Markus Schuller, founder of Panthera Solutions
The OECD Financial Roundtable on October 27 gathered together 20 representatives from the banking industry, fintech companies, and other financial services, as well as trade unions and other experts, in addition to the OECD delegations. The topic Fintech: Implications for the shape of the banking sector and challenges for policy makers allowed for an intense debate, especially among the 20 mostly private sector participants.
Ironically, both Fintechs and big banks lobbied for level playing fields, arguing that the respective “other” benefits from a regulatory advantage. It also became evident that the big banks try to justify their existence by highlighting their large capital and client base, expressing interest in cooperating with Fintechs by offering scalability. The latter is claiming to add a moment of disruption to financial services, opening it to a wider audience by democratising financial services. Whether the race is decided through competition or cooperation, regulatory “sandboxes” were presented as appreciated tools to level the playing field for both.
At Panthera, we are asset allocation specialists. As such, the Fintechs named Robo-Advisors in the field of asset management are of most interest for us. Inspired by the OECD FRT, we looked at whether Robo-Advisors deliver on the promise of adding a disruptive moment to our market segment. For that purpose we introduce two asset allocation penalties as indicators of disruption.
As we concluded in our article “Man at the centre of the investment decision”, the underperformance of professional investors versus the market portfolio is dominated by two structural factors, cost penalty and behavior gap penalty. Cost penalty is defined as the amount of under-performance caused by transaction costs, management fees, distribution fees, etc. Behavior gap penalty is the contribution of the human factor to a biased perception of reality caused by cognitive dissonances. Indicators of the penalty along the investment process can be certain market timing techniques, the application of flawed portfolio optimisation techniques, minimising career-risk as primary objective, and other expressions of cognitive biases.
As highlighted in a previous article, professionals managing other people´s money like regional banks, private banks, wealth managers, investment companies, (multi-) family offices, etc. are confronted for the first time in decades with a situation that forces them to:
- either grow aggressively in size to play a shaping role in the industry´s concentration process
- take on the competition with Robo-Advisor Fintechs in offering low-cost, fully automated wealth management solutions
- position themselves as leaders in an investment management niche via an innovation-driven competitive edge
- or accept to be squeezed out of the market.
Options 1 and 2 are out of reach for most of the investment service providers listed above as they are too small, too conservative, and/or too loaded with overhead costs. Assuming they want to survive, Option 3 is the only one left for the vast majority of professional money managers.
If Option 3 it is, getting trapped in pseudo-innovations like risk parity will be insufficient. Consequently, a learning organisation with a continuous improvement cycle is a prerequisite for establishing and maintaining the innovation-driven competitive edge of an investment process in the chosen niche. Many will not manage to reinvent themselves.
Like Big Pharma during the 2000s, which benefitted of windfall profits due to rent-seeking oligopolies, the asset management industry is increasingly dominated by a handful of multi-trillion-dollar players like Vanguard, BlackRock or State Street. Big Pharma was compensating its lack of innovation ability by re-investing its windfall profits into biotechs, refilling its product pipeline with the ideas of promising start-ups.
We see similar patterns occurring in the asset management industry, where Robo- Advisors convert from stand-alone B2C (business to consumer) providers to either white-label B2B2C providers or useful take-over candidates for the big players. With Vanguard launching its Personal Adviser Service already mid 2014, Charles Schwab following with its Schwab Intelligent Portfolios in 2015 and BlackRock taking over FutureAdvisor shortly after, the big players benefit from the momentum of digitalisation.
Here, the weakness of the Robo-Advisory start-ups become obvious. Their offering is lacking the disruptive element. All they offer is a more compelling user interface as improved distribution channel, lower production, and end-consumer costs compared to traditional money managers. In that, they are powerful enough to put pressure on the small-to-medium sized money managers, but have no leverage on disrupting the industry’s oligopoly. The explanation lies in four reasons:
- Robo-Advisors help investors to minimise their cost penalty. By still relying on traditional portfolio construction techniques of the first generation (Mean-Variance Optimization, MVO, etc.), they are offering identical services like thousands of established money managers. As such, they don’t offer disruptive innovation at the head of the asset management industry, but simply an evolution of presenting those methods – user interface – and distributing them differently – cheaper fee-model and no intermediaries along the distribution channel. Having talked to several Robo-Advisor executives, their responses can be summarized as: “we definitely have other issues than the portfolio construction methods used”. They consciously ignore, that, although their traditional techniques have been performing well since 2009 through a historical anomaly, they failed in raising significant assets under management because they lack competitive edge in portfolio construction.
