FinTech Ecosystem Insights by Bussmann Advisory is our weekly newsletter, summarizing relevant news and reports related to ecosystems around disruptive technologies, highlighting key updates from the industry as well as our portfolio companies:
The latest edition of the FinTech Ecosystem Newsletter is here:
FinTech Ecosystem Insights by Bussmann Advisory is our weekly newsletter, summarizing relevant news and reports related to ecosystems around disruptive technologies, highlighting key updates from the industry as well as our portfolio companies:
The latest edition of the FinTech Ecosystem Newsletter is here.
For the past months, few industries have been riding the Artificial Intelligence bullet like financial services. Whether it’s Wall Street or High Street – most of the big names in banking have launched various attempts at harvesting the promises of deep learning, language processing or reasoning algorithms. Some with recognizable success stories in the likes of automating legal work or quantitative trading, others overselling the introduction of merely rule-based systems like robo-advisors or process automation as machine intelligence.
Huge expectations
As of today, there are hundreds of vendors and consultants selling AI into financial services. More and more Fintech players also claim to use some form of Machine Learning, seen as a quality stamp helping to sell their applications into the financial industry. While this trend ups the pressure to rethink the value proposition of many products and services, it adds a whole new level of complexity and lock-in risk for traditional banks. Given the immaturity of many vendor solutions, they will almost exclusively rely on heavy training with banks’ data. What’s also seldom mentioned is that AI solutions are far from finished products, with a long path to readiness for integration and deployment in a large enterprise context. Moreover, there is a noticeable push of vendors that traditionally dealt with banks’ IT departments towards marketing their tools directly into the front office. Selling whatever buzzword gets their attention may make bankers fall in love with AI tools and speed up the their traditionally slow buying cycle. But buying technology for the sake of having technology typically won’t do the trick. Many business functions tend to start searching reasons to implement a certain tool; often without a clear concept of which client problem to solve, nor sufficient judgment of the effort needed to train algorithms or integrate a tool into existing IT architecture.
There is one theme that banks seem to have unofficially declared their favourite AI application: Chatbots. From San Francisco to New York, from London to Oslo and from Singapore to Shanghai – there are already various implementations of text-based chatbots answering client questions to more ambitious virtual assistants executing tasks like transferring money or scheduling advisor meetings. Add to that the first applications for devices like Alexa or Google Home, an even more challenging discipline given restriction to voice control plus unresolved data secrecy and authentication issues from their heavy reliance on cloud technology.
First learning curve
What most conversational agents have in common however is that their current user experience is mediocre at most. The vast majority are nothing more than dumb Q&A bots. Yes, Natural Language Processing is still the most challenging discipline in AI. And yes, users do give you a novelty bonus for the time being – after all we are still in the age of narrow AI. Currently most bots are capable of little more than linear, single-turn conversations. Many struggle with contextual background, let alone switching context during conversations. Navigating between content levels or understanding the status of a request is difficult. So is building shared context, which would make for a true dialog. With the memory of a certain Disney fish, and often helpless at facing sarcasm or fragments of sentences and words, today’s bots are far from enabling natural conversations. Numerous banks find themselves having to ramp up expert resources that spend their days scripting ever new contents into digestible answers. Many are genuinely surprised at the amount of training data needed to feed a bot with domain knowledge, the effort of getting even a single user intent right, and the lower-than-expected rates of correct intent detection. Add to this the challenge of generative replies and inferring new facts from user content, and it’s plain to see why many first generation chatbots have been shut down after only weeks in operation or trial. Humans have a habit of asking complicated questions, and humans tend to be annoyed quickly.
While bots hold the promise of easier, increased and more seamless interactions with clients, it will only be kept if the bank actually solves their most pressing needs. Don’t get me wrong, I’m all for innovation in financial services. But within reason. We are near the peak of inflated expectations and many banks seem unconscious of the deep trough likely to follow. It’s easy to fall victim to a hype, but when your own tech maturity speaks for starting with easier machine learning on structured data, it’s less smart to attempt automated client conversations first. It is essential to think through processes to the end – a conversation ending with a forced branch visit or waiting for physical mail will still be considered broken.
