To turn Adversity into Advantage, Banks need to Renovate in Winter

Renovate in Winter

To turn Adversity into Advantage, Banks need to Renovate in Winter

by Ben Robinson | March 30, 2020 | 8 minutes read

Crisis is not the time to stop all IT projects, but to double down on the ones that really matter.

Don’t pull up the drawbridge

Beware risk and opportunity cost

Bag some quick wins

Enterprise Software Stack Systems of Intelligence
How the Banking Enterprise Software stack is splitting

Consider Impact on the future

The Future of Banking
The Future of Banking and the Strategic Imperative

This a data play. It requires understanding customer context (interaction preferences, financial situation, needs) and be able to match to the right offering. In the first instance, financial services companies will do this for their own labelled services, but increasingly — to maximize utility and convenience — they’ll need to do it for third-parties services as well (requiring an extensible product catalog) and intermediating and bundling if necessary (which necessitates managing real-time risk). As a third phase, these same institutions can then orchestrate value between the different parties on the platform, stepping back from intermediating and becoming a system of collective intelligence.

Don’t waste a crisis

Articulate the change narrative

Use stop/go triggers

In summary

Do Traditional Banks Really Still Own the Customer Relationship?

Do Traditional Banks Still Hold Customer Relationship

Do Traditional Banks Really Still Own the Customer Relationship?

by Emma Wadey | Dec 11 2019 | 8 minutes read

Conversations and sessions at FinTECHTalents last month covered many hot topics, but one theme dominated: the customer relationship is at stake.

Banks continue to draw a false comfort from retaining customer current/checking accounts, without realizing that data, engagement and monetization opportunities are seeping away to other players.

Despite many banks having already embarked on digital transformation projects and despite many having launched an array of fintech/tech partnerships and initiatives, more is needed for them to prosper in the digital era. During FinTECHTalents, Jim Marous put it frankly:

“Banks have willful blindness; they don’t realise that they are losing business. Just because bank customers don’t switch doesn’t mean that they love you.”

Jim Marous, Digital Banking Report CEO, The Financial Brand Co-Publisher and Forbes Contributor speaking to Ben Robinson of Aperture.co

This ‘blindness’ comes from studying attrition rates which don’t show the bigger picture. Jim explains how this gives false comfort by talking about his own banking arrangements: “With my business bank, I still give deposits and get withdrawals, but most transactions are handled by PayPal, they understand my business intimately. PayPal can offer me a pre-approved business loan instantly. If I went to my bank, it would probably take me 4 maybe 5 days to get approval and that’s assuming that they will approve it at all. I can get this immediately from PayPal. My bank may have my business, but they don’t have my relationship.”

This view was further reinforced during the keynote session on Day 1 of the event when Aritra Chakravarty, Founder and CEO of Project Imagine and Dozens highlighted that:

”The number of customer accounts a bank has doesn’t reflect customer behaviour. People change partners more regularly than their bank; just because you have a large customer base it doesn’t mean that they are engaged and profitable.”

In short, then, for the incumbent banks, headline customer might hide the extent to which their business are being disrupted by new competition. So, what is needed for banks to truly engage with the millions of headline customers?

“We spend 3 hours a day on our mobile phone. On average we look at our phones around 80 times. We scroll through 300 feet of news-feed every day, that’s the equivalent height of Big Ben!” said Russell Pert, Industry lead, Financial Services at Facebook on Day 1 of the conference before concluding:

“people want to do their banking through the services where they live their lives.”

The point being made here is a profound one. It is easier to embed financial services into a service where customers already have a lot of engagement than trying to create engagement in a banking channel. Think how many times you visit, say, WhatsApp (another Facebook property) compared to your banking app.

Also, where banks are using artificial intelligence (AI) to understand customers better, they’re often introducing more, not less friction into the customer relationship. As innovation and entrepreneurship professional at RBS, Roshan Rohatgi said in the Behavioural Science panel,

”Stopping a card transaction due to a possible fraud risk may protect the customer, but can lead to embarrassment and negativity with the bank.”

Bradley Leimer and Theodora Lau, Co-Founders of Unconventional Ventures speaking to Ben Robinson of Aperture.co

And it has never been easier to embed banking into other services. Open Banking opens up access to customer transactional data, creating a unique opportunity for third-parties not only to serve embed banking into their services, but also to do it more personally by meshing up contextual and locational data with bank data. As Bradley Leimer put it to us,

“The promise of open banking to a High Street bank is a degradation of their relationship with the customer. For a fintech, it’s an inroad into a relationship. For a tech provider, it’s a way to take more data in, understand and profile a customer better, and further entrench them into the ecosystem.”

So should banks despair? Not all, sometimes the answer is to go back to basics, rather than to try to emulate Facebook or WeChat.

Roger Vincent, Chief Innovation Officer at Trade Ledger pointed out there is a global funding gap of £1.2 trillion, defined as the shortfall between the capital SMEs require to grow their businesses and what they receive in lending, and that gap continues to grow.

Roger Vincent, Chief Innovation Officer at Trade Ledger

The problem, says Roger, is that

“the economy is becoming increasingly intangible, but banks aren’t yet comfortable lending against these intangible assets, which requires them to capture and process new datasets in real time.”

But taking advantage of new datasets to get credit flowing to SMEs is exactly the kind of opportunity banks should be seizing with digitization.

Likewise, helping to create financial services that are better moulded around people’s changing lifestyles is another major opportunity. As Dharmesh Mistry put it, the way to create a deeper relationship with customers is give them “everything they need for a given context”.

He used the example of a freelancer: banks should adapt their own services, for example, by giving access to credit to top up volatile incomes, but in addition they should provide all of the ancillary services that a freelancer might need such as filing taxes, raising invoices, submitting expenses and so on.

