In 2009, in the midst of the global financial crisis, former Federal Reserve Chairman Paul Volker noted prominently that the socially productive economic innovation of the previous 20 years was only the automated teller machine (ATM). From mobile payment platforms to internet banking and peer-to-peer lending today one wonders what Volker will do with the tsunami of digitally enabled financial innovations.
Volcker can be assured that: Like the humble ATM, these innovations have very obvious advantages in terms of reducing transaction costs. But as a critic of the big financial institutions, Volker is also concerned about some big technology companies entering the field. Their names are well known as far as their services are concerned: e-commerce behemoth Amazon in the US, messaging company Kakao in Korea, online auction platform Mercado Libre in Latin America and Chinese technology giants Alibaba and Tencent.
They now do everything related to finance. Amazon has expanded its lending to small and medium-sized businesses. Cacao offers a full range of banking services. Alibaba’s Ant Financial and Tencent’s WeChat provide a corncopia of financial products that have expanded so rapidly that they have become a target for Chinese government repression.
The challenges for regulators are obvious. Like M-Pesa in Kenya, if a single company makes channel payments to more than one country’s population, for example, its failure will crash the entire economy. Regulators should therefore be very careful about operational hazards. They should be concerned about the protection of customer data — not just financial data, but also other personal data that big tech companies keep secret.
In addition, Big Tech companies have a better ability to target the behavioral bias of their customers, due to their ability to cultivate and analyze data on consumer preferences. While that bias may cause some borrowers to take a higher risk, Big Tech would have very little reason to care if it simply provided technology and expertise to the partner bank. The moral catastrophe is that China regulators are now required to use their own balance sheets with the country’s Big Tex.
Governments have laws and regulations to prevent financial product providers from discriminating on the basis of race, gender, ethnicity and religion. The challenge is to differentiate between price discrimination based on group characteristics and price discrimination based on risk.
Traditionally, regulators have required credit providers to list the variables underlying loan decisions so that they can determine whether the variables have prohibited group features. And lenders are required to specify the weights attached to the variables so that they can determine whether loan decisions are related to race or ethnic characteristics. As the artificial intelligence based algorithms of the big tech companies are replacing the debt executives, the variables and weights are constantly changing with the arrival of new data points. It is not clear that the regulators can continue.
In algorithmic processes, moreover, the source of bias may change. The data used for algorithm training may be biased. Training with the AI algorithm ‘Learning’ to use data in biased ways can also be biased. Depending on the black-box nature of the algorithmic processes, the location of the problem is rarely clear.
Finally, there are risks to competition. Banks and fintech companies rely on cloud computing services operated by big tech companies, relying on their formidable competitors. Big Tex can also cross-subsidize their financial businesses, which are only a small part of what they do. By providing a wide variety of interlocking services, they can prevent their customers from changing providers. Regulators have responded with open banking regulations requiring financial institutions to share their customer data with third parties after customers agree. They authorize the use of application programming interfaces that allow third-party providers to plug directly into financial websites to obtain customer data.
It is not clear if this is enough. Big Tex can use their platforms to generate large amounts of customer data, train their AI algorithms and more effectively identify high-quality loans than uninformed competitors. Customers can move their financial data to another bank or fintech company, but what about their non-financial data? What about a trained algorithm using one’s data and other customers’ data? Without it, digital banks and fintech players would not be able to price and target their services as effectively as Big Tex. The problems of consumer lock-in and market dominance cannot be overcome.
In the old parable about banks and regulators, banks are fast-moving greyhounds. Regulators are bloodhounds, slow-moving but confidently on the sidewalk. In the age of platform economy, bloodhounds have to choose the pace. Only three central banks reported having dedicated fintech divisions, so there is reason to worry that they will lose flavor. © 2021 / Project Syndicate
Barry Eichengreen is a professor of economics at the University of California, Berkeley