Challenges
Multi-turn, multi-intent, multi-language, natural conversations are currently wishful thinking and still a thought for tomorrow. In the meantime, it’s worth considering whether the time is ripe for facing clients with automated chats today. This cannot be taken lightly. It is essential to gain experience with user behaviour and establish a viable strategy on how to tackle conversational commerce. Determine preferred channels, interfaces and ways to structure your data sources. Select your vendor carefully and get a reference from its existing clients. Don’t outsource this decision or overload yourself with unrealistic ambitions or complexity from the beginning. Give the bot a frame on what it can say and what statements may be problematic due to their legally binding nature. Start trials with internal users and work your way towards clients. Define minimum thresholds for quality KPIs and measure them. Learn to deal with emotional responsiveness and what makes for a convenient conversation. Be transparent about the fact that users talk to a machine, make clear what it can and cannot do. Give your bot a recognizable, likeable, but neutral persona. Think through how to deal with data secrecy. Determine below what probability of generating the right reply the conversation is handed off to humans, and don’t forget to learn from your service centre’s written replies. Run analytics on conversations and monitor how users’ needs and behaviours change.
As plain as it seems, an industry built on trust cannot afford to jeopardize user centricity.
By Antony Jenkins and Oliver Bussmann
This blog post includes the position paper on banking models.
It’s hardly a secret that the winds of change have been howling through the financial services industry. From post-crisis regulation to the Fintech revolution to the emergence of disruptive technologies like blockchain, there is probably no subject more hotly debated in the industry than its future.
It’s good that banks are taking these changes – and their attendant threats – seriously. They are researching, considering, and examining what to do. Yet while we see a focus on innovation, there seems to be a marked reluctance from some bank executives to recognise the degree of transformation required.
To some extent, this is understandable. There is an unfathomable amount of change happening at the moment, especially on the technology front, making it difficult to keep up. The degree of change that is being talked about – not just adjustments but profound rethinkings – can seem daunting too, making it hard to know how to react.
The prospect of the consequences can be intimidating. Banks are complex, often mature institutions that have already made significant investments in expensive technologies and processes. It can be difficult to accept the thought of abandoning them, as well as certain businesses, for the unknown.
We can sympathise. Both Antony, as the former CEO of Barclays, and Oliver, as the former Group CIO of UBS, know very well what it means to be on the inside of a global bank facing the gale force winds of transformation.
Having both now left these institutions for the front lines of this new, emerging world – Antony as CEO of 10x Future Technologies and Oliver as Founder and Managing Partner of Bussmann Advisory – we think we have a good perspective on what is in store.
That is why we are concerned that our old banking colleagues may not have the right sense of urgency.
Let us make no mistake: for banks the time for research and deliberation is over. As the financial services sector grapples with its Uber moment, so banks may soon face their Kodak moment – a rapid diminution in the relevance of banks to their customers as technology provides the means for others to offer a radically superior experience. The time to act is now.
In this short paper, we try to explain why. We summarise the situation facing banks today, examine its causes, and suggest what we think needs to be done – bringing the perspectives we have gained with our experiences on both sides.
New banking models
We are convinced that the banking business model will be greatly disrupted over the next five to ten years as the result of a complete re-architecting of the underlying market infrastructure. We are already seeing the end of the first stage of this process, in which apps and contactless technology have led to enormous changes in how we use bank branches and cash. This is nothing, however, compared to what is coming. We believe we will soon see a new, unprecedented wave of change influenced by a number of factors, including:
Open for new partners
The opening of the financial services industry will present a completely new world for banks.
For one, this will mean getting used to different kinds of partnerships. Banks have traditionally been closed shops, designing, building and maintaining their systems themselves. While this worked in the past, it does not work in an age of highly complex, interconnected and rapidly changing technology.
In place of the standalone approaches of the past, banks will need to function as parts of larger ecosystems, joining networks of partnerships with FinTechs and other providers in various areas of their business. While challenging on the one hand, these partnerships can also help banks assemble best-in-class capabilities to create innovation and transformation at the speed and scale they will need, helping them stay competitive.
These open ecosystems will also create a new world for consumers. We will see this perhaps most dramatically with customer data, which will increasingly come under the control of customers themselves. With more say over how their data is used and which institutions they share it with, customer relationships will be far less sticky than they are today. The new freedoms customers enjoy with their data will enable them to seek more personalised advice and services from a wider set of providers. It could even conceivably be a source of income: in a world where personal data is a valuable commodity, customers may be able to request payment for its use.
Storm clouds of the 21st century
As financial services are disrupted, there will be no shortage of issues to overcome. Consider, for instance, the changes being wrought by PSD2. Here banks will face significant hurdles in areas like cybersecurity, enabling the integration and then onboarding of third parties, testing, and training. We can expect similar challenges in other areas of the banking business as the market transforms.
While this may seem like a lot of storm clouds on the horizon, banks should focus on the many silver linings. To return to the PSD2 example, banks that focus simply on doing what is necessary from a compliance perspective risk missing new opportunities. Those that take a broader view have a real chance to build a better customer experience, and with it new opportunities for revenues.