James Perry of BUD at FinTECHTalents
James Perry, Head of Client Delivery at Bud speaking to Ben Robinson of Aperture.co

This might entail a move to more of ecosystem-based business model, but platforms are emerging to facilitate these models. Trade Ledger is building a platform that could easily facilitate this, while Bud is doing this now. As James Perry, Head of Client Delivery at Bud, says:

“We open the platform where banks don’t have to do procurement for 8 to 10 providers, you only have to do it with Bud. We open the door to a network and allow lots of different providers to come “

But going back to basics even further, the route to more meaningful customer interaction may lie simply in helping customers to make better decisions.

Banks sit on rich datasets, but when they’re used well (if at all), it tends to be in the pursuit of up-selling and cross-selling. In part the issue is that customers might get the wrong products for their needs and also that they might find it intrusive — as Poojya Manjunath from Lloyds Banking Group said within the Behavioural Science panel,

“when a personalised message forces the client into a transaction/money exchange that’s when the customer will often back off.”

But the issue is bigger, the products might end up reinforcing bad behaviours.

Like the Facebook algorithm that serves us up more of the content we like, serving up more loans to an over-spender can perpetuate their problems and amplify the cognitive biases from which we all suffer. Instead, banks should help customers to understand themselves better and help them to achieve their long term goals.

Dr. Peter Brooks, Chief Behavioural Scientist at Barclays, put it well on the Behavioural Science panel,

“If our customers aren’t managing their money well, it is our job is to help them to manage it better. The result is that they will become better customers and their lives will improve and they will become stronger economically which helps both banks and society as a whole.”

And he went on to say that the problem often sets in with the product design, “the typical focus of a product manager is about delivering the end product and launching, rather than how to design it in the first instance. You need to get the design right first. Look at the customer journey, look at how the customer uses the product and ask if it encouraging positive behaviours.”

Pol Navarro, Digital Director at TSB with Ben Robinson of Aperture.co

In terms of using data to put the customers’ needs first, Pol Navarro, Digital Director at TSB, used a good example from the SME space.

“There are lots of opportunities to anticipate things. Imagine with Open Banking where you can easily get data from all your accounts wherever they are, and combine that with your accounting software in the cloud, banks can easily help customers predict their cashflow, for example, saying that in two weeks there are all these payments coming but you do not have sufficient funds and therefore something must be done whether it be taking out a loan or bringing money in from another account to avoid an impact in your cashflow.”

The imperative to make this shift to helping customer make better financial and commercial decisions was underlined starkly by Bradley Leimer, who sees it as the existential challenge:

“Banking is an industry today that continues to take profit rather than give profits. It’s a value proposition that’s about how much value I can derive from you rather than how much money I could derive for you. That to me is the biggest opportunity — along with a long term view — that the industry needs to shift or it will completely give up and recede the relationship entirely to big tech and a series of platforms that banking itself will no longer be a part of.”


Any bank looking at headline customers numbers and giving itself a pat on the back should be wary that disruption continues to abound. There remains the big threat, heightened since the advent of Open Banking, that the large technology platforms will eat their lunch.

But the challenge seems to be at one more profound and simpler. Banks more than anything need to change philosophy by promoting customer need above their own. Practically, this means using data to help customers understand themselves better and, in turn, helping introduce them to the services they’ll need and the banking services to support it. Trying to be Facebook won’t work, just try to be better banks.

To see the full interview with James Perry from Bud click here.

Digital Era Banking Systems

Digital Era Banking Systems of Intelligence

Digital Era Banking Systems

by Ben Robinson | Dec 17, 2019 | 11 minutes read

The banking software market is reconfiguring around the demands of the digital economy — and value is accruing to new systems of intelligence

In Clayton Christensen’s Law of Conservation of Attractive Profits, he talks about the “reciprocal processes of commoditization and de-commoditization” that occur in technology value chains when product architectures change:

“The law states that when modularity and commoditization cause attractive profits to disappear at one stage in the value chain, the opportunity to earn attractive profits will usually emerge at an adjacent stage.”

Our view is that this same process of commoditization and de-commoditization is playing out in the market for banking software. Changes in technology (cloud and AI) as well as changes in regulation (real-time payments and open banking) are causing a formerly integrated system to become modularized and new players are emerging to exploit this shift— new core banking systems but also new systems of intelligence that, akin to operating systems, orchestrate value across their networks.

A brief history of banking software systems

When we look at the technology debt in the banking industry, we might forget that banks were once IT pioneers. Banks were among the first industries to use software, adopting branch accounting systems to keep records of customer bank balances as well as to calculate interest, fees and tax.

But, because banks were such early adopters, they wrote their own applications — there was no software industry at that time from which to buy applications. This might not have been a problem except that 1/banks didn’t stop writing applications when commercial software arrived and 2/they have kept and extended those same branch accounting systems ever since — producing the kind of unwieldy system architecture depicted below.

A typical universal bank system architecture (source BCG)
A typical universal bank system architecture (source BCG)

Smaller and newer banks (from the 1980s onwards) skipped the branch accounting system and instead moved to packaged software, integrated core banking systems. These systems had many advantages: they could run on much cheaper hardware (and software) than S/360 mainframes; they could keep separate records based on parties and products (so that it was possible to have the same customer across branches and products and to provide consolidated views of customer holdings); and, they were integrated front-to-back — from the teller to the general ledger — meaning that changes could be applied across the whole system, reducing significantly both the run-the-bank and change-the-bank costs. And so banks running integrated core banking systems were in a position to achieve scale economies as well as to cross-sell effectively and, when product builders were added, to launch new products to market quickly.

System S/360

Bank systems in the internet era

With the arrival of the internet, banks opened up proprietary channels (apps and internet portals) which allowed customers to query their own bank records and set up payment instructions. But that was the extent of the upgrade: neither branch-based accounting systems nor integrated core banking systems were significantly re-architected in response to internet banking. In fairness, some core banking systems were already real-time and most have been scalable enough to cope with the rise in customer interactions. But the situation is changing.