- Related to reason 1 – neither the big players nor their emerging rivals are significantly reducing the behaviour gap penalty. Their rebalancing and cost average techniques are helping investors to apply some self-discipline. Though this does not hold investors back from overruling those techniques in times of turmoil, when the pro-cyclical temptation is shown to be the highest. This blind spot on the behaviour gap penalty is explained by the first generation portfolio construction models used, as for those, the human factor in investing does not exist. Unsurprisingly, this is less of a problem for the big players, given that the start-ups are not challenging them with taking the lead.
- The big players remain more competitive than Robo-Advisors because, while applying identical portfolio construction techniques, they can offer their advisory services even cheaper by still making money on the investment products chosen or through transaction fees. Charles Schwab, for instance, manages to charge zero fees for their Robo-Advisory service. It cannot get cheaper than that. Furthermore, the big players can scale their Robo-Advisory business through their enormous asset base.
- Both the big players with their Robo-Advisory front-end and the Robo-Advisor start-ups acknowledge in the meanwhile that retail and institutional investors need to have a human client advisor as back-up. By responding to that need, both are either hiring client advisors themselves or offering white label solutions of their platforms to RIAs/IFAs (registered investment advisors/independent financial advisers). Given the stronger balance sheets and better scalability of brand and existing customer base, the big players with Robo-Advisory front-ends enjoy a competitive edge.
In short, Robo-Advisor Fintechs are currently not revolutionising the asset management industry as they lack a disruptive element, but are helping accelerate the concentration process to produce even larger players.
A session at the 2016 OECD Forum entitled “Teaching & Learning with Robots” brought Nao, a humanoid robot, to meet with a class of young students from the Sections Internationales de Sèvres (SIS) school. Catherine Potter-Jadas, head of the primary school, noted the children’s reactions to the robot.
Educators will take comfort from views such as: “For the moment, the robot can’t replace teachers because, in a country like France, children are too immature, and because they need a real human to control them. A robot wouldn’t have the authority.” In fact, most of the children saw the robot as useful assistant, rather than a substitute: “I think it’s great that a robot helps children in schools. They’ll find it interesting and become more open” said one, echoed by a classmate who thought that “Nao could be really helpful to education.” And of course some were more attracted to the entertainment value: “When the presenter said ‘Nao can carry anything lighter than a wooden spoon,’ he flexed his muscles! I liked the way the robot laughed and showed his muscles, it made me think of an odd little creature.”
Robots first made their appearance in industry, starting in the automobile sector in the 1960s. For decades, industrial robots were bulky and expensive. They were operated from stationary posts inside the workshop, and they carried out a small number of repetitive tasks, sometimes dangerous ones like soldering and cutting metal. With improvements in technologies, a second generation of robots was born. Less bulky and expensive, more autonomous, adaptable and cooperative, these robots are programmable and can be used by workers without any specific qualifications. They can also play new roles in services, health (surgical operations), education, training, commercial information, services to the elderly…
The children’s comments and questions raised a number of issues about the evolving role of technology in society and the economy, and how to equip people to take advantage of the profound impacts digital technologies will have on all of us. The majority of concerns related to robots are based on loss of jobs in developed economies. That said, there could be a “relocation” of low-skilled jobs to countries that have robots.
It is quite clear that challenges to the development of robots remain, in particular in the areas of perception, specific object recognition in a visually cluttered environment, object manipulation, and cognition. But smarter and more autonomous robots will soon be a reality thanks to improvements in a number of areas, including computational performance, electromechanical design tools and computer numerical control machines, storage of electrical energy and energy efficiency of power electronics, availability and quality of local digital (wireless) communication, scale and effectiveness of the Internet, and data storage capacities and their computational power.
In the commercial and industrial sector, beyond the improvements in reliability of manufacturing processes, robots have already shortened delays in the manufacturing of finished products, which allows for greater reactivity to detailed variations in demand. The market for personal domestic robots is growing from year to year (20% a year), while the prices should drop in the near future.
But even if people with no particular training will be able to use the next-generation of robots, those who have not mastered ICTs will find themselves more and more limited in their access to many basic services, to rewarding jobs, and to opportunities to improve their skills through training. Without ignoring the problems that arise from the disappearance of certain jobs and the serious repercussions on people and society, we must acknowledge that these innovations are full of opportunities for productivity development; they could create the new jobs of tomorrow.