Banks should also be careful not to let the gathering storm clouds obscure their vision. Looking inward, they must be wary of an excessively risk-averse culture, which can lead them to move too cautiously. Looking outward, banks will want to be sure they don’t overlook where the real competition is coming from, and get blindsided.
To get an idea of the form such competition might take, consider what happens on our smartphones. Based on our behaviour, location and other factors, platforms like Google are already able to predict the next apps or services we may want to use, or information we may want to have. In the future, these platforms will be able to look at our financial preferences, consolidating our account balances, spending patterns and other information to provide us with highly personalised recommendations to help us manage our money and work towards achieving our life goals.
In other words, the financial advisor of the future doesn’t have to be a bank. It can be a machine, and not necessarily one that’s owned by a financial institution.
Facing new realities
So what do today’s banks need to be thinking about in the face of these new realities?
For one, banks will need to innovate beyond banking to reimagine the customer experience. That will mean taking a radical approach to reinvention. The current incremental approach to change and innovation will not be enough to survive in the future, let alone thrive. Nothing short of transformation is required. For this level of transformation to work, banks need to think beyond solving today’s problems. Instead, they must anticipate the needs and problems of tomorrow and actively help to shape a future that meets them.
In the real-time, connected world that will be enabled by such technologies as the Internet of Things and smart contracts, financial services will be increasingly embedded in the value chains of other industries. Banks need to understand what that means for them. They will also need a better understanding of the data in their possession, as data will be the oxygen that will feed the transformation and reinvention of financial services. The good news is that banks already have a wealth of data about their customer’s needs, preferences and behaviours. The bad news is that it resides in fragmented, closed and ageing systems, which prohibits them from aggregating and optimising it to offer better banking experiences. Those banks that can bridge their internal data silos will have a significant competitive advantage.
In the future banking marketplace, trust will become a key differentiator. We believe the definition of trust itself will change due to profound shifts brought about by the disintermediation of financial services and the adoption of distributed ledger technologies. If, as we maintain, customers will in future own and manage their own accounts and data, then the old question of whom I can trust with my money will be replaced by the new question of whom I can trust with my data. Those banks that can win trust will win business – though they should keep in mind that, once trust is given, customers will expect significant value in return.
That means banks will need to lead with the right values, particularly in the sometimes fraught worlds of digital data, privacy and cybersecurity. In these areas, customers will settle for nothing less than the highest standards.
Banking’s big moment
So what should banks be doing?
For one, banks will have to accelerate their innovation efforts while at the same time considering how to create transformation. That means breaking out of a ‘reactionary’ approach and mindset, breaking free from the burden of legacy infrastructures, and pursuing continuous instead of incremental innovation – among other things by learning from the dynamic, rapid culture of today’s new digital companies.
Doing so will most likely mean partnering with startups, FinTechs and other e-commerce players to accelerate change, grow new revenue opportunities and so achieve competitive advantage. This means adopting a Business Development 2.0 approach and embracing the FinTech ecosystem with an end-to-end orchestration – from setting the agenda to ideation to proof-of-concepts to go-live. 10x Future Technologies is a platform designed to enable such transformation, and can serve as an example. In a sector plagued by legacy technology, which prevents incumbents from reacting nimbly to technological threats, we believe the best platforms can only be designed from the bottom up, with the bank’s precise requirements and future-proof adaptability baked in from the outset. In doing so, banks can build significantly improved customer experiences at dramatically lower operating costs and with full transparency for bank management.
Last, but certainly not least, banks should be aware of the new perspectives all this change will bring. We think it is perfectly possible for banks to seize the opportunity presented by the Uber moment they are experiencing today, while avoiding the massive destruction of shareholder value that would result from a series of Kodak moments.
While it will require leadership and courage to provide the requisite focus on transformation, we believe there has never been a more exciting moment in banking, for those prepared to be bold.
Antony Jenkins is CEO of 10x Future Technologies and the former Group CEO of Barclays
Oliver Bussmann is Founder and Managing Partner of Bussmann Advisory and ex-Group CIO of UBS and SAP
About 10x and Bussmann Advisory
10x Future Technologies reflects today’s changes in infrastructure and business models by providing a holistic solution for banks to address their current challenges. 10x’s future-proof core banking platform will empower banks and non-banks to optimise their customer data and interactions in order to offer new innovative and compelling customer experiences in a secure and trusted way. This will put power back into the hands of the consumer and society and generate new revenue opportunities and models for banks.
Bussmann Advisory helps C-suite executives and decision makers in global enterprises stay ahead of the digital disruption curve. With a client base covering top-tier banks, global consultancies and other firms facing disruption, as well as strong connections in the global Fintech community, the Bussmann Advisory team is close to the pulse of the rapid changes facing industry. It provides thought leadership and advisory services above all in digital transformation, innovation orchestration, and business model re-creation.