Integrated to Internet Banking

The open banking era

In most industries, product manufacturers have a choice about whether or not they sell through distributors. In banking, in Europe and an expanding number of other places, this agency is being lost. Open Banking legislation is forcing banks to put their inventory online by obliging them to share customer transactional data with third parties (where customers give consent). In effect, banks face a stark choice: become aggregators of own-labelled and third-party products or risk being disintermediated by other aggregators, whether from inside the industry or outside (e.g Amazon or Alibaba).

The Open Banking era
CB Insights showing the spread of Open Banking legislation across the globe

In addition to open banking regulations, most jurisdictions have enacted — or are enacting — legislation related to real-time payments. This will likely have a profound impact on value chains outside of just banking — for payment schemes, for instance — but in banking it will usher in an era of not just higher volumes, but lower fees per transaction, requiring a step change in scalability if banks are to be able to keep up — and to do so profitably.

In response to these two changes, the integrated nature of most banking systems is unsustainable. If banks are to distribute third-party as well as own-labelled products, they will need a separate system for distribution. If banks are to cope with the demands of ever-increasing payment volume as well as continually rising interactions, they will need to separate channels from manufacturing to boost straight-through processing (STP). To put this last point in context, if a bank moves from 99% STP to 99.9% STP, this would likely translate not to a 1% reduction in costs but more likely a 10x reduction in costs.

The future model for banking systems could be the retail industry where the major players have all created distribution systems independently of accounting systems. But there is precedent that is much closer to home: when regulators pushed for higher STP in capital markets in the early 2000s, the industry very quickly split between front office (the buy side) and middle office (the sell side) and systems were re-architected accordingly. And, whereas in capital markets there was a push for faster transactions, in banking there is both a push for faster transactions and a push to open up the industry to new competitors. As such, this split seems all but certain.

Systems of intelligence

At the moment, there is a tendency to try to put more and more logic into banking channels, but this is flawed. Proprietary banking channels are likely to disappear as banking becomes more “embedded” in other products and services (such as WeChat), making these investments increasingly pointless.

Instead, this logic needs to sit somewhere else, where it can be used to produce a high level of engagement across multiple channels, where it can be combined with data from multiple other parties and systems, and where it can handle inquiries independently of orders and order entry asynchronously from order execution. This somewhere else is a system of intelligence.

Systems of Intelligence Basic.png

We borrow the term “system of intelligence” from this seminal article from Jerry Chen, a Partner at Greylock. In his article, Jerry describes how application software is splitting into three layers: systems of engagement, systems of intelligence and systems of record. If we apply the same taxonomy here, customer channels are the system of engagement (although we prefer to use the term system of interaction because we see these as thin clients, integrated using REST principles); core banking systems are the principal system of record; and distribution systems are the systems of intelligence.

In Jerry’s article, he highlights the importance of technology changes in creating the opening for new systems of intelligence. One is cloud in that it adds a new level of scalability on which to build these systems, but the more important is AI, which fundamentally changes the amount of data we can process and the insights we can draw from it. Echoing Clayton Christensen, Jerry Chen says that, because of AI, 

Systems of intelligence in banking

In Jerry Chen’s article, he makes the point that providers of systems of record often have an advantage in creating systems of intelligence because they have privileged access to their own data. This is true for banking also, although open banking removes part of this advantage (for transactional information). A bigger advantage for incumbent banking software comes by dint of serving hundreds or, in some cases, thousands of banks; creating the pull to attract other data sources to mash up with data from their own system of record.

The playbook for incumbents, regardless of industry, remains Salesforce. A lot of people get excited about the Salesforce AppExchange, a marketplace for complementary applications, since it created a platform business model with two-sided network effects. But at least as important in amassing the data to become a system of intelligence are Force.com (now the Lightning Platform), its platform-as-a-service on which third-parties build native applications, and Mulesoft, its API integration platform, which allows third-parties to integrate their existing applications and datasets. Lightning and MuleSoft don’t just provide a route to data but lock-in and switching costs around that data. And then, working on this data and giving an additional incentive to share the data is Einstein, the Salesforce system for artificial intelligence, deriving insights for Salesforce and its customers. We would argue that it is ensemble — MuleSoft, Lightning, AppExchange and Einstein — that makes up the system of intelligence.

Salesforce’s system of intelligence

And so in banking it is unlikely that creating an AppExchange equivalent will be sufficient to create a system of intelligence.

It is likely to need all of the above components: an API platform, PaaS, AI and an app store. And let’s not forget that because of open banking, the distribution play for a banking system of intelligence goes further than distributing apps — to helping banks distribute third-party banking services.

This extends the list of necessary capabilities to include, for example, order management and an extensible product catalogue, as well as customer engagement tools that, among other things, would help identify the right content and services to offer up to customers at the right time and over the right channel.

In addition, we believe a key component of successful systems of intelligence will be to share intelligence across their ecosystems.

The idea, very well articulated in this blog by Peter Zhegin, is that the source of competitive advantage (the moat) is constantly shifting. Processes— and software — are declining in importance relative to data. And within data, Peter argues that the moat is moving away from data collection — amassing the largest possible data set with which to train a model that benefits the company’s product — to improving the collective intelligence of the network.

In banking software, therefore, advantage is moving from having the best application to having the most value-added ecosystem around that application (app store) to helping customers make smarter decisions (system of intelligence) to helping the whole ecosystem perform better (a system of network intelligence).

As a practical example, this could mean moving from providing independent banks with the best credit scoring model to facilitating an open banking network.

Commoditization and de-commoditization — the emerging vendor landscape

As in any market where the value chain is being broken up, there is likely to be a significant shake-up in the competitive landscape for banking software. The keenest fight will be to dominate the market for systems of intelligence, since this is where value will accumulate. But we are also seeing new entrants into the core banking market.

Since the system of intelligence aggregates logic away from the system of record, the system of record is required to do less. Effectively, the most important characteristics of the system of record will increasingly become speed and cost.

