Digital Assets and Banking: Who Will be The Winners and Losers in the Knowledge Economy

By David Rimmer from Leading Edge Forum looks at role of digital assets in propelling banking into the knowledge economy.

In the knowledge economy, digital assets play a pivotal role. Information technology (IT) is both a significant intangible asset in its own right and the key connecter of other intangible assets. Inevitably, banks must build their strategies around digital assets, just as in the industrial age firms planned their business around machinery, factories and connections to transport networks. In developing strategies, banks will need to take into account the distinctive characteristics of intangible assets, such as scalability and synergies, and identify how to combine digital assets with other intangibles. Let’s take a closer look at the major digital assets which will drive bank revenue and profits.

Scalable digital operations

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David Rimmer
David Rimmer

Intangible assets, such as data and algorithms, have the potential to scale but banks require a means to scale in practice through digital operations if they are to maximise this notional value. For FinTechs and challenger banks this is no problem. They are built digitally from the bottom up, which inevitably gives them scalable software-based operations, with tiny variable costs per transaction. Conversely, if incumbent banks are to compete in the long term, they need operations and IT systems that can scale to match ‘digital-native’ businesses which are digitised from front to back.

At present, most incumbent banks have digital transformation plans that move the bank forward one step at a time from where they are now. This is sensible and pragmatic, but banks will not be able to compete without a parallel strategy that starts from the destination, working backwards from what the bank’s cost structure needs to be. This will be nothing like where they are now, nor even where they expect to be after their current digital transformation plans. Transaction costs may need to be orders of magnitude lower. A vital consideration is that, owing to concentration effects, the number of banks that reach scale in any given market may be small.

There are four options for achieving scalable digital operations: greenfield, brownfield, insourcing / outsourcing and per-click operations.

Greenfield – The critical manoeuvre in this strategy is making the cut-over from old to new, i.e.: “How does the bank bring over legacy customers and data?”; and “How are existing brands and partner relationships leveraged?”. Otherwise, this strategy amounts to following the challengers two to three years after the fact, without leveraging the bank’s strengths in intangible assets.

Brownfield – Banks may decide that in some parts of their business they can come close enough to the goal of scalability through a brownfield approach based on simplifying and transforming their current IT. Here, automation across every function will be indispensable as, put simply, people don’t scale.

Outsource/ insource – Where the bank is not at scale, outsourcing to other service providers will be a compelling option, especially if a function offers little potential to differentiate in the eyes of the customer. Of course, the mirror image of this strategy is to insource additional volumes from outsourcing banks. 

Per-click operations – The final option for scalability involves accessing external services on a per-click model. This is how many digital businesses have managed to achieve global scale quickly. Uber’s rapid growth was possible because its operations are essentially just a bundling of services sourced from partners on a per-click basis. Partner APIs lie behind Uber’s geo-positioning, route calculation, maps, push notifications, payments and receipts. Banks can identify where banking functions and commodity services are available on a per-click basis and incorporate them within their digital operations.

Digital Platforms

The platform is rapidly becoming the dominant business model for the 21st century.This makes platforms fundamental to any intangible strategy. In addition to driving revenue in their own right, platforms draw in more customers, spin off more data and create new data interfaces – all intangible assets that can be leveraged in other areas.

Building platforms

A platform is essentially a multi-sided marketplace that connects parties on each side, with network effects creating a virtuous circle that attracts ever more producers and consumers to the platform. Banks can build platforms around areas of banking, such as trade finance, asset management and wealth management. Alternatively, banks can target platforms at particular customer segments, e.g. small businesses, millennials or high net-worth individuals. A further option is building a platform whose sole role is connectivity, with Bloomberg the poster-child. In any case, the starting point must be total clarity around customer jobs-to-be-done, say, exporting goods or saving for retirement, to use Clayton Christensen’s framework. Why would a customer come to the platform? What jobs do they want done?Equally, banks need to think through which partners are required and what is in it for them. A number of banks have embarked down the road of building platforms, but too many have viewed the platform as a vehicle simply for distributing existing bank products, as opposed to working back from customer jobs-to-be-done and the partners needed for those jobs.[i]

Tapping into external platforms and network effects

Not everyone can be a platform – by definition. Banks should, therefore, consider where it makes sense to adopt a contrarian ‘cheap and cheerful’ strategy of accessing the network effects of other platforms. i.e. “If you can’t beat, them join them’. As an example, 58 banks across Europe have decided to use Raisin as a distribution platform for savings products in order to access a larger network of customers than is possible via their own channels.

