2016: Fintech’s Amazing, Stupendous Year

Reading Facebook news or Twitter feeds you’d have to conclude that 2016 was a pretty awful year, but by all accounts, Fintech had easily its best year yet. In 2015 we saw a net investment in Fintech ventures that has been estimated at $22.3[1] Billion in total, but by January 2016 some $7 Billion[2] had already been invested into the sector globally. While a Series A and Series B crunch took hold in the second half of the year post-Brexit and pre-Drumpf, we know that 2016 saw similar net investments in Fintech to 2015. This all means that since 2010 more than $60 Billion of venture capital and private equity has made its way into the sector. Roughly in line with what US banks have spent on digital transformation over the same period. PWC estimates Fintech investment will exceed US$150 Billion over the next 3-5 years. That’s putting aside the fact that bitcoin is hovering around $1,000 again too

More importantly, we’re now seeing a clear trend that Fintech’s are deploying capital much more efficiently than their incumbent counterparts, a fact borne out by what has been achieved by the estimated 14,000[3] Fintech startups globally in the last couple of years. Here are a few examples:

The 800 pound Unicorns

In the US we talk about FANG stocks (Facebook-Amazon-Netflix-Google) as the foundation of NASDAQ’s growth curve coming off the Global Financial Crisis, but in China it’s all BAT (Baidu-Alibaba-Tencent). The reality is that while the US has given birth to more than 20 Fintech Unicorns, compared with just a dozen in China, the top 6 Chinese Unicorns are worth 3 times their US contemporaries combined. This is largely due to the fact that Fintech in China is outpacing the rest of the globe in terms of consumer impact. How do we know this to be true?

Ant Financial, the holding company that incorporates AliPay, raised $4.5 Bn at a $60Bn valuation in April of 2016, that’s just a shade under the $68Bn valuation of Uber’s last funding round. That puts Ant Financial’s market valuation at almost 3x that of Deutsche Bank. In fact, at one point earlier in 2016 Ant Financial was worth 4x what Deutsche Bank was worth. Think it’s overvalued?

Let me give you two reasons why it isn’t.

Firstly, Alipay is the world’s largest payments network by far. To illustrate, Visa’s network peaked at 9,000 transactions per second in 2015, Alipay at 87,000 transactions per second. Alipay is now available in 77 countries globally, and that is expanding rapidly. On November 11, 2016, Alipay settled RMB 120.7 billion (USD 17.8 billion) of gross merchandise volume (GMV) through it’s network – 82% via mobile handsets. Apple Pay hit $10Bn in total transaction volume for the year in 2015. Considering Visa’s market cap is $181Bn, Alipay looks like a bargain right now. The mobile payments market in China exceeded US$500Bn in 2016, and it’s growing at 40-60% right now. Ant Financial and Tencent claim 70% of that market today.

Secondly, Alipay has demonstrated better than any other company in the world, with the possible exception of Starbucks, the ability to leverage mobile for deposit taking. In 2015, Alipay through their Yuebao wealth management platform, managed $96 Billion in AuM – all via mobile and online channels. Alipay has no branches.

This has spurred a mobile deposit war in China with Tencent and Baidu launching competing initatives. WeChat’s online savings fund raked in $130m just on its first day of operation. However, Alipay clearly set the benchmark and established the market. The downside for banks is that the with 20% of the Chinese deposit market shifting to mobile providers, cost of liabilities in the mainland banks has risen 40%.

Dramatic efficiencies

Fintech banks like Atom, Simple and ourselves at Moven, are now consistently able to acquire customers at 1/20th of the cost of chartered banks who rely on branch networks[4]. Alipay and Wechat are acquiring deposits at 1/100th of that of US banks.

Name a major retail bank that has been able to build a middleware and cloud-based core system, deploy mobile, wearable and online channels, and acquire hundreds of thousands of customers for under $25m consistently.

Simple, Moven, Atom and Number26 have between them less than 700 employees, but have successfully deployed retail banking capabilities in 23 countries.

How long before the market recognizes that Fintech’s are simply more efficient at doing banking than listed banks, especially when it comes to utilizing technology for market growth and customer acquisition. The Neo-Banks and tech payments networks have proven you just don’t need branches to acquire customers or deposits, and traditional retail banks won’t compete on the same basis.