As a result, these systems will be re-architected for speed (into microservices) and they will be deployed in the public cloud. And it is no surprise, therefore, that we are seeing the arrival of new cloud-native core banking systems such as Mambu, one of the first and the most successful so far.

Furthermore, as the need for scalability increases, we predict that we may even see these systems further fragment, with the accounting capabilities (fees, limits, etc) splitting from the manufacturing capabilities, which, incidentally, seems to be how Thought Machine is architected.

Digital Banking to Real-time Banking

As regards the systems of intelligence, we foresee a three-player race.

The first players are horizontal systems of intelligence. Insofar as the system of intelligence is like an operating system (nCino actually calls itself “a revolutionary bank operating system”) — providing a consistent set of interfaces, mashing up and running analytics on multiple data sets — these systems do not need to be as domain-specific as systems of record.

Accordingly, there is the potential for horizontal players to make bigger inroads into banking software — such as Salesforce, which already has good traction in wealth management and is pushing aggressively into retail banking.

The second players are the incumbent providers of systems of record. Many are well-positioned — having the pull of large customer bases and investing in the tech infrastructure. Finastra, for instance, has assembled many of the underlying components of a system of intelligence — an app store (Fusion Store), a PaaS (Fusion Operate) and an API platform (Fusion Create). The bigger question is likely to be whether management at these companies will place enough importance on a platform strategy to be able to overcome the immune system challenges.

The third set of players are the new entrants. With a couple of exceptions, such as nCino, these are chiefly vertically focused: for example, Additiv is focused on wealth management (and increasingly credit), The Glue is focused on retail banking, and Trade Ledger on lending. While there are likely many more shared than vertically-specific components in banking systems of intelligence, which makes a cross-banking strategy possible, an initial vertical focus makes sense to build a network quicker (the micromarket strategy to overcoming the chicken-and-egg challenge) and conforms with the pattern of disruptive innovations, which are typically commercialized first in smaller and emerging segments.

To sum up…

In response to regulatory and technology changes, the banking market is undergoing a digital upgrade with new networked business models emerging.

The most successful banking technology companies will be those that align themselves with — and enable — this change.

Our bet is on those that can create the best systems of intelligence.

What is a Challenger Bank for?

What is a Challenger Bank for?
The battle is on to see which firms will dominate the internet-era banking market

What is a Challenger Bank for?

by Ben Robinson | Sep 18 2019 | 13 minutes read

The last couple of months have seen JP Morgan close its digital bank Finn, as well as BPCE close the UK arm of its digital bank, Fidor. This has led to a lot of speculation about whether it’s possible to run a disruptive business within an incumbent organisation. But, it also raises a simpler point. When does it make sense to launch a challenger bank?

Challenger banks are definitely in vogue. In the aftermath of the financial crisis, regulators sought to introduce more competition into their domestic banking markets. One of the most progressive was the UK regulator, which lowered capital requirements for start-up banks as well shortened the application process, leading to a influx of new competitors. But many jurisdictions, including the UK, also introduce Open Banking legislation, which obliges banks to share customer data with third-party providers, also increasing competition.

The Open Banking era
This graphic from CB Insights shows the spread of Open Banking legislation across the globe

In addition to the regulators’ efforts, technology has also lowered barriers to entry. Infrastructure services like AWS have reduced start-up costs while smartphones have opened up distribution at the same time as making possible new digital features, such as remote, paperless customer onboarding.

The result has been an explosion in the number of new companies offering banking services — 17% of all companies having been created since 2005, according to Accenture.

Accenture Beyond North Star Gazing Open Banking
Source: Accenture, Beyond North Star Gazing
Challenger Banks a Global Phenomenon

But digital banks are not just banks without branches. They are offering something different. Part of this is about customer experience — they have built their offerings from scratch, taking advantage of new technology to mould their services around people’s lives. But part is also around business model.

A look at the latest Monzo annual report illustrates this well. It has over 2m customers, but it makes significant losses. It has over 2m customers, but makes net operating income of only £9.2m (£5.7/customer). The majority of its money comes from fee income, not interest income. And, despite heavy losses, it is ramping up marketing expenses (up 700%) and pushing forward with international expansion.

Note: Monzo reported 2m customers as of May 2019. However, most of the metrics above are calculated based on the total number of customers (1.6m) at the end of fiscal year, Feb 2019

In this regard, Monzo — like the other challenger banks — is operating a classic digital-era business model. In contrast to traditional banking models, it recognizes that distribution is not the primary point of differentiation in the value chain; but, instead, customers are.

Number of Customers per Challenger Bank

It is this desire to maximize customer numbers that explains why, despite a historical customer acquisition cost of less than £3, Monzo is engaging in TV ad campaigns. This explains why its investors are prepared to cover its losses and fund its international expansion. And lastly it explains why most income comes through fees, not interest income. Because this is a business where, with low customer acquisition costs, low churn (NPS is +80%) and low cost to serve (GBP30 per account at present and falling fast as scale economies kick in), the incentive is to maximize lifetime value.

Valuation per customer Challenger Banks

Monzo, like other challengers, will seek some direct customer monetization for sure — Monzo is now offering loans — but this is likely to be levied on those who can most afford it, in the shape of premium subscriptions, ensuring that most services remain free (current account, foreign ATM fees) to attract as many new customers as possible. Instead, most of its monetization will be indirect, using the pull of its large customer base to bring in third-party fees. At present, most of its fee income comes from interchange fees as its customers spend money using their Monzo debit card, but over time other routes will make more meaningful contributions. Monzo has said that it wishes to become its customers’ “financial control centre” by introducing them to the best possible third-party financial services and, although the resulting commissions from these introductions are small at present (just £85k), this will grow as two-sided network effects materialize.

