Big techs in finance raise complex policy trade-offs

Large companies whose primary activity is digital services (big techs) have entered the financial sector. After starting in payments, big techs have increasingly branched out into credit, insurance and wealth management – and have grown rapidly. In China, two big techs jointly account for 94% of the mobile payments market, and play a significant role in other financial services such as digital credit. Leading big techs’ market capitalisations surpass those of the largest banks.  

Why have they become so large? Big tech business models rest on enabling direct interactions among a large number of users. This may be in e-commerce – such as Alibaba, Amazon or Mercado Libre; social media – such as Facebook, Tencent or Kakao; telecommunications – such as Safaricom or MTN; or search – such as Google or Baidu.      

An essential by-product of their business is the massive amount of user data they generate and collect. They exploit natural network effects, generating further user activity and data which can be leveraged to improve their product offering. As an example, payments services generate transaction data, network externalities facilitate interaction among users, and all this helps big techs to introduce other activities, such as credit or wealth management, generating more engagement with existing users and attracting new ones. 

Policy trade-off impacts

A recent paper analysed the entry of big tech firms into financial services and how this impacts the existing trade-offs among the public policy objectives of: (i) financial stability and market integrity; (ii) efficiency and competition; and (iii) data privacy and consumer protection. We can elaborate such trade-offs around a policy triangle (see the graphic below). 

We start with the “traditional” stability-competition trade-off (the red arrow). Regulators have long debated the relationship between competition and financial stability. One school of thought emphasises that competition reduces banks’ profits and overall franchise value and therefore is not always optimal or conducive to financial stability. This approach implicitly views concentration, market power, and potentially higher-end user costs, as an acceptable price to pay for financial stability. 

A second school of thought argues that competition in the financial sector increases innovation, choice and efficiency, improving the quality of financial services and reducing their costs (albeit in part by reducing market power and redistributing profits from providers to consumers). Entry, exit and contestability foster beneficial competition and reduce incumbents’ market power and reduce incumbents’ market power. How these forces impact the market depends on other features, of course, including regulation. 

Source: Feyen et al (2021). Adapted from Petralia et al (2019) and Carletti et al (2020) (graphic redrawn).

A changing paradigm

The entry of big tech into finance may change this model; in particular, their entry may decrease competition. Companies with market dominance in their core business could translate that dominance into complementary financial services, making entry a source of increased concentration and market power. Such control may also generate conflicts of interest and potential market abuse when big techs’ platforms become the main distribution channel for their competitors (e.g. banks). 

While competition and more efficient solutions may often benefit consumers, trade-offs between efficiency/competition and privacy/consumer protection arise. This is represented by the blue arrow. In many jurisdictions, big tech providers may not be subject to the same regulatory oversight that protects financial services consumers. Big tech mobile money competes with bank payments services in terms of price and availability, but more personal data might be exposed to mobile money and digital credit providers than to banks.

Data and power 

As data become an even more important source of market power, there are tensions around the ownership and use of data. Data can in principle be used many times and by any number of firms simultaneously, without being depleted – this is the so-called ‘non-rivalry’ characteristic of data. Credit bureaus operate on this principle.

However, unrestricted sharing of data can also harm individuals. For example, open access to personal data represents a loss of privacy, and can allow for identity theft, reputational damage and the manipulation of behavioural biases to sell consumers products that are not in their own interests. On the other hand, allowing data producers to maintain a monopoly over that data presents challenges as well. It could impede consumers from switching providers, or enable price discrimination or algorithmic exclusion. 

Big tech companies are also very efficient in pricing using big data. They can divide a customer population into very fine subcategories – each charged a different price, representing the maximum price each individual is willing to pay. By extracting more of the consumer surplus from those willing to pay more, prices can also be reduced for those able to pay less, potentially creating a more inclusive offering. Yet such fine price discrimination may overlap with protected categories such as gender or race. Regulators need to balance innovation and efficiency with consumer protection that might dampen certain innovations. 

Data sharing can alleviate problems related to asymmetric information, and adequate data are crucial for monitoring financial stability and integrity. This potentially introduces a new trade-off between privacy (and consumer protection more generally) on the one hand and financial stability and market integrity on the other. This trade-off is represented by the green arrow. 

For example, in the credit market, there is ample evidence that more data can improve stability. Credit reporting systems allow safe lending to borrowers who had previously been priced out of the market, resulting in higher aggregate lending and furthering financial inclusion. In the case of credit reporting, the data can only be accessed by licensed entities, only upon customer consent and only for authorised purposes. 

In the case of big techs, the data they capture are far more granular, so it is important to have safeguards for privacy. At the same time, detailed information on all parties in a transaction could support market integrity. Anti-money laundering (AML) and combatting the financing of terrorism (CFT) practices could benefit from machine-learning applications on big data. Balancing privacy and integrity goals will require societal dialogue, and will likely require legislation. 

Conclusions

The rise of big techs – alongside the rapid growth of fintechs and embedded finance – underscores how rapidly digital innovation can disrupt markets and put competitive pressure on incumbents. This brings efficiency and financial inclusion, but also new concerns for policy. As innovation in finance accelerates, policy and regulation must keep pace, both to regulate the innovators, and to accord incumbents the flexibility they need to compete. 

The current framework for regulating financial services follows an activities-based approach where providers must hold licences for specific business lines. To maintain a level playing field and mitigate regulatory arbitrage, treating the same activities and risks similarly is often proposed. However, in some cases, addressing the new policy challenges that arise due to the unique risk profile of new players calls for specific entity-based rules, as proposed in several key jurisdictions – notably the EU, China and the US.

The new trade-offs between the policy objectives in the triangle also call for more coordination. At the domestic level there is a need for more coordination between national authorities overseeing competition, financial regulation, and data and consumer protection. As the digital economy expands across borders, there is also a need for international coordination of rules and standards across all three areas. 

Written by Erik Feyen (World Bank), Jon Frost (Bank for International Settlements (BIS) and Cambridge Centre for Alternative Finance (CCAF)), Leonardo Gambacorta (BIS and Centre for Economic Policy Research (CEPR)), Harish Natarajan (World Bank) and Matthew Saal (International Finance Corporation (IFC)).

In collaboration with the Centre for Economic Policy Research and VoxEU.

The views expressed herein are those of the authors and do not necessarily reflect the views of the Bank for International Settlements or the World Bank Group.