Opportunities and risks from Big Tech’s entry into finance

The provision of financial services by large technology companies can increase the efficiency of and access to such services, but also generate risks relating to financial stability, market competition and use of data, says the Bank for International Settlements.

by Chakravarthi Raghavan

GENEVA: The entry of large technology firms such as Alibaba, Amazon, Facebook, Google and Tencent into financial services, including payments, savings and credit, could make the sector more efficient and increase access to these services, but also introduces new risks, according to the Bank for International Settlements (BIS).

The views of the Basel-based BIS, which is commonly described as the central bank for the world’s central banks, are in Chapter III of its 2019 Annual Economic Report. Titled “Big tech in finance: opportunities and risks”, Chapter III was released on 23 June, in advance of the full report which was published on 30 June.

In this special chapter, the BIS notes that these companies, or “big techs”, offer many potential benefits, including enhanced efficiency of financial services provision, facilitating financial inclusion and promoting associated gains in economic activity.

However, big techs’ entry into finance introduces additional elements into the risk-benefit equation. Some are old issues of financial stability and consumer protection in new settings, but a new element is big techs’ access to data from their existing platforms. This could spark rapid change in the financial system through the emergence of dominant players that could ultimately reduce competition.

The role of big tech in finance thus raises issues that go beyond traditional financial risks. Tackling these requires striking a balance between financial stability, competition and data protection. Regulators need to ensure a level playing field, taking into account big techs’ wide customer bases and particular business models.

As big techs’ move into financial services accelerates, expanding beyond regulatory perimeters and geographical borders, policymakers will need institutional mechanisms to help them work and learn together. Coordination among authorities – national and international – is crucial to sharpening and expanding their regulatory tools, the BIS stressed.

[The advance release of the chapter came in the wake of the announcement on 18 February by Facebook of its entry into the payments and money transfer industry with its new venture, a cryptocurrency called Libra that could be used to send money around the world. Libra will be controlled, administered and managed by Facebook and 27 partners (some of the world’s largest corporations including Visa, Uber and Vodafone) through an independent association based in Geneva, Switzerland, with a membership fee of $10 million.

[The move by Facebook brought some quick responses from key national decision-makers. French finance minister Bruno Le Maire underlined that Libra would not be allowed to supplant government-backed currencies. The Bank of England governor Mark Carney warned that it could become “instantly systemic” and would consequently be subject to heightened regulatory scrutiny.

[Financial Times columnist Martin Sandbu says that “implicit in Facebook’s plans is not just a capture of the banking industry, but a privatization of monetary policy: a democratically abhorrent prospect in principle, and a power that there is absolutely no reason to think Facebook would discharge responsibly in practice.”

[In another comment, Facebook co-founder Chris Hughes (who is no longer with the company) points out that if even modestly successful, Libra would hand over much of the control of monetary policy from central banks to these private companies. Libra would insert a powerful new corporate layer of monetary control between central banks and individuals. Inevitably, these companies will put their private interests, profits and influence ahead of public ones.

[If Libra works as planned, says Hughes, “[h]undreds of millions of people around the world will be able to send money across borders as easily as they send a text message”. Such an ability “will disrupt and weaken nation states by enabling people to move out of unstable local currencies and into a currency denominated in dollars and euros and managed by corporations”. Such a liquid, stable currency would be attractive to many in emerging markets, “threaten[ing] the ability of emerging market governments to control their monetary supply, the local means of exchange and, in some cases, their ability to impose capital controls”.]

Entry into finance

Compared with such strongly critical and antagonistic views, the views of the BIS economists appear somewhat ambivalent.

They note that technology firms such as Alibaba, Amazon, Facebook, Google and Tencent have grown rapidly over the last two decades. Their business model rests on enabling direct interactions among a large number of users. An essential by-product of their business is the large stock of user data which are utilized as input to offer a range of services that exploit natural network effects, generating further user activity. Increased user activity then completes the circle, as it generates yet more data.

Building on the advantages of the reinforcing nature of the data-network activities loop, some big techs have ventured into financial services, including payments, money management, insurance and lending. As yet, financial services are only a small part of their business globally. But given their size and customer reach, big techs’ entry into finance has the potential to spark rapid change in the industry.

Their low-cost business can easily be scaled up to provide basic financial services, especially in places where a large part of the population remains unbanked. Using big data and analysis of the network structure in their established platforms, they can assess the riskiness of borrowers, reducing the need for collateral to assure repayment. As such, big techs stand to enhance the efficiency of financial services provision, promote financial inclusion and allow associated gains in economic activity.

At the same time, their entry into finance introduces new elements in the risk-benefit balance. Some are old issues of financial stability and consumer protection in new settings. In some settings, such as the payment system, big techs have the potential to loom large very quickly as systemically relevant financial institutions. Given the importance of the financial system as an essential public infrastructure, the activities of big techs are a matter of broader public interest that goes beyond the immediate circle of their users and stakeholders.