Monzo customer contribution margin

For incumbent financial institutions, it is difficult to match the challenger bank model. Their businesses were created for a different age, where distribution was the choke point in the value chain. The need for a costly and difficult-to-achieve banking licence plus a network of physical branches kept out new entrants, meaning banks could push undifferentiated, expensive products to captive clients. And now it is extremely difficult for banks to change course and match challenger banks like-for-like — they don’t have the cost base, the financial incentives or innovation capabilities to do so. And so many banks are starting to launch challenger banks themselves.

Incumbent Banks Offering
The now defunct universal business model where banks were able to mass produce undifferentiated products

However, the bank-within-a-bank model is also difficult to pull off.

Firstly, as Aperture-subscriber John Hagel so eloquently describes in this piece, you have the problem of the immune system fighting off anything that threatens the business model and revenue streams of the body corporate (what in more successful companies might be described as the Innovator’s Dilemma). This likely contributed to closure of Fidor UK and explains why the rest of the business is up for sale. But the challenge to incumbents is very real. As the following table from Citi shows, banks’ RoE is much more sensitive to falls in revenue than reduction in costs, meaning that with stubbornly high costs — and in the absence of business model change (see below) — it will be difficult for banks to countenance a strategy that cannibalizes existing revenue streams.

Source: Citi GPS Research

Assuming that the immune system can be countered — by creating a completely separate organization, with different people, processes, tech, brand, incentives and reporting directly into the CEO — then you have the problem that innovation is hard. This seems to have been more of the root issue at Finn. Built on the bank’s existing IT infrastructure, its objective was more around putting a new UX on traditional products than using the virtues of digital to create a unique offering. As a result, it didn’t manage to attract large numbers, let alone introduce viral features that leverage the power of networked consumers, as Revolut has successfully done.

Revolut

The last problem is one of strategic intent. If a bank launches a digital bank because its strategy is a) to defend itself against challenger banks; b) to lower cost to serve by using digital channels; c) to improve User Experience; d) to capitalize on Blockchain/AI/IoT/Cloud or e) to change its perception among younger customers, it’s probably going to fail.

Launching a digital bank is about launching a digital era business model, which goes way beyond changing brand perception, user experience or moving customers onto cheaper-to-serve channels. As noted above, it is about maximizing customer numbers and engagement to activate demand side economies of scale. This requires clear strategic intent because, in turn, it requires organizational transformation. Launching a challenger bank can be a (faster and less disruptive) route to digitization, but it is neither an easy option nor a panacea.

Our view is that a challenger bank strategy has a higher likelihood of success if is underpinned by one (or more) of the following six objectives.

It is interesting to see Goldman Sachs’ digital bank Marcus referenced in so many of the articles on Finn. For us, it is very different. For instance, it is built on a new technology stack. But most importantly, it moves Goldman into a completely new space, consumer finance, where it does not have the cannibalization concerns that trigger the corporate immune system. This allows it to operate under very different constraints and, like other new entrants, challenge the status quo with a proposition that includes market-beating interest rates, no origination or late fees as well as customizable payment dates and payments. And it’s working: Marcus had 4 million customers and $46bn in deposits at the end of March 2019, two and half years after launch.

In the same way as entering new markets allows the new business to operate more freely, so does entering new countries. It is also a less risky strategy than M&A, which made sense at a time when distribution was the barrier to entry, but now encumbers the acquirer with all of the legacy issues they inherit. And this is why the challenger-led strategy is being pursued by many banks, including DBS, which has launched digibank (“a bank in a smartphone”) in India and Indonesia with already over 3m customers — and why it is looking to do the same in Vietnam.

Launching a challenger bank with the purpose of bringing banking to the unbanked is by definition the antithesis of cannibalization — because no one was providing these services in the first place. And, as banks like CBA in Kenya have shown with M-Shwari and now Stawi, when you combine mobile distribution with low costs and intuitive user experience, you can succeed in bringing financial services to millions of people. But, as demonstrated below, while countries like Kenya and China have very successfully leveraged digitization to tackle financial inclusion, there still exists massive scope to do the same in populous counties like Egypt, Indonesia or Pakistan.

The Financial Inclusion Opportunity

Another reason for launching Marcus was that Goldman Sachs can use retail deposits to lower its group cost of capital. But a business where this is more transparently the objective is EQ Bank in Canada. It is a subsidiary of Equitable Bank, which provides residential and commercial real estate lending services, and the bank uses EQ customers’ savings to fund its lending, allowing it to start to increase its net interest margin (now at 1.6%) in a low interest environment. Structuring the group like this not only creates complementarity between the bank and its challenger brand, as opposed to a cannibalization threat, but also reinforces incumbency advantages. EQ Bank can sustain its above-market deposit rates thanks to its parent’s large lending book.

Equitable EQ Bank Funding Mix

Another reason to launch a challenger bank is to attempt lower-risk and faster-to-value technology renovation. Banks sit on decades of technology debt, batch-based legacy systems built around products not people that have been continually added to over time, resulting in massive cost and massive complexity. If banks are to compete on price and on user experience with digitally-native challenger banks, then they will have to address this technology debt. But doing so is expensive and risky, which why it is tempting for many banks to start again — create a new bank with new technology.

A typical universal bank system architecture (source BCG)
This BCG image shows the mass of interdependent systems and interfaces within a typical universal bank

This “build and migrate” strategy is still somewhat unproven , even though it looks like some banks like Santander, with Openbank, may be going down this route (its annual report states that Openbank is “the testing ground for our future technology platform”).

For banks considering this strategy, they should be mindful that they will have to run two IT platforms in parallel for a good while (it is unlikely that regulators would let incumbents close all branches — or indeed the bank itself — for a long time). They should also be aware that there will be customer attrition in the base business as they divert investment into the new bank and also likely attrition when they try to move across customers to the new bank. In addition, they should start small, that is, with a single product offering like savings, which will enable them to test the market proposition before committing big expenditure, get fast RoI on the initial capital expenditure and minimize the risk of rejection from the corporate immune system — at the same time as probably lowering cost of funding (or, in the case of one challenger bank we consulted, whose first product will be to lend boomer savings to millennials, increase asset yield). Furthermore, if the technological renovation is successful and the bank creates a great platform, then, as Starling, OakNorth and Ant Financial have done, it can sell this to other banks — the “make yourself the first customer” model of creating an exponential software business.