It stands to reason that workers who acquire the skills necessary to adapt to changes in their line of work will be less vulnerable to replacement by digital technology. The innovations sparked by digital technologies could also present the potential for development and management of social improvements, in areas like public administration, health, education, and research. The creation of huge amounts of data and the capacity to extract knowledge and information from this data (known as big data) will launch a new wave of innovation, the creation of new services, the emergence of new products and markets…
Employee skills management will become vital in order for companies to adapt to rapid technological change, with support from complementary public investment in, for example, education and training. Primary and secondary schools will be responsible for preparing young people for an interconnected world where they will live with people of different origins and cultures, an undeniably “globalised” world.
The children from SIS didn’t sound at all worried by the prospects of more and better robots, quite the contrary: “I found it quite amazing and fabulous that technology is able to do such things. The people who built Nao must be very proud of their invention.”
Johannes Jütting, Manager of The Partnership in Statistics for Development in the 21st Century (PARIS21), and Christopher Garroway, Economic Affairs Officer at the United Nations Conference on Trade and Development (UNCTAD)
In January, the World Economic Forum meeting in Davos, Switzerland saw members of the global elite extolling the virtues of the so-called “4th industrial revolution”. The catch-all term, also known as “Industry 4.0,” ties together a wide range of cutting-edge digital technologies – such as 3-D printing, machine intelligence, the internet of things, cloud computing, and big data – into a vision of a future world of work. In this brave new world, smart factories will operate by automation with machines exchanging data seamlessly. The consequences for the work force in both developing and developed countries will be huge.
To start with, the hoped-for productivity gains from the 4th industrial revolution will have a global impact on the amount, type and quality of jobs available and on worker competitiveness. Most of the worries expressed so far about the rise of the robots have focused on job losses in developed economies. But there will be consequences, too, for those developing countries that depend for their competitive advantage on low-cost, low-skilled labour. For example, we could see the re-localisation of low-skill jobs (and even many medium-skill jobs) back to developed countries that possess robots. That could turn global value chains on their head, potentially spelling their demise as a development strategy, as mentioned in some of the targets and commitments of the new United Nations 2030 Agenda for Sustainable Development and in the Addis Ababa Action Agenda.
So how can developing countries confront this possible widening of the digital divide, and its potential threat to their development strategies? One thing they need to do is turn the possibly liberating power of open data and big data to their own advantage. If data are the lifeblood of the robot revolution, then they must also be used to defend and compensate those who might lose out from these disruptive technologies.
Open data and big data can be important tools for helping entrepreneurs in developing countries maintain a stake in global value chains. Take the example of business-2-business web marketplaces like China’s Alibaba, which connects small- and medium-sized businesses to global markets. The more these businesses in developing countries can get online and engage in e-commerce, the greater chance they will have of following the changing patterns of global value chains. Another promising example is the US data-driven trade-analysis solutions company, Panjiva, which uses machine learning and data visualization tools to mine publicly available customs data. This allows entrepreneurs to identify and source new suppliers and new importers. While today a European importer might be using such tools to find a supplier in Asia, as the 4th industrial revolution kicks in, these tools may soon be connecting entrepreneurs from the developing world to robot factories in Germany, for example. But for this to work in everyone’s interest, open data standards and big data analysis skills need to be more widely embraced and prioritized in developing countries. This also means putting in place the right institutions that can allow their use to spread – and empower – citizens.
Outside factories and boardrooms, the technologies of the 4th industrial revolution can be used to enable a wide range of new services to help guarantee and protect citizen rights. The impact of these technologies is also already being felt through the expansion of public “smart” services: Smart cards and RFID technology, for example, are being used to create unique identification numbers for citizens in many developing countries, not only to improve civil registration, but also to enable financial inclusion and payment of government benefits as countries expand social protection. Agricultural productivity can also be improved: In East Africa, for example, cell-phone services are offering real-time price data to farmers.
One of the biggest challenges to embracing these new technologies in developing countries may be that the relevant policies and legal frameworks are in their infancy or non-existent, as UNCTAD’s Global Cyberlaw Tracker reveals. Data literacy, official statistical capacity and investment in 4th industrial revolution technologies are particularly low in these countries. Legal standards and frameworks are outdated or non-existent, and individual rights with respect to data collection and privacy almost unheard of.
To realize a “digital dividend” from Industry 4.0, the World Bank’s recent 2016 World Development Report says countries need to put in place “analogue components”. This means providing a level playing field for healthy competition between tech companies; raising the tech skills of all workers; and holding brick-and-mortar government accountable to citizen’s online rights. These “analogue components” are at play in the ongoing dispute in India over Facebook’s Free Basics service, which rolls out limited online services on mobile phones to underserved markets. Some see it as a promising idea for expanding the digital citizenry, helping improve poor people’s skills and use of new technology. However the telecoms regulator in India has just come out against the service because it provides free access only to some websites, rather than to the internet as a whole.