There are also important new and unfamiliar challenges that extend beyond the realm of financial regulation as traditionally conceived. Big techs have the potential to become dominant through the advantages afforded by the data-network activities loop, raising competition and data privacy issues.

Public policy needs to build on a more comprehensive approach that draws on financial regulation, competition policy and data privacy regulation. The aim should be to respond to big techs’ entry into financial services so as to benefit from the gains while limiting the risks. As the operations of big techs straddle regulatory perimeters and geographical borders, coordination among authorities, national and international, is crucial.

The activities of big techs in finance are a special case of broader fintech innovation. Fintech refers to technology-enabled innovation in financial services, including the resulting new business models, applications, processes and products. While fintech companies are set up to operate primarily in financial services, big tech firms offer financial services as part of a much wider set of activities. Their core businesses are in information technology and consulting (e.g., cloud computing and data analysis), accounting for around 46% of their revenue, while financial services represent about 11%. While big techs serve users globally, their operations are mainly located in Asia and the Pacific and North America. Their move into financial services has been most extensive in China, but they have also been expanding rapidly in other emerging market economies, notably in Southeast Asia, East Africa and Latin America.

Financial services offered

In offering financial services, big techs both compete and cooperate with banks. Thus far, they have focused on providing basic financial services to their large network of customers and have acted as a distribution channel for third-party providers, e.g., by offering wealth management or insurance products.

Financial services are a small part of big tech business. Payments were the first financial service big techs offered, mainly to help overcome the lack of trust between buyers and sellers on e-commerce platforms. Over time, big techs’ payment services have become more widely used as an alternative to other electronic payment means such as credit and debit cards.

Big techs’ payment platforms currently are of two distinct types: the “overlay” and proprietary systems. In the overlay system, users rely on existing third-party infrastructures, such as credit card or retail payment systems, to process and settle payments. Big techs’ payment platforms compete with those provided by banks, but they still largely depend on banks.

Overlay systems are used more commonly in the United States and other advanced economies, while proprietary payment systems are more prevalent in jurisdictions where the penetration of other cashless means of payment, including credit cards, is low. This helps explain the large volume of big tech payment services in China: 16% of GDP, dwarfing that elsewhere. More generally, big techs have made greater inroads where the provision of payments is limited and mobile phone penetration high.

Remittance services, and cross-border retail payments more broadly, are another activity ripe for big techs’ entry. These cross-border transactions, however, still rely on a correspondent banking network and require collaboration with banks.

Big techs use their wide customer network and brand name recognition to offer money market funds and insurance products on their platforms, capitalizing on their payment services. Their one-stop shops aim to be more accessible, faster and more user-friendly than those offered by banks and other financial institutions.

On big tech payment platforms, customers often maintain a balance in their accounts. To put these funds to use, big techs offer money market funds (MMFs) as short-term investments. In China, MMFs offered through big tech platforms have grown substantially since their inception. At end-2018, total MMF balances related to big techs amounted to CNY2.4 trillion ($360 billion), only about 1% of bank customer deposits or 8% of outstanding wealth management products.

Some big techs have started to offer insurance products, using their platforms mainly as a distribution channel for third-party products, including auto, household liability and health insurance. In the process, they collect customer data, which they can combine with other data to help insurers improve their marketing and pricing strategies.

Building on their e-commerce platforms, some big techs have ventured into lending, mainly to small and medium-sized enterprises (SMEs) and consumers. Loans offered are typically credit lines, or small loans with short maturity (up to one year).

The (relative) size of big tech credit varies greatly across countries. While total fintech (including big tech) credit per capita is relatively high in China, Korea, the United Kingdom and the United States, big techs account for most fintech credit in Argentina and Korea. The uneven expansion of total fintech credit appears to reflect differences in economic growth and financial market structure.

Despite its substantial recent growth, total fintech credit still constitutes a very small proportion of overall credit. Even in China, with the highest amount of fintech credit per capita, the total flow of fintech credit amounted to less than 3% of total credit outstanding to the non-bank sector in 2017.

Big techs’ relatively small lending footprint so far has reflected their limited ability to fund themselves through retail deposits. Big techs have some options to overcome this constraint.

One is to establish an online bank, though in some countries, regulatory authorities restrict the opening of remote (online) bank accounts. More recently, however, these banks have started to issue “smart deposits” that offer significantly higher interest rates than other time deposits and the possibility of early withdrawal at a reduced rate.

A second option is to partner with a bank. Big techs can provide the customer interface and allow for quick loan approval using advanced data analytics; if approved, the bank is left to raise funds and manage the loan. This option can be attractive to big techs as their platforms are easily scalable at low cost and may also be profitable for banks, as they can gain an extra return, despite providing lower-value-added services.