Oak North, a unicorn SME challenger bank, sells its lending analytics platform to other banks

The counterargument to the “build and migrate” strategy is two-fold. Firstly, modern core banking systems are modular, meaning that progressive renovation is possible — replacing systems one by one — to combat risk and speed up time to value. In our experience, however, these projects tend to more complex than they seem and subject to the same issues as all in situ transformations, such a scope creep. A better argument for not executing a build and migrate strategy is that it is increasingly possible to achieve what banks want — improve customer user experience, launch digitally-native products, run advanced analytics and open up to third-parties — without replacing all of their back-office systems, as vendors like Additiv and The Glue are helping institutions to do.

In a blog we wrote last year, we set out what we thought were five viable banking business models for the digital age. However, at least three of these new business models were off limit to banks given their organizational constraints. Launching a challenger bank removes those constraints and allows banks to unbundle themselves by launching a narrowly-focused digital proposition (in terms of product offering as well as demographics) and then to rebundle themselves around this proposition. This is what fintech companies like Transferwise, Robin Hood and Zopa have done successfully and we are starting to see banks do the same — like Imagine Bank from Caixa which successfully attracted a new customer demographic with a convenient and high margin savings product and has since rebundled a whole set of own label and third-party services.

Unbundle to Rebundle

There is also the possibility to do this unbundling to rebundling via a holding company model. This represents the digital equivalent of the traditional universal banking model but where each product offering is run by a separate subsidiary. Doing this keeps each unit nimble enough to compete to respond to market shifts, permits partial customer acquisition, but also allows the overall group to achieve economies of scale (both supply-side, like IT, and demand-side, like customer data insights). In banking, the best example of this seems to be Pepper from Bank Leumi, which is building up a set of discrete product propositions.

If an incumbent bank chooses not to launch a challenger bank, what are its options?

It could choose to do nothing, essentially pursuing a mix of tactical options like cutting discretionary spend, shrinking risk-weighted assets and lobbying the regulator to slow, or reverse course on, new legislation. But, even though this might get management through to its next stock option vest, this isn’t a long-term remedy.

Another option could be to go upstream. We see this a lot in wealth management. Since HNWIs want a somewhat bespoke service and interaction with a relationship manager, then a lot of banks are moving to serve exclusively these HNWIs and UHNWIs where they think they can earn good fees for the foreseeable future. However, since many banks — as well as independent asset managers and family-offices-as-a-service focus on the same market — fees are gradually eroding. But more importantly, it leaves the banks open to classic disruptive innovation as the providers who now serve retail and mass affluent customers with digitally native services start themselves to move upstream.

In our view, for the banks that don’t want to launch challenger banks, there are only really two options. One is to become a bank-as-a-service, offering their back office and compliance to other banks and fintech providers, as banks like Bancorp in the US have done. But, this is a low-margin business. A better option is what we call the thin, vertically-integrated bank, where a bank starts to offer third-party services alongside some of its own products, capitalizing on its advantages —a bank licence, trust, the pull of a large customer base — to give its customers more choice. The challenge here is, of course, that this is a radically different business model which is likely to activate the corporate immune system.

Vertically integrated digital bank

So, the conclusion seems to be: if a company can dismantle the corporate immune system for long enough to adapt its existing business, then a challenger bank might not be the right option. Otherwise, it probably is.

Firms need Business Model change, not Blockchain

Firms need business model change, not blockchain

Firms need Business Model Change, not Blockchain

by Ben Robinson | Jun 1, 2018 | 13 minutes read

When Jimmy Song, a venture partner at Blockchain Capital, took to the stage at Consensus two weeks ago (wearing a black cowboy hat), he launched an attack on the blockchain-is-the-answer-to-everything mentality. He said,

“When you have a technology in search of a use, you end up with the crap that we see out there in the enterprise today.”

Jimmy Song
Jimmy Song

Jimmy was clearly trying to be provocative and burst the bubble of blockchain fanatics, but he has a point. It’s not so much about blockchain per se (although this may be where the worst offences are committed) but about the focus on technology in general. Every day we are bombarded with articles about the need to digitize or about how [Blockchain/AI/APIs/Cloud/Mobile/IoT] will transform or disrupt such and such an industry. But we forget that technology in the absence of new business models never changed anything.

UBER Business Model, by Tim O'Reilly
UBER Business Model, by Tim O'Reilly

It wasn’t the internet that transformed retail or music. It wasn’t the smartphone that created Uber. Instead, it took business model change which exploited new technologies. In retail, it was the Amazon business model of one-click checkout, marketplace and next-day delivery. In music, it was the iTunes model of unbundling music to let us buy individual songs and then the Spotify rebundling model of all-you-can-listen streaming subscription service. And Uber didn’t just use the smartphone to let people order cabs (as many of the incumbents did), but instead uses the power of GPS to allow anyone with a car to become a taxi driver, transforming supply in the course of transforming user convenience and experience.

And so in banking we can safely predict that it won’t be blockchain or APIs or AI that transform the industry. Instead, it will be new business models empowered by those technologies.

Implementing technology without a clear plan risks making matters worse

In fact, we could probably go further and say that implementing new technologies without a clear idea of the future business model may make matters worse because it could well entrench existing practices.

The reason for this is that these new technologies will be implemented in support of existing business models rather than in pursuit of new ones. This means — as we have seen so often in banking — that digital technologies are used to digitize analogue products, rather than reinventing them for the digital age. But, it means more importantly that these technologies will be used to double-down on scale.

