Twitter adds ‘tip jar’​ to pay for good tweeting | WEF on blockchain use in capital markets | HSBC voice biometrics prevents £249m attempted fraud

FinTech Ecosystem Insights by Bussmann Advisory is our weekly newsletter with over 29’000 subscribers, summarizing relevant news and reports related to ecosystems around disruptive technologies, highlighting key updates from the industry as well as our portfolio companies:

  • Twitter introduces Tip Jar to send and receive cash for tweets
  • WEF: Little market-wide agreement on blockchain use in capital markets
  • HSBC voice biometrics prevents £249m attempted fraud

The latest edition of the FinTech Ecosystem Newsletter is here:

Alibaba hit with record $2.8 billion fine in China | Google has long way to go to becoming anti-Amazon of e-commerce | Fintech billionaires revealed

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:

  • Alibaba hit with record $2.8 billion antitrust fine in China
  • Google has long way to go to becoming anti-Amazon of e-commerce
  • Fintech billionaires revealed: Who is on the 2021 rich list?

The latest edition of the FinTech Ecosystem Newsletter is here:

Google to spend $7 billion in data centers in 2021 | Morgan Stanley to offer clients access to bitcoin funds | SPACs raise record quarterly amount

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:

  • Google to spend $7 billion in data centers and office space in 2021
  • Morgan Stanley becomes first major U.S. bank to offer clients access to bitcoin funds
  • SPACs raise record quarterly amount

The latest edition of the FinTech Ecosystem Newsletter is here:

China-linked hack hits Microsoft customers | Amazon opens cashier-less store in London | PayPal might buy digital currency startup Curv for $500m

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:

  • China-linked hack hits tens of thousands of U.S. Microsoft customers
  • Amazon opens cashier-less store in London
  • PayPal might buy digital currency startup Curv for $500m

The latest edition of the FinTech Ecosystem Newsletter is here:

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PayPal goes Galactic | TransferWise becomes Wise | FinTech firm Klarna is raising $1 billion at a $31 billion valuation

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:

  • PayPal goes Galactic to make universal space payments a reality 
  • TransferWise becomes Wise 
  • Fintech firm Klarna is raising $1 billion at a $31 billion valuation

The latest edition of the FinTech Ecosystem Newsletter is here.

Consider launching a chatbot in 2017? Challenge the hype!

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.


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.

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Everything else is changing – why isn’t your bank?

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:

  • Broad-based platforms driven by standards and interoperability: The continued development and increased use of standards, along with ever greater technological interoperability, means that it will be increasingly feasible to build ever more broad-based platforms and ecosystems with other companies and FinTechs. As these systems are built, it will drive the creation of new business models.
  • Open platforms driven by regulation. Banks and other financial services institutions are preparing for the implementation of the revised EU payment services directive, PSD2, in 2018. This directive will force banks to open their customer accounts to third-party service providers; we can expect similar developments in other jurisdictions. This will lead to the creation of open banking platforms, allowing third parties – either as partners with banks or competitors – to create more exciting customer experiences than are available today, as well as provide increased transparency on performance and fee structures.
  • First-mover blockchain use cases. Blockchain has been tipped as a major disruptor of financial services for a while now, but only this year have we started to see blockchain-based platforms moving from proof-of-concept into production. The first movers have been focusing on areas like global payments, trade finance, automated compliance, post-trade processing and cryptocurrencies. That makes sense. It has been estimated that blockchain technology could drive efficiency savings of between USD 80-110 billion, a powerful incentive. And as the low-hanging fruit are successfully picked, it will only add to blockchain’s momentum.
  • An intensified war for talent. As the underlying market infrastructure changes, so too will the skills needed to build and run it. In financial services, these new skills will be in areas like artificial intelligence (in particular, robotics and machine learning), as well as big data, distributed ledger technology, and cybersecurity. We can expect a war for talent in these and related disciplines, as banks and FinTechs battle for the people with the right skills as well as the right domain and technical expertise.
  • Crumbling legacy architecture. To bring in the new, what to do with the old? Incumbents have long been dealing with the pressures of their high-cost, highly vulnerable legacy systems. These pressures will continue to grow.
  • Growth of FinTech challengers. As banks deal with their legacy systems, the door will open for more innovative, less encumbered FinTech providers. That will continue their push to ever greater market share.

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.

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The big picture of radical decentralization

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.


All together now


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.


New world, new model


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.


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2017: the year we get real?

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.