A third option is to obtain funds through loan syndication or securitiza-tion – already a common strategy among fintech firms.

Big techs’ DNA

Big techs have typically entered financial services once they have secured an established customer base and brand recognition. Their entry into finance reflects strong complementarities between financial services and their core non-financial activities, and the associated economies of scope and scale.

Data analytics, network externalities and interwoven activities (“DNA”) constitute the key features of big techs’ business models. These three elements reinforce each other. Financial services both benefit from and fuel the DNA feedback loop. Offering financial services can complement and reinforce big techs’ commercial activities.

Big techs’ DNA can lower the barriers to provision of financial services by reducing information and transaction costs, and thereby enhance financial inclusion. However, these gains vary by financial service and could come with new risks and market failures.

Besides the cost of raising funds, the cost of lending is closely tied to the ex ante evaluation of credit risk and the ex post enforcement of loan repayments. The information cost (of lending and ensuring loan repayments) can sometimes be so prohibitive that banks refrain from serving borrowers or do so only at very high spreads. Most at risk from exclusion are borrowers who lack basic documentation or are difficult to reach.

Also, many SMEs in developing economies do not meet the minimum requirements for a formal bank loan application as they often do not have audited financial statements. As a result, big techs can have a competitive advantage over banks and serve firms and households that otherwise would remain unbanked. They do so by tapping different but relevant information through their digital platforms.

The cost of enforcing loan repayments is an important component of total financial intermediation cost. To reduce enforcement problems, banks usually require borrowers to pledge tangible assets, such as real estate, as collateral to increase recovery rates in the case of default. Banks also spend time and resources monitoring their clients’ projects.

Big techs can address these issues differently. When a borrower is closely integrated in an e-commerce platform, for example, it may be relatively easy for a big tech to deduct the (monthly) payments on a credit line from the borrower’s revenues that transit through its payment account. Big techs could also enforce loan repayments by the simple threat of a downgrade or an exclusion from their ecosystem if in default.

Big techs’ role in financial services brings efficiency gains and benefits, but also has the potential to generate new risks and costs associated with market power. Dominant platforms can consolidate their position by raising entry barriers, exploit their market power and network externalities to increase user switching costs or exclude potential competitors. Other anti-competitive practices could include “product bundling” and cross-subsidizing activities.

Another, newer type of risk is the anti-competitive use of data. Given their scale and technology, big techs have the ability to collect massive amounts of data at near zero cost. This gives rise to “digital monopolies” or “data-opolies” that can engage in price discrimination and extract rents. They may also use their data to identify the highest rate the borrower would be willing to pay for a loan or the highest premium a client would pay for insurance.

Regulatory response

Traditionally, financial regulation is aimed at ensuring the solvency of individual financial institutions and the soundness of the financial system as a whole, and incorporating consumer protection goals. When big techs’ activity falls squarely within the scope of traditional financial regulation, the same principles should apply to them.

However, two additional features make the formulation of the policy response more challenging. First, big techs’ activity in finance may warrant a more comprehensive approach that encompasses not only financial regulation but also competition and data privacy objectives. Second, even when the policy goals are well articulated, the specific policy tools should actually be shown to promote those objectives. This link between ends and means should not be taken for granted.

A well-functioning financial system is a critical public infrastructure, and banks occupy a central place in that system through their role in the payment system and in credit intermediation. Banks’ soundness is a matter of broader public interest beyond the narrow group of direct stakeholders (their owners and creditors).

For this reason, banks are subject to regulations that govern their activities, and market entry is subject to strict licensing requirements. Likewise, when big techs engage in banking activities, they are rightfully subject to the same regulations that apply to banks. The aim is to close the regulatory gaps between big techs and regulated financial institutions so as to limit the scope for regulatory arbitrage through shadow banking activities.

Accordingly, regulators have extended existing banking regulations to operations of big techs in payments, such as know-your-customer rules designed to prevent money laundering and other financial crimes. In addition to existing rules being extended to big techs, new rules may be warranted in those cases where big techs have wrought structural changes that take them outside the scope of existing financial regulation.

Prudential regulators have turned their attention to specific market segments, notably in the payment system, where big techs may have already become relevant from a systemic perspective. Where rapid structural change has outrun the existing letter of the regulations, a revamp of those regulations will be necessary. The general guide is to follow the risk-based principle and adapt the regulatory toolkit in a proportionate way.

New challenges

When the objectives of policy extend beyond the goals of traditional financial regulation into competition and data privacy, new challenges present themselves. Even when the objectives are clear and uncontroversial, selecting the policy tools to secure the objectives requires taking account of potentially complex interactions.