Economies of Scale Illustrated

The industrial economy was all about scale. Once a company had come up with a winning product, the challenge was to exploit economies of scale as fully as possible. This allowed unit costs to be minimized and allowed firms to undercut rivals, seeing them gain more market share and scale and thereby locking in their leadership position. So all investments were aimed at maximizing scale — mass marketing, mass production, mass distribution — and business were organized into centralized, hierarchical structures to make this possible.

But these investments in scale in the digital age are quickly moving from sources of competitive advantage to sources of competitive disadvantage.

Technology and platforms have neutralized scale advantages

In their recent book, , Hemant Taneja and Kevin Maney talk about how the technologies of cloud and AI have turned scale economies on their head.

In the world of cloud computing, IT resources are available cheaply to everyone meaning that — other than for the platform providers like Microsoft— scale doesn’t matter. A business can rent as little or as much IT as it needs, conferring little scale advantage in running massive operations.

But it’s not just IT resources, the same model is being applied everywhere. Take human resources, it is becoming increasingly easy to contract the people a company needs at the time they need them through platforms such as Malt and through a new breed of companies like  and .

In economic terms, technology has lowered the minimum efficient scale of production to a point that is within the reach of most SMEs. And with a monolithic business structure, diseconomies of scale kick in sooner and are more material.

Artificial Intelligence is also having a profound impact on scale. If new technologies and platforms make it possible to manufacture profitably without scale, AI is making it possible to know what each and every customer wants — so that product and service can be tailored to everyone.

While the slight flaw in the unscaled argument is that more scale leads to more data and more data leads to better AI, it is nonetheless the case that any company offering undifferentiated products at scale will soon lose market share and scale. And so we see white space for new kinds of business models, where firms — or platforms — are able to take advantage of these new technologies to offer mass customization at scale.

The incumbents’ challenge

The incumbents challenge is, therefore, how to move away from this heritage of scale. This is likely a bigger challenge than it seems. Many companies in the industrial age missed shifts in consumer trends, but because of scale they could in many cases afford to catch up — copying rivals, buying rivals, etc.

In this digital age, the scaled business model is likely to lead to the double whammy of failing to spot new trends and the impossibility of catching up. Moreover, scale is so deeply embedded — across company structures, performance metrics, remuneration, processes, employee skillsets, cultures — that it will be so difficult for incumbents to make the transition.

Number of investments in tech companies by country — source Atomico
Number of investments in tech companies by country — source Atomico

And it’s not just an issue facing companies. Take Germany, for instance. For so long, its industrial sector has been admired all over the world for consistently high quality engineering. But, the German economy is struggling to make the transition to the unscaled, digital world. It doesn’t (yet) have a  from which the new unscaled models are emerging and the .

But there is hope. We do see many incumbent companies, including in the banking industry, adopting new, unscaled business models for the digital age.

New banking business models for the digital age

In many ways, the following section is an update  ago looking at how technology and new regulations, particularly PSD 2, were likely to lead to new business models. Where back then we identified 4 business models, now we identify 5 (but now fully discount the universal banking model as a relic of the industrial age). And where back then we framed the choices around asset intensity and profitability, we now frame the choices around the size of the demographic a firm wishes to serve and the number of products it offers to this demographic (although profitability is likely to improve in correlation with these factors).

Let us consider each in turn.

The unbundled start-up

This is the model that most B2C fintech companies have pursued until now. They spot a niche, which could one of: a product that wasn’t previously offered (e.g Coinbase), a demographic that is un- or underserved (e.g Lending Club), a much better experience (with likely cheaper pricing), combining tech and design thinking (e.g Transferwise) or all of the above(e.g. WealthFront).

It is very much the embodiment of an unscaled model: using cloud infrastructure to operate at low volumes and using AI to serve small segments of the market. However, given it is both targeting a niche and targeting the consumer directly, it is often difficult to make this model profitable. The low infrastructure costs are more than offset by high customer acquisition costs which, because these tend to be single product companies, cannot be spread over many revenue lines. There are exceptions, of course, where the regulatory costs are low and the market is large (e.g WorldRemit), where there is a virality in the product design that lowers acquisition costs (e.g Revolut) or where the product solves a big issue in a big market such that it becomes a very large company (PayPal, M-Pesa, Stripe).

The unbundled startup
The unbundled startup

But the much more likely outcome is that successful unbundled start-ups start to bundle multiple products under the same brand.

The rebundled start-up

Once a start-up has found a strong product/market fit, it is logical for it to offer multiple products in order to boost its return on capital by cross-selling and upselling to its existing clients. It effectively moves from a single, unbundled product offering to rebundling a full banking service over time. However, it is different from a traditional universal banking model in a number of ways, such as the fact that it is digitally native but more importantly because it remains focused on serving the same demographic. In that sense, it doesn’t engage in mass marketing and production, but sticks to a narrow target market. Were it to begin to offer all products to all customers, it then risks becoming the victim of unbundling itself.

Unbundle to Rebundle
Unbundle to re-bundle strategy

Examples of successful unblundled to rebundled start-ups include Zopa, Transferwise and Revolut.

The platform model

The platform model is somewhat of an anomaly in this list since it is essentially a scale play. However, it is likely to be an enduring model since:

1/ it is underpinned by strong network effects in a way that the universal banking model isn’t;

2/ it is often executed as part of an unscaled holding company strategy (see later); and,

3/ it is offered in the service of (and helps to make sustainable) the model of unbundled start-ups.

The platform model is simple. Banks rent out their back office as a service to others. For the unbundled start-ups who would be clients, it offers the advantage of not having to undertake a bunch of low value-added regulation and IT activities and it helps them to go beyond just renting IT infrastructure to renting IT applications and compliance. For the banks, it helps them to spread the largely fixed costs of IT and compliance over much larger volumes, improving economics.

