As the World Economic Forum publishes the most extensive and in-depth report yet on artificial intelligence’s transformation of the financial services sector, Internet of Business editor Chris Middleton presents a 3,500-word breakdown of the document’s key findings and highlights.
Essential reading for all finance and technology professionals.
Internet of Business says
The financial services sector is in the vanguard of deploying artificial intelligence (AI) worldwide. However, the technology has the potential to be either a transformative and beneficial force, or a destabilising, even existential threat to the global financial system, according to the World Economic Forum.
This risks of economic contagion spreading via the technology are real, it says.
The 166-page sector analysis finds that the bonds that have historically held financial institutions together are weakening as a result of new technologies. This is creating new threats, new opportunities, and new centres of gravity where emerging and established capabilities are being combined in unexpected ways.
The operating models of financial institutions are being fundamentally reshaped, making financial institutions more specialised, leaner, more highly networked – and more dependent on the capabilities of technology players.
At the same time, the competitive dynamics of the financial ecosystem are being upended, it says, driving the formation of bifurcated markets in which scale and agility win at the expense of the traditional mid-scale players that make up much of the industry.
Tearing out an industry’s heart
This suggests that there will be a global market shakedown analogous to what is happening in the retail sector, where the poorly differentiated mid-market is being pulled apart by the polar forces of high-end, bespoke players and value-led, mass-market outlets.
Meanwhile, online giants such as Amazon are presiding over the creative destruction, with their own focus on data, personalisation, service, and loyalty allowing them to redefine traditional markets.
In financial services, the first-movers in the deployment of AI will be able to compound their leads, says the WEF, accelerating early data advantages to the benefit of both front and back offices, which will deeply influence firms’ strategic approach to alliances, infrastructure, and talent.
Yet shared prosperity in this fast emerging future is far from guaranteed, it cautions, and requires deeper cross-industry collaboration than exists today.
Indeed, institutions will need to balance their competitive impulses against new collaborative opportunities. AI offers financial institutions the ability to solve a host of shared problems that plague both the industry and its customers – but only if they come together to build shared solutions that benefit everyone, says the WEF.
So how will AI reshape financial services?
The new physics
AI is changing the fundamental ‘physics’ of the market: the core theme of the report. In particular, AI is altering the attributes necessary to build a successful business in financial services.
In the past, dominant institutions were built on: the scale of assets; mass production; exclusivity of relationships; high switching costs; and dependence on human ingenuity. But in the future, institutions will be built on: scale of data; tailored experiences; optimisation and matching of digital connections; high retention benefits; and the value of human performance being augmented by technology.
“This shift will have far-reaching consequences for the make-up of financial services, placing legacy business models under pressure from those whose businesses are built around these new attributes,” says the report.
However, financial institutions often lag behind other industries in recruiting and retaining people with the knowledge, skills and capabilities needed to create an AI-enabled workplace, adds the WEF.
So much for the elevator pitch, but what about the detail? The changing physics of financial services will transform the industry in the following specific ways, says the report:
Front- and back-office operations will look radically different. The shift from cost centre to profit centre will mean that institutions will turn AI-enabled operations into external services, both accelerating the rate at which these capabilities improve, and compelling others to become consumers of those services to avoid falling behind.
AI-enabled back-office processes will be improved more rapidly by offering them as a service to competitors, explains the WEF. This transformation of the back office will shift the competitive basis of firms towards the front office, changing the distribution of talent in the industry.
This will have the effect of removing operational efficiency as a competitive differentiator. Back-office processes will become increasingly uniform across financial services as most institutions will be consuming similar capabilities, forcing them to look for new areas of differentiation.
As a result, market power will shift to service providers. “As service-provider offerings become increasingly efficient, institutions that consume those services face high switching costs, allowing service providers to charge high margins,” says the report.
But this begs the question: How will institutions protect the competitive value of their proprietary data in a world of data sharing?
Inevitably, talent will also shift from financial institutions to service providers. As institutions primarily become consumers of capabilities, jobs will flow out of banks and insurers and be recreated in specialist providers.
Despite this, workforce engagement will be critical to the large-scale deployment of AI in financial institutions, adds the report.
While AI is often seen as a substitute for human talent – against the advice of providers, such as Microsoft and IBM, which see cognitive services as complementing human skills – establishing a workforce that views the implementation of AI as an opportunity will be critical for anything but marginal business transformations.