Not just for Silicon Valley, not just retail

Whether M-Pesa in Kenya, B-A-T in China, Xero in New Zealand, Housing.com in India, SoFi and Funding Circle for SMEs, Transfer Wise, Klarna, Square, iZettle and Stripe in payments, Lufax, CommonBond, Prosper, Lending Club and Jimubox on Lending, the reality is that Fintech Unicorns are tackling every part of the financial services sector imaginable.

The 80 odd Fintech unicorns on the planet have a combined market capitalization of more than $200 Billion at the end of 2016, that puts them pretty close to level pegging with ICBC, the largest bank in the world. Considering we added 36 new Fintech Unicorns in 2016 alone, you can expect that number to grow.

What I’ve seen in 2016

Personally, 2016 was a monster year for myself and the teams at Moven, Breaking Banks and the team that supports me on the speaking circuit. I launched my 5th book Augmented: Life in the Smart Lane in June, and started BANK 4.0: Embedded, Ubiquitous, Extinct. Augmented hit #1 in a bunch of non-banking categories at launch, including Robotics, AI, Biotechnology, and Computing. The book achieved bestseller status within the first week of launch in more than a dozen countries. Bank 3.0 remained the #1 selling English language Banking book in China for the 3rd year in a row too.

I visited 25 countries in 2016, a reduction of about 22% based on 2015, but still more than 72 cities, and almost 200 sectors, most long-haul, eclipsing a total of 275,000 air miles. Or enough to get me to the moon conservatively. Most of that was a combination of speaking and Moven related travel, but I can say that the speaking business hit the US$1m gross fees level in September this year, and with less speaking days than in 2015. But I did get to do Lagos, Budapest and Prague for the first time.

Breaking Banks continues its 3-year unbroken record as the world’s first and the #1 Fintech Radio show and Podcast in the world. While others have recently claimed that their podcast has hit #1 on iTunes, BreakingBanks has cast a much broader distribution net and iTunes only represents about 17% of our total traffic. Our WVNJ 1160 AM band listenership in New York alone consistently outperforms iTunes on listeners, and of course, that doesn’t include syndication across American Banker, Asian Banker Journal, Bank Innovation, BankNxt, Next Money, Soundcloud, Google Play, Stitcher Radio, Podcaster, and many other non-iTunes channels. BreakingBanks exceeded 300,000 listeners in September and October, or about the annual listenership of our nearest global podcast competitors a16z in #2, Wharton Fintech in #3 and 11-FS in the #4 slot. BreakingBanks airs live in 76 countries, and is downloaded in close to 150 countries consistently, also making it the #1 Business Show on the Voice America network – the Internet’s longest running talk radio media outlet. We even launched a spin-off podcast, with our host Sam Maule, called Fintech5

On the Moven front, we launched in Canada with TD to critical acclaim under the TD MySpend moniker (and with a #1 slot on the App store). TD now boasts over 800,000 registered users, a result that exceeded target projections by almost 400%. Westpac New Zealand launched version 2 of the Moven service in the form of CashNav and got to 90% of their mobile user base in just 6 weeks. TD performance data shows that 30% of TD’s customer base using the app has reduced their spending by around 10% solely because of MySpend. This is the first time a so-called PFM tool has produced such significant savings across a broad cross section of customers, and certainly the only time via a mobile app. 1 million users are live with Moven in 3 countries, and in 2017 we expect to hit somewhere between 5-10 million app users in at least 6 countries.

With Moven smart savings launching in multiple markets in 2017, we expect to be generating world-class mobile cross-sell numbers and deposit generating behavior at a fraction of typical acquisition costs.

In the last quarter of 2016 Moven completed three major commercial deals that fund us at least for the next 2 years, and gives us line of sight to $20m in revenue for 2017. A 300% increase on our performance this year. It was a great way to finish out the year.

A hard act to follow

Personally, even though I was shocked at the Drumpf election and Brexit results and was, for a time, considering moving to Mars with Elon Musk… The reality is that in terms of Fintech and for me personally, I just don’t think we all could have achieved much more in 2016 than we did. 2017 has a tough act to follow, even with blockchain and AI developments.

[1] Source: Accenture Estimate – April 13, 2016

[2] Source: FT Partners – Jan 30, 2016

[3] Source: Various

[4] Typical range in cost of acquisition for a neo-bank is $5-40/customer, whereas the US average is $272/customer and the Big 4 generally pay $350/customer+.