Strategies for the global platforms (GAFA and the Chinese platforms)

A further strategic dimension should be assessing opportunities and threats from the global platforms that have become such dominant features in our business landscape. The global platforms may act in the role of distribution channels, customers or competitors – or all three.

  • Competitors – In China, AliPay and WeChat have come to dominate certain financial services segments. In Europe, Amazon, Facebook and Google have registered as a third party to aggregate payment data and initiate payments under Payment Services Directive 2. As a result of these and other moves, banks need to identify where their business is vulnerable to the major platforms and develop defensive strategies.
  • Distribution channels and interfaces – Global platforms, such as Amazon, have become the high streets of today’s digital world and, consequently, it is essential for banks to have a strategy for distribution via these digital high streets. This strategy will need to include integration with platforms’ intelligent agents such as Siri and Alexa, which are likely to become the standard interface for accessing frequently-used digital services.
  • Customers – Banks should look out for revenue opportunities from providing financial services to the platforms themselves and to their customers. For example, Zopa, the UK peer-to-peer lender, has struck a deal with Uber to offer car loans to their drivers.

Algorithms

An algorithm isa set of rules for solving a problem in a finite number of steps. In some ways, the written operational procedures that banks depend on today can be regarded as algorithms because they similarly define a series of steps. Computerisation, however, has transformed the ability of banks to deploy algorithms. Computerised algorithms bring greater consistency in decisions, allow much larger volumes of data to be employed and increase the speed of decision-making.

Machine Learning (ML) and Artificial Intelligence (AI) bring a further step-change in potential to apply algorithms. Whereas hitherto people programmed an algorithm’s rule-set, ML and AI models allow computers to derive their own rules and progressively improve decision-making. In future, all the decisions that are fundamental to banking – credit, risk, fraud and investment – will be made, or at least supported by ML and AI models.

Monetisation of algorithms

Banks will want to capitalise on opportunities to monetise algorithms outside their own operations. After all, if you have a scalable asset why wouldn’t you want to market its use, generating not only added revenue but also harnessing more data to improve your algorithm? A case in point is Metro, the UK challenger bank, which has partnered with Zopa. Metro brings the customer deposits; Zopa brings the algorithms. Similarly, the AI-based lender, OakNorth, is commercialising its algorithms in countries outside its home UK market.

In order to develop and manage algorithms as a coherent set of corporate assets, acentre-of-excellence model stands out as an obvious approach, especially when it comes to ML and AI. The reasons for this are that:the state of the art is not yet mature; expertise is scarce; ML and AI are General Purpose Technologies (GPTs) with application across the whole bank; and, multiple ML and AI models will draw on similar data. Critical too will be measures of model performance and their impact on cost, revenue and profit. These metrics will be prominent in the dials that executives monitor most closely in tracking and predicting bank performance.

Data

Most banks have long-running data quality programmes, but compliance is typically their principal driver. Of course, compliance matters but the importance of algorithms means that banks should think about data first and foremost as a vital asset for revenue generation. In many respects, the data is more valuable than the algorithms. With data you can build algorithms, but algorithms without data are worthless. Google is happy publish its algorithms because it is the only firm that has the dataon customer search queries. Few banks can expect to succeed in the knowledge economy if they are not masters of their data.

Data Value = Data x Ability to Exploit Data

As with any asset, you first need to know what you have. Banks should build a map showing what data they hold and its business value (or potential value). I say ‘potential value’ because as with many intangibles assets, the value of data is not intrinsic, it depends on how / if it is brought into play, i.e. Data Value = Data xAbility to Exploit Data. For most incumbent banks there is a huge ‘data value gap’: the difference between what their data is worth at present and what it would be worth if it were classified, associated with other data and made accessible to those who need it in a timely manner.