Achieving this will require an honest and collaborative relationship between an institution’s workers and its leadership, says the report.
Regulating the market
In all of these effects, market regulators will be constantly challenged. Data regulations will have transformative impacts on the shape and structure of financial markets, particularly where they require increased data portability.
“Existing regulatory regimes often struggle to keep pace with emerging technologies, creating roadblocks in the deployment of AI capabilities,” says the report. “Unlocking the full potential of AI will require financial institutions and their regulators to co-create new approaches and solutions.”
As some processes are shifted to shared utilities, institutions will seek to offload accountability to those central utilities as well, while the regulators will push to hold the original institutions accountable. This will create new tensions within the sector.
Efficient compliance will become a commodity. As institutions pool compliance services, they will participate on the same competitive plane, removing yet another competitive differentiator.
Yet in a market where every institution is vying for diversity of data, managing partnerships with competitors – and potential competitors – will be critical, but fraught with strategic and operational risks.
“Regulations governing the privacy and portability of data will shape the relative ability of financial and non-financial institutions to deploy AI, thus becoming as important as traditional regulations to the competitive positioning of firms,” says the report.
Global data regulations are themselves undergoing a period of unprecedented change, as governments move to adopt new rules to protect and empower citizens.
For example, the revised Payment Services Directive (PSD2) of the European Union came into force in January 2018, with the aim of enabling more innovative payments across Europe.
In conjunction with the General Data Protection Regulation (GDPR), this means institutions have to balance requirements to share data with third parties against the risk of substantial penalties in cases where data is mishandled.
The UK – which has adopted GDPR locally – has been one of the first jurisdictions to adopt open banking as a mandate across financial services. This push started in 2016, with a report by the Competition and Markets Authority, which found “older and larger banks do not have to compete hard enough for customers’ business, and smaller and newer banks find it difficult to grow”.
While China does not have a comparable open banking framework, its existing regulatory regime has been conducive to fintech companies and third-party providers, according to the WEF.
In China, the proliferation of APIs (both public and private) between technology companies and incumbent institutions (eg WeChat and Alipay) have allowed these platforms to become interoperability layers that facilitate the flow of data between institutions.
In other parts of the world, governments are considering radical changes to their data regimes. Australia, Singapore, Canada, and Iran, among others, are actively considering different forms of the open banking model, mirroring some of the steps taken by the EU and the UK.
In this regard, the US remains an anomaly. There, data-sharing alliances are more ad hoc than mandated, with individual banks building bilateral relationships with data aggregators. As yet, national regulators have not signalled their intention to implement frameworks similar to the UK’s and the EU’s.
However, Congress has been listening to testimony from large technology companies, such as Facebook, Google, and Twitter, on the topics of data privacy and security. That said, many such companies oppose the data privacy regulations introduced recently in California, which may yet become de facto US standards.
Those rules come into force in California – home of Silicon Valley – in 2020, and it is expected that advertising- and data-driven tech companies, such as Google and Facebook, will attempt to water down the regulations before then.
Telecoms providers also oppose the rules. Others, such as Apple, Microsoft, Salesforce.com, SugarCRM, and Box, have stated their support for GDPR-style regulation globally – companies that are not reliant on selling customer data to advertisers.
Either way, the evolution of data regulations worldwide will be the critical driver in determining the roles and relative positioning of different players in financial services.
Growing cyber-risks present further operational challenges. Institutions must develop strategies to mitigate the increasing risk of abuse and leakage of confidential information at both customer and transaction level, as well as the increased risk-sharing of sensitive, competitive information.
What about the customer?
AI development must serve the needs of customers and remain in the best interest of society, says the WEF. Its deployment should enable a fairer, more accessible, and more stable financial system – but only if institutions can resist their own worst impulses in what many, culturally, see as a high-stakes gamble to win (as the 2008-09 crash amply demonstrated).
The risk of bias and discrimination entering the system and helping misguided firms act against the interests of some groups is real. Maintaining a human-centric approach to the deployment of AI will be critical to ensuring it serves the interests of individuals and society at large, as well as private investors.
The customer will be constantly challenged as financial institutions battle for their loyalty. As past methods of market differentiation are eroded, AI will present an opportunity for institutions to escape the ‘race to the bottom’ in price competition by introducing new ways in which they can to distinguish themselves from their competitors.