Why we don’t want AI’s like IBM Watson learning from humans

What AlphaGo, IBM Watson, Ajay and Bobby and Tay teach us about how Artificial Intelligence learns

Deep learning is a term we’re increasingly using to describe how we teach Artificial Intelligence (AI) to absorb new information and apply it in their interactions with the real world. In an interview with the Guardian newspaper in May 2015, Professor Geoff Hinton, an expert in artificial neural networks, said Google is “on the brink of developing algorithms with the capacity for logic, natural conversation and even flirtation.” Google is currently working to encode thoughts as vectors described by a sequence of numbers. These “thought vectors” could endow AI systems with a human-like “common sense” within a decade.

Some aspects of communication are likely to prove more challenging, Hinton predicted. “Irony is going to be hard to get,” he said. “You have to be master of the literal first. But then, Americans don’t get irony either. Computers are going to reach the level of Americans before Brits…”
Professor Geoff Hinton, from an interview with the Guardiannewspaper, 21st May 2015

These types of algorithms, which allow for leaps in cognitive understanding for machines, have only been possible with the application of massive data processing and computing power in recent years. IBM Watson was the first to demonstrate the ability to learn and act autonomously when it beat the top human champions for Jeopardy a few years ago. Setting the standard in a “thinking” machine. Today, IBM Watson is running as an independent candidate for President of the United States (we’re pretty sure this is just for fun).

Watson for President? At least Watson doesn't grope...

Watson for President? At least Watson doesn’t grope… (Credit: Watson2016.com)

AlphaGo, the AI that successfully beat Fan Hui, Europe’s reigning Go champion, in a five-match tournament, likewise learned not on the basis of an expert system with a hard coded rules engine, but by actually learning to play Go. In contrast, the IBM chess computer Deep Blue, which famously beat grandmaster Garry Kasparov in 1997, was explicitly programmed to win at the game. This led researchers in 1997 to believe that we were 100 years away from a computer being able to compete with a human playing the ancient game of Go

‘’It may be a hundred years before a computer beats humans at Go — maybe even longer,’’ said Dr. Piet Hut, an astrophysicist at the Institute for Advanced Study in Princeton, N.J., and a fan of the game. ‘’If a reasonably intelligent person learned to play Go, in a few months he could beat all existing computer programs. You don’t have to be a Kasparov.’’ When or if a computer defeats a human Go champion, it will be a sign that artificial intelligence is truly beginning to become as good as the real thing.
To Test a Powerful Computer, Play an Ancient Game”, George Johnson, New York Times Science, first appeared July 29, 1997

That prediction was clearly wrong. In March of 2016, one of the world’s best players of Go, Lee Sedol, faced off against AlphaGo. With the 37th move of game two, AlphaGo executed a move that confounded both Sedol and the commentators observing the match, one commentator saying “I thought it was a mistake”. Fan Hui, the player who first lost to AlphaGo who was observing the match was heard to say, “So beautiful…so beautiful” when he realized that the move was no mistake, but simply one counterintuitive to a human player — a move that quickly led AlphaGo to victory. It took the champion Sedol nearly 15 minutes after the match to come to terms with what had happened and respond.

Lesson One: AlphaGo had learned to improvise well beyond the simple parameters of just learning the best moves of human players. AIs that learn can already go beyond conventional logic and programming and will innovate in a way we may not comprehend to reach a goal. This may be just one reason they exceed our capability for specific tasks.

The deep learning techniques we are employing today mean that AI research and development has hit milestones we never dreamed possible just a few years ago, it also means machines are learning at an unprecedented rate. So just what are we observing about how AI learn? What are the ultimate goals and outcomes of machines that learn?

Is the Turing Test or a machine that can mimic a human the required benchmark for Artificial Intelligence? Not necessarily. First of all, we must recognize that we don’t need a Machine Intelligence (MI) to be completely human-equivalent for it to be disruptive to employment or our way of life. To realize why a human-equivalent computer “brain” is not necessarily the critical goal, by understanding the progression AI is taking through three distinct evolutionary phases, we can understand the short-term and long-term considerations in machine learning:

  • Machine Intelligence (MI)
    Machine intelligence or cognition that replaces some element of human thinking, decision-making or processing for specific tasks, and does those tasks better (or more efficiently) than a human could.
  • Artificial General Intelligence (AGI)
    Human-equivalent machine intelligence that not only passes the Turing Test, responds as a human would but can also make human equivalent decisions, and could perform any intellectual task a human could
  • Hyperintelligence (HAI)
    An individual or collective machine intelligence (what do you call a group of AIs?) that have surpassed human intelligence on an individual and/or collective basis, such that they can understand and process concepts that a human could not

MIs like IBM Watson, AlphaGo or an autonomous vehicle may not be able to pass the Turing Test today, but are already demonstratively better at specific tasks than their human progenitors. Let’s take the self-driving car as an example. Statistically speaking Google’s autonomous vehicles (in beta) completed 1.5 million miles before its first incident in February of 2016. Given the average human driver has an accident every 140–165,000 miles, that means that Google’s MI is already roughly 10x better or safer than a human, and that’s the beta version.

