2min view

Digital Wealth management: a videographic update, March 2016

In addition to robos, Oh, the things you could do with the enormous Cash pile! November 2016, in which I looked at `competing` unbundled Fintech offerings.

Thanks to the market feedback shared at the BAICC event, I will be updating the `Unadvised Assets` perspective to check if there has been any noticeable impact on the cash pile possibly from rise automated investing offerings at the B2C and B2B2C level.

PostFinance, a Swiss financial service provider that has been investing in Fintechs for a while, has launched a text chatbot in collaboration with ELCA, a Swiss IT company. Postfinance is the first Swiss bank launching a customer-facing chatbot on its website. This is part of their business goal to offer 24/7 service with no queuing (as in the case of live online chatting with an `agent`) as one of the advantages of text chatbots is the simultaneous handling of requests.

The global chatbot market is expected to reach $1.23 billion by 2025according to a recent report by Grand View Research[5]. The challenges however to adopting chatbot technologies are not negligible. As ELCA explained, there needs to be a clearly defined business goal before designing a suitable chatbot, that of course, needs to be trained with the relevant content. Add to this, the complexity in the chatbot market because of the incompatible between text chatbot interfaces and voice user interfaces. In simple words, the language used and the content for training text chatbots is very different from that of voice chatbots. For example, in text chatbots often answers are provided in the form of links, which cannot work in voice chatbots.

Tech integration is always more complicated than it seems at the surface. Both because of legacy system integrations but also because experimentation maybe needed until the suitable product fit is determined in each use case. Pictet has been using chatbot technology internally, to modernize communication between the front office and compliance. This is a functionality that is also built behind the scenes of the JB DIAS system too.

[1] Agenda BAICC – EE – Seminar Robotics in FS – Agenda f (master). http://www.baicc.news/a-propos-baicc/

[2] JB separated in 2009 from its asset management business.

[3] Neurprofiler is a MiFIDII-compliant customer risk profiler for Financial Advisors.

[4] Salivating for Unadvised assets: a videographic, Nov. 2015

[5] https://www.techradar.com/news/support-agents-versus-conversational-chatbots

Book one hour with Efi – Ask me anything (AMA) for 0.10BTC – Efi@dailyfintech.com

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).

Unadvised Assets ’16