“The ability of institutions to optimise financial outcomes by tailoring, recommending, and advising customers better will allow them to compete on value offered,” says the report.
For example, the Royal Bank of Canada (RBC) has invested in diversifying its digital platform. This has allowed it to incorporate a broader range of services. One example of this is the company piloting a forecasting tool for car dealers to predict demand for vehicle purchases based on customer data.
Financial institutions in the UK are revisiting their core value propositions too, now that open banking has come into effect. For example, Lloyds Banking Group’s investment of $4.1 billion a year is positioning the company to combine banking and insurance services, along with new API-enabled propositions.
This is supplemented by a new focus on AI capabilities to transform both customer offerings and business operations. Lloyds’ aim is to be a platform or ecosystem provider and a trusted guardian of data in an age of multiple providers.
Meanwhile, Chinese insurance giant Ping An has invested aggressively in building a suite of partners, services, and products to achieve what the WEF calls “a massive scale of data beyond just financial services”.
Through its suite of apps in finance, medicine, cars, and housing, Ping An has been able to take advantage of data from over 880 million users, 70 million businesses, and 300 partners to power its core business.
Companies in China have access to far more data on which to train AI systems than any of their competitors, particularly as the country introduces its mandatory social ratings and citizen surveillance scheme in 2020. This poses an ethical and operational technology challenge to their counterparts in the West – as Google is currently finding.
Online, people increasingly favour engagement over exposure, and so prefer brands that create experiences for them that are relevant and valuable. This opens up opportunities in financial services for giants such as Amazon, which can harness this behaviour by developing products and services that cross sector boundaries.
Large tech firms, such as Amazon and Google, have distinct advantages in attracting new customers, says the WEF. Their core strategies have long been highly focused on capturing user attention and data by offering free products and services, or low-cost loss leaders (such as Amazon’s Echo and Dot devices).
In this landscape, financial services companies need to radically alter the way they work and the types of products they develop in order to compete for customers.
As a result, institutions need detailed insight into customer behaviour – both inside and outside financial services – and will need to be highly focused on delivering what customers actually want. “This means getting to know customers beyond just their finances and looking for opportunities to improve their day-to-day lives,” says the report.
Inevitably, therefore, product development and a willingness to experiment will be critical skills for institutions, alongside a detailed understanding of – and commitment to – data privacy.
To succeed, market incumbents must harvest new resources and establish new ways of working – including technical AI skills, product development capabilities, new datasets, and cultures of innovation and experimentation.
And they will need to foster trust: an uphill battle in many cases, in the wake of the 2008-09 crash, publicly funded bank bailouts, and austerity policies.
However, all of this begs the questions of what “the natural equilibrium of price” will be in a platform economy, says the report, and what margins institutions can expect to earn without the old market differentiations they used to rely on.
Margins will be squeezed for institutions that fail to develop new differentiators. They will face an uphill battle to maintain their profitability, especially after traditional metrics such as price and speed have been normalised by technology.
Some may go to the wall in the industry-wide shakedown.
“AI can deliver a radically reimagined customer experience by allowing customers’ finances to run themselves, and acting as a trusted adviser in moments of need,” says the report.
However, an emerging risk is that ‘self-driving finance’ will upend existing competitive dynamics, pushing returns to the customer experience owner, while commoditising all other providers.
Many future customer experiences will be centred on AI, which will automate much of customers’ financial lives and seek to improve their financial outcomes.
However, AI will also push market structures to extremes, warns the report, favouring scale players and agile innovators at the expense of mid-sized firms, as AI reduces search and comparison costs for customers.
And something not mentioned in the report is that if AI can automate fraud detection – a big plus for the technology in the WEF’s estimation – then, logically, it may also automate fraud itself, or make it harder to detect.
Some economists have already documented the power of information technology to drive firms to market extremes, says the WEF. For example, Erik Brynjolfsson defined this phenomenon as being the result of the simultaneous creation of long tails in product availability, combined with a ‘winner-takes-all’ superstar structure.
AI accelerates this phenomenon by magnifying the impact of several market drivers, cautions the WEF: search and database technology; personalisation; making niche products cheaper to build; and improving returns to scale.
Market extremes are already developing in asset management, for example. Players such as Vanguard have aggressively pursued a low-fee offering by taking advantage of economies of scale. Meanwhile in the exchange traded fund (ETF) market, automated platforms – aka robo advisers – have increased the ability to optimise investments and fees seamlessly, helping scale players to win customers.