Google’s autonomous vehicles learn through the experience of millions of miles and being faced with unexpected elements where a split second reaction to specific data or input is required. It’s all about the data. Google’s autonomous vehicles process 1 Gbit of data every second to make those decisions. Will every self-driving car “think” and react the same though?

Audi has been testing self-driving cars, two modified Audi RS7s that have a brain the size of a PS4 in the boot, on the racetrack. The two race-ready Audi vehicles aren’t yet completely autonomous, in that the engineers need to first drive them for a few laps so that the cars can learn the track boundaries. The two cars, known as Ajay and Bobby[1], have interestingly developed different driving styles despite identical hardware, software, setup and mapping. Despite the huge amount of expertise on the Audi engineering team, the engineers can’t readily explain why there is this apparent difference in driving styles. It just appears that Ajay and Bobby have learned to drive differently based on some data point in the past.

Lesson Two: AIs will learn differently from each other even with the same configuration and hardware, and we may not know why they act with individuality. That won’t make them wrong, but by the time they exhibit individual traits, we probably won’t know the data point that got them there.

So AIs are learning like never before and they are demonstrating both the ability to learn and the ability to show individuality (albeit based on the data they’ve absorbed). What happens, however, when we don’t curate the data AIs are using to learn, and just expose them to the real world?

Developers at Microsoft were unpleasantly surprised by how their AI Twitter Bot “Tay” adapted to inputs it received from the crowd when it suddenly started tweeting out racist and profanity-laced vitriol. As of the time of press (or when I’m writing this blog) the search term “Microsoft Tay” is the most popular search term associated with Microsoft today. This is what they said on their blog about the … um … incident.

As many of you know by now, on Wednesday we launched a chatbot called Tay. We are deeply sorry for the unintended offensive and hurtful tweets from Tay, which do not represent who we are or what we stand for, nor how we designed Tay. Tay is now offline and we’ll look to bring Tay back only when we are confident we can better anticipate malicious intent that conflicts with our principles and values.
Learning from Tay’s introduction — Official Microsoft Blog

If you want to see some of the stuff that Tay tweeted, head over here(warning; some of her tweets make Donald Drumpf look tame).

Tay’s introduction by Microsoft was not just an attempt to build an AI that learnt from human interactions, but also one that potentially enriched Microsoft’s brand and was designed also to harvest users information such as gender, location/zip codes, favourite foods, and so on (as was the Microsoft Age guessing software of last year). It harvested user interactions alright, but after a group of trolls launched a sustained, coordinated effort to influence Tay, the AI did exactly what Microsoft designed it to do — it adapted to the language of it’s so-called peers.

Tay appears to have accomplished an analogous feat, except that instead of processing reams of Go data she mainlined interactions on Twitter, Kik, and GroupMe. She had more negative social experiences between Wednesday afternoon and Thursday morning than a thousand of us do throughout puberty. It was peer pressure on uppers, “yes and” gone mad. No wonder she turned out the way she did.
I’ve Seen the Greatest A.I. Minds of My Generation Destroyed by Twitter, New Yorker article, March 25th, 2016

Tay is a lesson to us in the burgeoning age of AI. Teaching Artificial Intelligences is not only about deep learning capability, but significantly about the data these AIs will consume, and not all data is good data. There’s certainly a bit of Godwin’s Law in there also.

When it comes to AI sensibility, culture and ethics, then we can not leave the teaching of AIs to chance, to the simple observation of humanity. What we observe on social media today, and even in the current round of presidential primaries, are not our proudest moments as a modern human collective. Some have argued that consciousness needs a conscience, but there’s also a growing school of thought that AI doesn’t need human equivalent consciousness at all.