To navigate the new, uncharted waters, regulators need a compass that can orient the choice of potential policy tools. These tools can be organized along the two dimensions. The first spans the range of choices over how much new entry of big techs into finance is encouraged or permitted. The second dimension spans choices over how data are treated in the regulatory approach, ranging from a decentralized approach, endowing property rights over data to customers, to a restrictive approach placing walls and limits on big techs’ use of such data.

Current practices cover a broad territory. The choices involve decisions by three types of official actors: financial regulators, competition authorities and data protection authorities. The choice of policy tools has been quite heterogeneous across jurisdictions.

Traditionally, public policy on entry into the banking industry has been influenced by two divergent schools of thought on the desirability of competition in banking. One view is that the entry of new firms in the banking sector is desirable as it fosters competition and reduces incumbents’ market power. On the other side of the debate is the school of thought emphasizing that a concentrated – or less competitive – banking sector is desirable because it is conducive to financial stability.

However, the relationship between entry and effective competition is far from obvious when the DNA feedback loop is taken into account. New entry may not increase market contestability – and competition – when big techs are envisaged as the new entrants. Big techs can establish and entrench their market power through their control of key digital platforms, e.g., e-commerce, search or social networking.

Such control may generate outright conflicts of interest and reduce competition when both big techs and their competitors (e.g., banks) rely on these platforms for their financial services. Also, a big tech could be small in financial services and yet rapidly establish a dominant position by leveraging its vast network of users and associated network effects. In this way, the rule of thumb that encouraging new entry is conducive to greater competition can be turned on its head.

The traditional focus of competition authorities on a single market, firm size, pricing and concentration as indicators of contestability is not well suited to the case of big techs in finance. Competition authorities may need to adapt their paradigms.

Some jurisdictions (e.g., the European Union, Germany, India, the United Kingdom and the United States) have recently been upgrading their rules and methodologies for assessing anti-competitive conduct. In India, for example, the main e-commerce platforms are prohibited from selling products supplied by affiliated companies on their websites to avoid potential conflicts of interest.

By tying market power to the extensive use of customer data, big techs’ DNA feedback loop creates a new nexus between competition and data. Wide access to data can in principle be beneficial. Digital data are a non-rival good – i.e., they can be used by many, including competitors, without loss of content. Moreover, since data are obtained at zero marginal cost as a by-product of big techs’ services, it would be socially desirable to share them freely.

The issue, therefore, is how to promote data-sharing. Currently, data ownership is rarely clearly assigned. For practical purposes, the default outcome is that big techs have de facto ownership of customer data, and customers cannot (easily) grant competitors access to their relevant information. This uneven playing field between customers and service providers can be remedied somewhat by assigning data property rights to the customers.

However, the mapping between the policy tools and the ultimate outcomes is more complex in the case of big techs. Given the network effects underlying competition, the competitive playing field may be levelled out more effectively by placing well-designed limits on the use of data. Introducing some additional rules regarding privacy could increase effective competition, because the addition of such limitations on the use of data could curb big techs’ exploitation of network externalities.

This policy choice along the data usage dimension has taken centrestage in the debate on big techs. The underlying arguments that bear on the available choices are reflected in the policies recently adopted in a number of jurisdictions.

Two particular examples are the various forms of open banking regulations that have been adopted around the world, and the EU’s General Data Protection Regulation (GDPR). To the extent that they entail the transfer of data ownership from big techs to customers, both regulations can be seen as measures intended to facilitate greater effective market contestability.

At the same time, some of the new regulations also limit the scope of data-sharing. The rationale for limiting the use of data rests on a number of considerations. Not all types of data are relevant for the provision of financial services. To assess a borrower’s creditworthiness, for example, a lender may not necessarily need to know their social habits or travel plans. Moreover, not all types of service providers should be given access to their customers’ financial data.

Accordingly, open banking regulations selectively restrict the range of data that can be transmitted (e.g., financial transaction data), as well as the type of institutions among which such data can be shared (e.g., accredited deposit-taking institutions). Similarly, the GDPR requires customers’ active consent before a firm can use their personal data.

Both types of restrictions can be seen as barriers to big techs’ entry into finance. More drastic approaches involve outright restrictions on the processing of user data, such as the recent rule by Germany’s competition authority that prohibits a prominent social network (Facebook) from combining its user data with those it collects from its affiliated websites and applications. Where to draw the line is an issue that involves not just economics, but also society’s privacy preferences.

In the face of the rapid and global digitization of the economy, policyma-kers need institutional mechanisms to stay abreast of developments and to learn from and coordinate with each other. Coordination among authorities is crucial, at both the national and the international level.

First, there is a need for coordination of national public policies. Second, as the digital economy expands across borders, there is a need for international coordination of rules and standards (e.g., for data exchange). To prevent those differences from leading to conflicting actions, policymakers not only need a new compass but also need to find the right balance of public policy tools. (SUNS8932)

Third World Economics, Issue No. 683, 16-28 February 2019, pp2-5, 11