Closing the data value gap

In large part, closing the ‘data value gap’ is a matter of improving data quality through traditional disciplines, such as data cleansing and applying meta-data. However, new factors are coming into play as banks extend their use of algorithms and harness new types of data such as unstructured data and ‘big data’ from outside the bank. As an example, for many ML and AI models, where data is stored and how is critical. In addition, as banks hold more and more data, the cost of data storage and management will become a significant concern. Likewise, because the value of much data ages fast – a breaking news story is worth infinitely more than yesterday’s news – access to data in real-time may be important, for example, via data streaming.

This is an area of rapid technology innovation where ‘received wisdom’ around how best to do things has yet to evolve. For most banks, unlike say manufacturing companies, the challenge is not the volume of data but its inter-relatedness and its timeliness. In the meantime, the challenge is keeping abreast with a flood of new technologies, understanding how and where they fit.

 Data access and monetisation

Once data is classified and made accessible, i.e. turned into an asset, it can be monetised. Whereas traditional management information and business intelligence models involve ‘pushing’ data to consumers of information, maximising the value of data entails reversing the flow through a ‘pull’ model. ‘Self-service’ becomes the goal, where consumers of data are provided with data, metadata and a set of tools.

There will also be opportunities to monetise data outside the bank’s own operations, for example:

  • Data services – Banks can seek to provide data services, both in order to generate revenue and to increase stickiness. In personal banking, increasingly customers’ choice of a bank will be shaped by the tools it offers to analyse and advise on spending. For merchants, Wirecard, the German payments provider, has built a service on top of its ePOS solution, which takes merchants’ payment data and provides back to them a machine learning solution for analysing customer value and migration rates.[ii]Data integration is another strategy: Barclays’ DataServices transfer data on payments and cash balances directly into customer accounting systems
  • Revenue from data sales – GDPR and other data regulations notwithstanding, banks will derive revenue from data sales. For example, companies such as Cardlytics provide targeted offers from retailers to bank customers who have opted to receive offers
  • Data aggregation – As banks’ ability to derive value from data increases, they will be active in aggregating and acquiring additional data, through offering customers added-value services in return for permission to use and partnering with data vendors who hold complementary data sets where 1 + 1 =3.

The sooner banks start down the road of thinking about their data as a vital corporate asset the better because it is hard to make up lost ground. Firstly, resolving data management issues around years’ of complex inter-related data plain takes time. Secondly, developing algorithms – which is why you want the data – is a learning process that depends on iterations, so it too just takes time. Most banks require much more impetus here.

Digital assets that can be monetised in their own right

Having built digital assets to support their own business, banks may find opportunities to monetise digital assets in their own right.

In many cases, the opportunity to monetise digital assets will come through APIs. Capabilities that were developed as part of an overall bank process, such as providing account data or initiating a payment, may be commercialised as stand-alone services via an API. Partners will consume bank APIs on a per-click basis as part of their own distinct customer proposition. As more and more elements of the economy are digitised, there will be an increasing range of opportunities to embed payments and other banking functions within the operations of other sectors. Banks should consider monetisation of any functions that they have digitised to support their business – not just banking functions. For example, Know Your Customer (KYC) checks are needed in a range of sectors (accountancy, legal and real estate) as a precursor to doing business.

Conclusion

To succeed in the knowledge economy, banks will have to put digital assets at the centre of their strategies to drive revenue and profits. Thinking about scalable digital operations, platforms, data and algorithms as distinct assets will in itself mark a step-change – right now they barely feature, if at all, on bank balance sheets. Human capital and organisation capital – people, skills, roles, processes and governance – will all need to evolve in support. Moreover, as with chess pieces, banks will have to learn the moves that are possible with each digital asset and decide how to bring them into play alongside other intangible assets within an overall game strategy.

[i] A framework for brownfield firms to map out platform strategies and to anticipate the moves of digital-native competitors is detailed in Liberating Platform Organizations, by Bill Murray of the Leading Edge Forum

[ii]Digitise Now, Wirecard Annual Report, 2017