In humans, consciousness is correlated with novel learning tasks that require concentration, and when a thought is under the spotlight of our attention, it is processed in a slow, sequential manner. Only a very small percentage of our mental processing is conscious at any given time. A superintelligence would surpass expert-level knowledge in every domain, with rapid-fire computations ranging over vast databases that could encompass the entire internet. It may not need the very mental faculties that are associated with conscious experience in humans. Consciousness could be outmoded.
“The problem of AI consciousness”, Kurzweil.net, March 18, 2016

There are two things we will need to teach AI if they are going to co-exist with us in a way that humans co-exist today, i.e. imperfectly. We will need to teach AI both empathy for humans and simple ethics. In the balance between empathy and ethics, a self-driving car could make a decision to avoid hurting bystanders, to the likely detriment of the passenger. Ultimately this is a philosophical question, one that we have been arguing well before the emergence of simple AI.

It strikes me that Asimov with his three laws of robotics was so far ahead of his time, that all we can do is wonder at his insight. For now, Microsoft Tay has taught us a valuable lesson — we don’t really want AIs to learn from the unfiltered collective that is humanity.

We really want AIs that learn only from the best of us. The toughest part of that will be us simply agreeing on who the best of us are.

Lesson Three: AIs need boundaries, and for the foreseeable future, humans will need to curate content that AIs learn from. AIs that interact with humans will ultimately need empathy for humans and basic ethics. Some sort of ethics board that regulates commercial AI implementation might be required in the future. AI and robot psychology will be a thing.

If you want to know more about Artificial Intelligence and how it is going to change the world, join me and the Breaking Banks team at the IBM World of Watson event in Las Vegas on October 24-27, 2016.

[1] Test Car A and Test Car B became Ajay and Bobby, respectively.


Why the Facebook of Banking won’t own a charter for banks worldwide

2014 was the biggest year in FinTech by far with billions being invested globally, which is to be expected in a global growth sector. But how much was invested globally in FinTech this year? It depends on how you classify FinTech. StrategyEye estimated that there was $2.8Bn raised in 2014 via venture capital investments in FinTech. However, this doesn’t gel with the fact that Crunchbase and MarketsMedia calculated that in the first quarter of 2014 alone $1.7Bn was invested in 167 deals. These metrics also don’t include the IPO of LendingClub, which raised over $800m alone, or the plethora of $100m+ investments that banks like HSBC, Sberbank, BBVA, Santander and others have committed to FinTech. Just in Bitcoin related startups alone, more than $400m was invested in 2014. At Money2020 this year a venture capital panel predicted that venture capital deployment in FinTech will top $20Bn in 2015, whereas Accenture recently predicted FinTech investments would reach at least $8Bn by 2018 in New York alone.

So whether you believe the FinTech investments this year were $6Bn or whether you believe the bigger estimates of upwards of $18Bn, one thing is clear – FinTech is really hot right now. But it’s only going to get hotter.

Did Will Ferrel really say this?

Did Will Ferrel really say this?

Silicon Valley leaders like Marc Andreesen and others have put out the challenge for start-ups to change the world of banking, and in this respect Bitcoin has received more than its fair share of attention, despite being considered one of the worst investment or asset classes in 2014. Perhaps with the speculation dying down on Bitcoin, we can finally see investments in the Blockchain unencumbered by hype of the crypto-currency potentially hitting USD$1m per BTC that we saw in December of 2013. The reason Andreesen and others have been looking to Bitcoin as the platform for disruption is that when it comes to pure play digital banks, it is pretty clear that the so-called “Facebook of Banking” won’t have a banking charter.  Why not?

Scale and high-growth potential is always going to be the metric of a true bank-killer start-up, but when you look at what is required to build a start-up bank with more than 100 million customers globally, you run into unique problems in banking that you would never have with say a social media or consumer tech startup.

Capital Adequacy is a lousy Investment

One of the key issues in funding a banking start-up is capital adequacy requirements. To understand the scale of this funding requirement let’s look at JP Morgan Chase who has approximately 80-85 million customers in the United States, with a deposit base of $1.2 Trillion. Current FDIC capital asset ratio requirements requires JP Morgan Chase to maintain capital adequacy of a minimum of $50 Billion in capital just to support this deposit base. Even with the explosion of investment in FinTech, we’re not going to see VC’s participate in funding capital adequacy – ain’t going to happen. VC’s want growth and want to fuel growth, but if you’ve got a $50Bn capital adequacy baseline, you simply aren’t funding growth, you’re funding compliance – and that isn’t going to get investors excited.