Infrastructure Play
Infrastructure Play

The challenge, as pointed out in the last blog, is that this is a difficult model to scale across borders, limiting its profitability potential and meaning that there will be likely only one or two platform players per country/geo.

Examples of this model we have seen so far include Wirecard, Railsbank, Solaris and Bancorp. And it is no surprise that they are cropping up in the largest banking markets first where potential for scale economies is greatest.

The aggregator model

The aggregator model is where a firm uses its grip over distribution to introduce the consumer to the right unbundled services. Effectively it uses AI and machine learning to understand the customer’s financial affairs and preferences and to anticipate their needs so that it can make the right service recommendations at the right time. With the introduction of PSD 2 — and similar regulation across the world — this model becomes easier to operate since it forces banks to share customer data. And, theoretically, it becomes possible to operate this model without engaging in any product manufacturing or without having a banking licence or any compliance team — as firms like Centralway Numbrs are trying to implement.

The Aggregator Model
The Aggregator Model

Nonetheless, our view — consistent with the blog from two years ago — is that this model will be thin, open but vertically integrated. By this we mean that aggregators will work with many different unbundled start-ups, but because of the nature of banking, they will likely manufacture some products — like current accounts that require a banking licence. And because of the need to deliver exceptional customer experience, they will end up having to become more vertically integrated. We , such as with Amazon and Netflix, and now we observe the same thing happening in banking. When unbundled fintech start-ups rebundle, they tend to become more vertically integrated — witness Transferwise’s move off the Currency Cloud platform or N26’s move off Wirecard.

Vertical Bank Business Model
Vertically-integrated, thin digital bank

And so it is not a surprise that the aggregator models we are starting to observe in banking are thin and vertically integrated, such as M-Shwari and Starling Bank.

However, there are a couple of potential issues with the aggregator model. The first might arise from regulation. Will regulators allow banks that offer own-labelled services to aggregate services from third-parties and trust them to do so completely impartially? Especially given the marked tendency for aggregators to move from . Moreover, there may be a business model challenge in that, as , models like Starling’s rely on third-parties while seeking to internalize the network effects, especially around data.

Aggregators vs Platforms
Aggregators vs Platforms adaption of Ben Thompson's diagram

So, while we continue to believe that this is a sound model, aggregators of this type will need to look to empower the ecosystem by externalizing network effects and may seek to use arms-length intermediaries, like Bud, to avoid potential pitfalls around regulation.

And, where these potential issues are not addressed, aggregators leave themselves open to the threat from rebundled start-ups and from holding company models.

The Holding Company Model

The holding company model attempts to replicate the universal banking model — or conglomerate model in other industries — for the unscaled world and in a way that confers competitive advantage on the subsidiaries, especially by dint of network effects.

There is probably no “standard” for the holding company model. Berkshire Hathway is a great example of how a holding company structure can create competitive advantage across the group companies, in its case by using the cashflows and very low cost of capital of its insurance business to provide the cheap cash for investing.

Amazon is another great model to study and probably more relevant for banking. Jeff Bezos made a decision in 2002 to standardize the way information is shared across Amazon using APIs. It was a brilliant move in how to achieve control at scale. Essentially, the inputs and the outputs of every team were measurable in real time, such that their performance was instantly calculable and all other teams would get the information needed to conduct their work without bottlenecks, but it still allowed the teams autonomy in how to execute. The upside of this API model, so well documented in this , was manifold:

  • it allowed the different teams to operate autonomously so that that those business could be opened up to work with third-parties (as happened with AWS)
  • It allowed each unit to be kept focused on its own KPIs, which essentially means that they are forced to remain close to customer trends. As , the genius of Amazon’s customer obsession is that it forces every part of the business to innovate at the same time as making it practically impossible to overshoot consumer demands.
  • It critically allows every business unit to stay focused on its niche (essentially an unscaled model) but allowing for scale at a group level (e.g IT resources, distribution and brand), positive working capital flows, and the exploitation (internalization) of network effects across group companies.

This is what makes Amazon such a formidable company. It has figured out how to make the conglomerate model work in the digital age — through a holding company structure. And, furthemore, in its digital form, it overcomes one of the key shortcomings of its industrial age predecessor — it can achieve increasing returns to scale thanks network effects.

In the financial services space, the best example of this holding company structure is Ant Financial. Where Amazon has figured out how to adapt the conglomerate model for the digital age, Ant Financial has figured out how to recreate the universal banking model for an unscaled world. Its hub and spoke model sees the group leverage data, brand and distribution while the subsidiaries remain narrowly enough focused — on unsecured lending, investing, money market funds — to remain nimble and adaptive in the face of changing technologies and customer trends.

Ant Financial Holding Company
Ant Financial's Business Model as a Holding Company

The Holding Company as a model for reinvention

We see a strong trend in banking for incumbents to launch new digital banks. The examples abound, such as BNP Paribas’ Hello Bank. While this model to reinvention is in many ways sound — it allows these banks to transplant customers and trust into a new digitally native version of themselves — it risks creating another universal banking model, albeit one built on digital foundations. A better way of going about reinvention would seem to be a holding company model. This might be built on a Berkshire Hathaway model, as seems to be the case with Equitable Bank’s creation of its , to create a sticky, low cost source of funds for its lending business. Or, probably more likely, it would be an Ant Financial model of having individual subsidiaries target different business lines, which is the approach that Bank Leumi seems to be taking with Pepper Bank.

Pepper Bank, by Leumi

Conclusion

There is a clear danger that with the constant hype around technology, banks miss the need to redefine their business models before embarking on major technology renovation. In fact, technology renovation in the absence of business model renovation may well make things worse because it would entrench existing business models based on selling undifferentiated products at the greatest possible level of scale. The digital age calls for something else — products, many of which will be new, targeted on specific demographics, made possible now thanks to technology change. In this blog, we have presented 5 models which would work in this new paradigm, of which the holding company offers perhaps the best route to success — especially for incumbent organizations looking to reinvent themselves.