The year ahead: Predictions for 2017

  • The year of the pilot. 2017 will be the “year of the pilot” for blockchain in financial services, as it moves from proof-of-concept into production. We should see this in particular in cross-border payments and trade finance. Overall however blockchain will still be restricted to the “low hanging fruit” in banking. I remain convinced that broad-based application of DLTs will happen more quickly outside of financial services.
  • The year of the standard. We may see significant progress in blockchain standards during the year. If so, it will be driven by small groups working on specific use cases as opposed to large, complex consortia. Indeed, I expect we will see consolidation in the blockchain consortia area.
  • The year of the platform. On the back of increased standards and interoperability, we should see broad-based platforms and ecosystems continue to emerge, driving banking as a service and the creation of new business models. Look for this particularly in the robo-advisory and lending businesses.
  • The year of the attack. The number of cyber-attacks on organizations will increase significantly, and we can expect a steady stream of revelations about hacks. Denial of service is becoming much more threatening and dangerous for banks and in 2017 banks and others will be called on to toughen their defenses. This will be reflected in cyber-security spends, which among wholesale banks will increase from 5% of total tech budgets to 7-8%.

Eye on the prizes: Trends to watch in 2017

Along with the above “predictions”, here are some of the trends I think worth keeping an eye on in the coming year.

  • PSD2 pushing partnerships between banks and FinTechs. Banks and other financial services players will have to spend 2017 preparing for the implementation of the revised EU payment services directive PSD2 in 2018. With the creation of open banking platforms, there will be opportunities for FinTechs to partner with banks to create more exciting customer experiences and provide increased transparency on performance and fee structures.
  • Competition among financial centers for FinTech innovation. 2016 was the year of regulatory sandboxes with the FCA and MAS Singapore leading the change by establishing themselves as business developers with a mandate to attract business to their respective jurisdictions. In 2017, leading regulators will strengthen their position with global collaboration and implementation of new policies and laws based on learnings from their “sandbox” environments in order to reduce uncertainty in the FinTech ecosystem.
  • The continued rise of smart machines. It’s no secret that there are great strides happening right now in artificial intelligence. Advances in machine learning and robotics will I think continue to sweep the business world. Startups will continue to get funding in the areas of risk assessment, research, investment management, trading and back office automation.
  • An intensified war for talent. Banks and FinTechs will be competing for people with the right skills. The key expertise in financial services will be in artificial intelligence, in particular robotics and machine learning, where the game will be to attract scientists with Masters Degrees and PhDs. There will also be a battle for domain and technicaly expertise in finance, distributed ledger technology, and cyber security.

A new road

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.

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Mastering the 4th Industrial Revolution

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:

A brief history of industrial revolutions

  • Prior industrial revolutions have centered around improvements in automation and connectivity.
  • The First Industrial Revolution introduced early automation through machinery, and boosted intra-national connections through the building of bridges and railways.
  • The Second Industrial Revolution began when automation enabled mass production and fostered more efficient, productive connectivity via the division of labor.
  • The Third Industrial Revolution was propelled by the rise of the digital age, of moresophisticated automation, and of increasing connectivity between and within humanity and the natural world.
  • The Fourth Industrial Revolution is being driven by extreme automation and connectivity. A special feature of the Fourth Industrial Revolution will be the wider implementation of artificial intelligence.



What are the potential global economic consequences?

  • Polarization of the labor force as low-skill jobs continue to be automated an this trend increasingly spreads to middle-skill jobs. This implies higher potential levels of inequality in the short-run, and a need for labor market flexibility to harness Fourth Industrial Revolution benefits in the long-run.
  • Greater returns accruing to those with already-high savings rates. In the short run, this could exacerbate inequality via relatively lower borrowing costs and higher asset valuations.
  • As the issuer of the world’s reserve currency, the US’ competitive advantages, sitting at the heart of the Fourth Industrial Revolution, could tighten effective monetary conditions among US dollar-linked economies.
  • The Fourth Industrial Revolution increases the magnitude and probability of tail risks related to cybersecurity and geopolitics, but may spur regional action to invest and embrace Fourth Industrial Revolution benefits.



Who will be the regional winners and losers?

  • “Flexibility” will be key to success in the Fourth Industrial Revolution; economies with the most flexible labor markets, educational systems, infrastructure, and legal systems are likely to be relative beneficiaries.
  • Developed economies are likely to be relative winners at this stage, whereas developing economies face greater challenges as their abundance of low-skill labor ceases to be an advantage and becomes more of a headwind.
  • Emerging markets in their demographic prime may find that extreme automation displaces low-skill workers, but that their limited technology infrastructures do not allow them to reap the full benefits of extreme connectivity.


What are the investment consequences?

  • Given current assessments of relative competitiveness, emerging markets maybe less well placed to profit from Fourth Industrial Revolution benefits, relative to developed markets.
  • We expect further disruption to traditional industries from extreme automation and connectivity.
  • Big data beneficiaries include firms that harness big data to cut costs or target sales; firms that automate big data analysis, and firms that keep big data secure.
  • Blockchain applications could benefit firms that use them to automate processes securely, to cut out costly intermediaries, and to protect intellectual property.