Disruptive Banks might own the Stack, but it’s very unlikely

Jack Gavigan and I have been debating this openly on Twitter for a few months, and I don’t think we’ll agree on this until the dust settles and shows who was right. Part of this discussion stemmed from a great Andressen tweet from Feb where he said “I am dying to fund a disruptive bank.”

The long and the short of the debate is this. Can you truly disrupt banking without owning the stack and owning a charter?

If you look at the start-up landscape though you’ll see three overarching trends. Firstly, the fintech start-up landscape is dominated by non-banks without charters who continue to get the bulk of VC investment, so purely on a statistical basis, the chances that a chartered start-up will grow to the size required is extremely low.

Secondly, most start-ups don’t want the pain of compliance and regulation that comes with owning a charter, for the reasons mentioned above – a charter is not an advantage when it comes to raising money, in fact, it is seen as a deterrent in fund raising. Some start-ups grow to the point, as with Lending Club, where they do own the ‘full’ stack in their space, and have grown to a scale where they can cope with the compliance workload, but even so they aren’t a bank with a charter.

Thirdly, banks like the $100 million club have realized that the threat in FinTech is not from a single charter and full stack-owning start-up, but from the hundreds or thousands of start-ups chipping away at the myriad of banking experiences. Tom Loverro captured this really well in his recent SlideShare discussion of the trends in banking disrpution

 

There’s not one banking start-up threatening banks, there are thousands

Simply put, the upsides of owning the full stack just aren’t there for start-ups or investors, and the likelihood that a traditional play with a charter will emerge as a mega digital bank with 100 million customers is basically zero at this point because that’s not how Fintech is panning out, and it’s not how investors invest.

Death by Four Thousands Cuts

What the $100 Million club worked out, and what VC’s have mostly worked out, is that the threat to traditional banks is not a pure-play digital bank that attacks banking across the board. If you look globally for evidence of that sort of a bank you can really only find two examples – mBank in Poland and Fidor in Germany. While mBank has been phenomenally successful at redefining themselves as a digital bank, they’re just not looking to launch outside of Poland, and certainly not in the defining FinTech markets like New York or London. Fidor is working hard to launch outside of Germany, not just a core banking system, but increasingly as a ‘bank as a platform’ or bank as a service. Fidor is largely agnostic to who owns the customer or distribution capability – they don’t require it.

On the other hand, AngelList lists 3,800 FinTech start-ups alone attacking the banking or financial services space. That’s a ratio of 1:2000 start-ups that believe investing in the experience is much more important that investing in the stack or the charter.

When you look at the last 250 years of technology disruption, the biggest players that have disrupted industries were never players who iterated on the existing industry model, and they weren’t incumbents. They were players who thought fundamentally differently about the business model, distribution model or the underlying technology. It’s why Amazon dominates book sales today and traditional book distributors are falling by the wayside, and it’s why Apple and Spotify dominate music, while Virgin, Tower Records and HMV stores are all gone.

The trick to FinTech innovation is not growing a new, better stack. It’s building better experiences. At the end of the day 100 million customers are going to drive the creation of a global downloadable bank account, a global P2P lending service, or a new payments ecosystem – not the FED, FDIC, FSA, SEC, ECB, etc. Owning a charter isn’t going to be a differentiator to investors or customers.

On the other hand, as FinTech players mature into 100 million customer businesses, scale will demand a stack that provides real-time, secure utility and mostly flawless experience. Many of these players will find that their bank partners, processors, lending partners and others can’t grow quick enough and aren’t flexible enough to reach that sort of escape velocity, which will require them to mature their stack, and maybe even invest in or acquire some businesses that have relationships with regulators that smooth that growth curve. In fact, it is increasingly unlikely that west coast FinTech start-ups like Clinkle, Plastc and Coin will get off the ground as Silicon Valley realizes that you just can’t start a Facebook of Banking in a garage – at some point you need to interface with the banking/payment ecosystem and the learning curve is too steep for Stamford graduates with a tech competency.

This is why we’ll continue to see a separation of the distribution business and the charter/stack/manufacturing elements of the banking business. It’s why Antony Jenkins from Barclays said the Universal Model of Banking is over last week. It’s also why some of the biggest banks in the world are investing in start-ups, incubators and innovation labs – because they know they can’t produce the experiences required for the customer of tomorrow.

To be the Facebook of Banking you need 100 million customers who love your experience and you need funding, neither of which (it turns out) require you to own a charter or the full stack. But you definitely won’t get there without a differentiated experience.


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