In a previous post I wrote about my belief that the world was ultimately moving to large-scale, public, decentralized technology models, and these would give rise to global, public, decentralized platforms for enterprises.
The impetus for that post was the announcement of the Enterprise Ethereum project, and my focus was on blockchain and the debate around permissionless or permissioned ledgers.
Blockchain, however, is only part of the picture. Today we are seeing a grand convergence of several technology mega trends that, working together, will make these future platforms extremely smart, fully autonomous, hyper-connected, fully decentralized, and very broad-based.
While it is the technologists who are building these platforms, it will fall to business decision makers to figure out how best to use them commercially. Certain industries are racing ahead in thinking about the radically new business models this future will bring. Others – including financial services – seem to me to be lagging.
I think that’s a mistake, as I intend to discuss in a later post. Here I would like to look at this convergence in some detail, as I think enterprises really need to understand the new environment they will eventually being doing business in.
Today, as people have recognized when for example talking about the fourth industrial revolution, we have at our disposal the various technological ingredients needed for radical automation and radical decentralization.
Most prominent among these, at least in a commercial context, are artificial intelligence (including, but not limited to, machine learning), big data, the Internet of Things (IoT), and edge computing.
Advances in each of these fields represent extremely interesting new technological capabilities in themselves. But to be truly useful for platform building, they need to work in tandem. That’s because they have a number of dependencies.
For example, thanks to artificial intelligence we can teach computers to think for themselves and make autonomous decisions orders of magnitude faster and, at some point, orders of magnitude better than we can.
But thinking machines first need to be educated – either by being fed a steady stream of information so they can learn on their own, or by being given robust enough models of the world to allow them to make intelligent choices without our help. The prerequisite for this is having enough information around in digital form with which to train our machines. This was impossible before big data.
Once our machines can “think”, we will want to “do”. To drive true large-scale automation, our AI decision makers will need to manipulate real-world devices outside of themselves. But this only works on devices that can receive messages, understand what they are being asked to do, and autonomously carry out their instructions. This was not possible before the IoT. And, as we are learning, for IoT-enabled devices to be able to react quickly, and so be useful in a decentralized world, they will not be able to wait for data and instruction from the cloud. Hence the current interest in developing edge computing, in which data and computation takes place on the devices themselves (the “edge” of the network) and not in central nodes. This prediction was described by Peter Levine, a partner at Andreessen Horowitz in his talk “Return to the Edge and The End of Cloud Computing”. In following video, Peter discusses the pressures that our pushing toward edge computing and away from the cloud:
Last but not least, no decentralized platform can be built if the nodes on the network, whether machine or human, can’t easily, securely, autonomously, transparently, traceably and quickly share data. Where can we look for a technology to allow them to do this? To the blockchain or other distributed ledgers, of course. For this reason, I think blockchain will play a key role in the coming convergence, as the communications, trust and auditing hub. But it is only a part of the picture.
There is no doubt that the radical decentralization and automation this will enable will have a radical effect on business models too. The new environment will just be too different for business as usual.
I expect that, thanks to far greater integration of value chains or between businesses and customers, business verticals will blur. The silos between industries will also come down.
Enterprise decision-makers will need to keep this in mind. In subsequent posts I will lay out in more detail how I think these new models will look, and how in my experience some industries seem to be doing a better job than others in preparing for the decentralized future.
There is no doubt that 2016 has been a tumultuous year.
From Brexit to Trump and the Ukraine to Syria, we have seen many upheavals on the geopolitical front. A lot has happened in Fintech, too, although here the upheaval has in my opinion been almost all positive. Today FinTech is firmly established as one of the biggest sectors in all technology.
What will next year bring?
We can look forward to more tumult I think. If there is one overarching FinTech trend, I would say that several things that were only “potentials” in 2016 will become much more concrete. That could make 2017 the “year of getting real” on a number of fronts.
Here are some of my thoughts.
Along with the above “predictions”, here are some of the trends I think worth keeping an eye on in the coming year.
Finally, 2016 was a very big year for me personally.
2016 was also the year of the launch of Bussmann Advisory, with the goal of helping companies stay ahead of the digital disruption curve.
The company has gotten off to excellent start, better than I could have imagined. For that I am grateful, to my new clients and all those who have collaborated with me and supported this move.
With that, I would like to wish everyone the best of the season and a happy and healthy new year. It promises to be an interesting one.
UBS published a White Paper for the WEF Annual Meeting 2016 about extreme automation and connectivity: The global, regional, and investment implications of the 4th Industrial Revolution.
See the Executive Summary below and read the UBS White Paper now: