Transformation of the financial sector and the provision of financial services is driven by ‘big data’ and the computer-aided ability of financial institutions to analyze these data to provide improved services to customers.

By big data, we mean very large structured and/or unstructured data sets containing tens of thousands of observations on bank customers, insurance policy holders, and users of online payment platforms etc., as well as textual data that can be digitized and used for the computer-aided analysis of newly issued financial regulations, newspaper reports to search for indicators of economic uncertainty, and reports by investment banks that may reveal information about market sentiment.

The evolving analytical techniques that enable financial institutions to take advantage of big data are commonly known as machine learning or artificial intelligence (AI). These are sophisticated methods to discover intricate, often non-linear, relationships between variables that can inform decisions on customer credit worthiness, asset allocation decisions, risk management, and forecasting.

AI is widespread in the financial services industry

The use of AI in the financial services industry is widespread. A survey of AI in financial services conducted jointly by the Cambridge Centre for Alternative Finance and the World Economic Forum found that 70%–80% of the firms surveyed had already implemented or were in the process of implanting some form of AI solution in their business models.

Not surprisingly, fintech firms were in general more active users of AI, although only by a relatively small margin. While these developments will change the nature of financial services and how they are provided by incumbent financial institutions and new start-up fintech companies, they are not likely to pose an existential threat to the traditional financial services industry as a whole. The arrival of new institutions, so-called ‘Big Tech’ firms, may do so.

The challenge from Big Tech

Big Tech institutions are firms like Alibaba and Tencent in the People’s Republic of China; Amazon, Google, and Facebook in the United States; Uber in Europe; and Grab in Southeast Asia. These companies did not start as financial services companies, but by taking advantage of their vast networks of customers and the consequent huge amount of data generated by the actions of these customers, they have entered into the financial services business.

Big Tech companies are a source of numerous direct benefits for consumers, especially in emerging and developing economies, where they have contributed substantially to the financial inclusion of previously unserved segments of the populations. Particularly important has been their engagement with small and medium-sized enterprises (SMEs), which traditional financial institutions have not served adequately.

In lending, Big Tech firms can use their wealth of data on the payments and receipts of SMEs to assess creditworthiness and, hence, be in a better position to grant loans. Big Tech companies are also a source of indirect benefits for consumers as they provide technology infrastructure for traditional financial institutions and encourage innovation, diversification, and efficiency.

With their size, extensive customer base, and access to customer information, Big Tech companies constitute a competitive threat to traditional banks that goes beyond that of fintech start-ups. While incumbent financial service providers can and do replicate many of the innovations of fintechs, it is much more difficult to replicate the business model of Big Tech companies because of the advantages the latter can extract from their vast information data bases on just about all aspects of their customers’ behavior.

Financial stability risks

Financial liberalization and financial innovation have traditionally preceded stresses in the financial system. The basic mechanism is as follows. Financial deregulation and financial innovation create opportunities to expand credit extension and engage in new financial ventures without adequate understanding or appreciation of the underlying risks.

The extension of credit leads to economic expansion, which makes the increased debt burden of the borrower seem tolerable, and the riskiness of new financial products are not well understood because, by definition, there is no or very little past data to guide decisions. The result is overextended borrowers and over-leveraged lenders, and when the tide turns, turmoil and even havoc ensues.

These mechanisms apply also to the digital transformation of finance. The emergence of new types of institutions providing financial services is akin to financial liberalization, as some of the activities of these institutions lie outside the perimeter of the regulatory system. Innovations brought by fintech and Big Tech can introduce products whose risk characteristics are not well known and that can have systemic stability consequences, the rapid growth of peer-to-peer lending by fintech firms being one example.

Machine learning and artificial intelligence may also amplify systemic risk as risk management functions in financial institutions are employed to optimize compliance with the existing regulatory framework. If the optimization algorithms lead to solutions that are similar across institutions, the result may be a financial system that is increasingly procyclical when shocks materialize.

Regulators must be vigilant and ready to adapt to the new financial landscape. New entrants that are not yet included in the perimeter of the regulatory system must be monitored, and potential systemic consequences of new sources of risk to individual institutions must be continuously assessed.

As some activities of unregulated institutions are indistinguishable from the same activities in regulated institutions, there is a risk of regulatory arbitrage taking place. It is, therefore, imperative that regulatory frameworks be adjusted to focus on activities rather than on institutions.

Post-pandemic implications

This is being written in the midst of the coronavirus disease (COVID-19) pandemic, which has created unimaginable human suffering and great economic upheaval. As it unfolds, it is hard to imagine that the world will return to what it was just over a year ago.  How might the above analysis and conclusions be affected?

A salient feature of the digital transformation of finance is that virtual AI-assisted financial intermediation is challenging financial intermediation and payment services that are based on personal contacts. The social-distancing behavior that has been mandated or highly recommended during the pandemic, and that may well continue voluntarily in a modified form in the future, increases the competitive advantage of the virtual business model.

Entities that have broad access to potential customers, either through their social media presence or their Internet-based commerce engagement, will be particularly strongly positioned to expand in this environment. These are the Big Tech firms.

Because of their ability to take advantage of scale, there is a risk of greater concentration in the financial intermediation industry and, hence, a greater risk of monopoly pricing, cybersecurity challenges, and too-big-to-fail problems. Regulatory authorities must be vigilant and ensure that the financial services activities of these firms are appropriately regulated.

Further discussion and analysis on this topic can be found in the recent ADBI publication:

Genberg, H. 2020. Digital Transformation: Some Implications for Financial and Macroeconomic Stability. In J. Beirne and D.G. Fernandez (eds.), Macroeconomic Stabilization in the Digital Age. Tokyo: ADBI.

About the Author
Hans Genberg is professor of economics and senior director of central banking and finance programs at the Asia School of Business.