By its very nature, technology can be both liberating and disruptive. Attempting to resist it can also be futile or counterproductive. But the promise of the 4th industrial revolution suggests that disruptiveness does not have to mean divisiveness. Open data, big data and smart services, working hand in hand with the right policies, can go a long way to counterbalancing the disruption caused by robots, machine intelligence and the internet of things.
Continue the conversation on twitter with Johannes (@Jo_Jutting), Chris (@chelnikov) and PARIS21 (@ContactPARIS21)
In the second of two postings, we look at the impact of artificial intelligence on our societies and economies.
Back when Amazon mostly sold books, it hired writers and editors to come up with helpful reviews and recommendations. The aim was to create the atmosphere of a friendly local bookshop. But the writers and editors didn’t last. They were replaced by Amabot, an algorithm that picked up on users’ browsing and buying history.
Amabot’s buying recommendations were – and are – often eerily accurate, but even some of Amazon’s own people didn’t much like the software robot. As Steve Coll writes, one anonymous staffer even vented his spleen in a newspaper ad: “Thanks for nothing, you jury-rigged rust bucket. The gorgeous messiness of flesh and blood will prevail!”
Will it? Just over a decade since that ad appeared, the rust buckets are becoming more powerful by the day. Even sober commentators like the Financial Times’ Martin Wolf speak of the dawn of a “second machine age,” one in which machines “will replace and multiply our intelligence.” And how about flesh and blood – i.e. you and me? To return to Martin Wolf’s theme, it depends on whether your intelligence is about to be multiplied or replaced.
Wolf’s theme is explored in greater depth in Average is Over by the influential economist Tyler Cowen, who argues that only a fairly small number of workers – perhaps 10 to 15% in the U.S. – will have the sort of skills that can be complemented, or multiplied, by computers. As a metaphor for the carbon-silicon partnerships that will succeed in the high-tech economy, he uses “freestyle chess,” where players are allowed to augment their skills with computers. For example, while a computer might need to scroll through all the possible moves in a chess game before making a decision, a human partner could use intuition and insight to spot an opening and then instruct the computer to focus on pursuing that opportunity. In this case, the combination of human and computer is stronger than either human or computer alone.
But what about everyone else? Well, if you can’t add value to the computer you may be at increasing risk of simply being replaced by it. According to estimates by Carl Frey and Michael Osborne, 47% of jobs in an advanced economy like the U.S. are at risk from computerization, adding to the long list of jobs that have already been lost to technology. As Bill Gates warned recently, “Technology over time will reduce demand for jobs, particularly at the lower end of skill set. … I don’t think people have that in their mental model.” But the technology revolution won’t just affect people working in low-skill, highly routinized occupations. As Tom Meltzer notes, it will also increasingly threaten the jobs of lawyers, architects and doctors (even writers won’t be immune).
Of course, these fears may be overstated. Technology has repeatedly replaced jobs in the past – think of hand-weavers and phone operators – but people still found something new to do. Still, even if we don’t head into an era of mass unemployment, there seems little doubt that the second machine age will drive an even bigger wedge into the division of economic spoils, deepening still further the trend of rising income inequality in the coming decades.
According to a recent OECD paper, earnings inequalities in countries that are today regarded as relatively egalitarian, such as Italy, Sweden and Norway, will by 2060 match levels currently found in the U.S. Most of the gap in earnings will be concentrated between high and middle-income earners.
How can societies respond? The Dutch economist Jan Tinbergen ascribed much of the widening in income gaps to a “race between education and technology”. When education levels are rising relative to improvements in technology, the gap narrows; when they’re falling, it widens. That’s why so much policy discussion in this area, including the OECD paper, emphasises education investment, especially building strong foundations in children’s early years.
But with technology now racing so far ahead of education, societies will clearly need to consider other options. These could include, perhaps, even deeper income redistribution than we see today, effectively subsidising people to work less.
That’s not such a new idea: As long ago as the 1930s, the economist J.M. Keynes foresaw a time when economic wealth and automation allowed people to work a 15-hour week. More recently, Google’s Larry Page revived the idea in an interview with The Guardian: “In Page’s view robots and machines should be able to provide a ‘time of abundance’ where everyone’s basic needs could be met relatively easily.” How would you spend all that spare time? No doubt Amabot could recommend a few good reads to fill the long hours.
Divided We Stand – Why Inequality Keeps Rising (OECD, 2011)