At the other end of the spectrum, a new class of funds has emerged. These are led by entrepreneurs who use AI and quantitative investing to deliver differentiated return profiles. These can be scaled rapidly without substantially increasing their costs or human capital footprints.
Again, polar forces are pulling the middle apart and the risks to mid-tier firms are ramping up in this new market, warns the WEF.
Such institutions typically lag behind others in technology investment. A recent survey by DBR Research found that 48 percent of banks with more than $50 billion in assets have deployed an AI solution, compared to just seven percent of banks with between $1 billion and $10 billion in assets.
One reason for this is that mid-tier firms have tighter investment budgets and rely more on technology vendors, resulting in limited internal capacity for innovation and a reduced ability to move quickly.
As institutions get pushed to market extremes, firms’ structures and core competencies at either end of the spectrum will begin to look radically different.
As a result, incumbents will be placed in a double bind: they cannot resist entering data partnerships, but those partnerships may threaten their competitive positions.
The social and employment angle
Overall, AI raises critical challenges for society and employers to meet, says the WEF.
For example, skills transformation will be the most challenging speed limits on institutions’ implementation of AI, putting at risk the competitive positioning of both firms and countries that fail to address skills needs alongside their technology investments.
“Financial institutions need help to conceptualise the union of talent and technology as they aspire to move forward and achieve AI-driven growth,” says the report.
It quotes the example of economic historian Robert C. Allen, who has observed that the current wave of transformation – often dubbed the Fourth Industrial Revolution – bears a strong resemblance to the Industrial Revolution of the 19th Century.
In particular, the reaction of workers to the increased use of AI is reminiscent of ‘Engel’s Pause’, a phenomenon where a new technology results in a worsening of circumstance followed by increased prosperity.
In the immediate term, there is certainly a risk that unemployment rates will rise: a phenomenon outlined in numerous recent reports on robotics, AI, and automation.
As AI digitises and automates routine roles, there will be a net displacement of people who are unable to rejoin the workforce with their existing skills. This spike in unemployment will create the perception that AI is worsening society, rather than improving it.
However, in the near term, the shift in talent will drive growth, but this will have the knock-on effect of reinforcing the impression that financial gains for some are deepening economic disparity.
A world of AI-enabled ‘haves’ and technologically disenfranchised ‘have nots’ may emerge, with skills being the core issue, alongside technology access and investment.
For society to overcome these harms and embrace AI, the technology must be developed to a level where it is pervasive, says the WEF. Although this may be technologically feasible, there will need to be a shift in priorities to encourage innovation and longer-term thinking, both within formal institutions and outside them.
This is ironic, as many technology companies have found themselves battling the short-term approach of Wall Street to their own technology transformations, as the following two Internet of Business reports explain:-
In the longer term, relationships between society and business will need to be redesigned, says the WEF – and this comprehensive redesign must benefit everyone, and not just private companies.
The challenge for some within the sector, therefore, is to address a critical question, post 2009: do they serve society, or their own selfish interests?
In summary, the benefits of AI will be maximised only if societal structures and processes adapt to support new ways of working.
By aligning with and enabling AI innovations – while working to address social challenges – both institutions and social structures are “likely to see the prosperity that justifies AI-driven growth”, says the WEF.
But new ethical dilemmas will emerge on that difficult journey.
AI will force a collaborative re-examination of the principles and supervision techniques that address the ethical challenges and regulatory uncertainties that come with the technology.
For example, the enigmatic nature of some ‘black box’ solutions is a challenge in itself. Understanding the technology in greater depth is critical to detecting and preventing models that discriminate against or exclude marginalised groups and individuals.
However, the WEF warns that there is little real understanding of AI, or agreement on a definition, within the industry, adding that most coverage of the issue concerns the technology, and not its strategic implications for the business. This gap in coverage is dangerous.
As AI takes an increasingly critical role in the day-to-day operations of the financial system, it poses a new source of systemic, as well as ethical, risk. This has the potential to disrupt national and global economies, necessitating new controls and responses.
“Without proper oversight, AI innovation could introduce new systemic risks into the financial system and increase the threat of contagion,” warns the WEF.
“AI is likely to have a transformative effect on the global financial system, so the task of the ecosystem will be to maximise the benefits, while mitigating the harms.”