The cost of financial intermediation has declined in recent years thanks to technology and increased competition in some parts of the finance industry. I document this fact and I analyze two features of new financial technologies that have stirred controversy: returns to scale and the use of big data and machine learning. I argue that the nature of fixed versus variable costs in robo-advising is likely to democratize access to financial services. Big data is likely to reduce the impact of negative prejudice in the credit market but it could reduce the effectiveness of existing policies aimed at protecting minorities.
Fintech covers digital innovations and technology-enabled business model innovations in the financial sector. Such innovations can disrupt existing industry structures and blur industry boundaries, facilitate strategic disintermediation, revolutionize how existing firms create and deliver products and services, provide new gateways for entrepreneurship, and democratize access to financial services. On the other hand, they create significant privacy, regulatory and law-enforcement challenges and they could increase the scope for some forms of discrimination. Examples of innovations that are central to Fintech today include various application of blockchain technologies, new digital advisory and trading systems, artificial intelligence and machine learning, peer-to-peer lending, equity crowdfunding and mobile payment systems. In this paper I offer some preliminary evidence and theoretical analysis about the impact of technological progress in the finance industry. The first question is whether there has been any material change in financial intermediation in recent years. To shed some light on this question, I update the work of Philippon (2015) with post-crisis U.S. data. The puzzle emphasized in previous work was that the unit cost of financial intermediation had remained stubbornly close to 200 basis points for more than a century, despite advances – and large investments – in computers and communication technologies. The post-crisis data suggests that this puzzle might be diminishing. I find that the unit cost of financial intermediation has declined over the past 10 years. I then study two issues that are at the heart of the Fintech debate: access to finance and discrimination. If we accept the fact that Fintech brings efficiency gains to financial intermediation, the next question is: how will these gains be shared? Will Fintech democratize access to financial services or will it increase inequality? I highlight two forces that will shape the answer to these questions. The first force is increasing returns to scale brought by technology. I argue that the nature of fixed versus variable costs has changed in a way that is likely to improve access to financial services. It may not, however, reduce inequality among all groups. The second force is the use of big data and machine learning (BDML for short). I argue that this technology is likely to reduce unwarranted human biases against minorities, but it will probably decrease the effectiveness of existing regulations. The tentative conclusion is that Fintech can bring widely-shared welfare benefits but changes in existing policies and regulations are necessary to achieve its full potential.
Recent literature Philippon (2016) discusses the literature up to 2016 so I will mention here some recent papers. Focusing on residential mortgages, Buchak et al. (2018) study the growth in the market share of shadow bank and Fintech lenders, arguing that it can be explained by differences in regulation and technological advantages. They find that Fintech lenders serve more creditworthy borrowers (relative to shadow banks) but charge higher interest rates (14-16 basis points), which is consistent with the idea that consumer are willing to pay for better user experience and quick decisions. Fuster et al. (2019) study the differences between Fintech and traditional lenders in the mortgage market and find that the former is quicker in processing applications (20% faster), without increasing loan risk. They also provide evidence that Fintech lenders adjust supply more elastically to demand shocks and increase thepropensity to refinance, especially among borrowers that are likely to benefit from it. Their results suggest that Fintech firms have improved the efficiency of financial intermediation in mortgage markets. The advent of Fintech is often seen as a promising avenue for reducing inequality in access to credit. Bartlett et al. (2018) study this issue, analyzing the role of Fintech lenders in alleviating discrimination in mortgage markets. They find that all lenders, including Fintech, charge minorities more for purchase and refinance mortgages but that Fintech algorithms discriminate 40% less than face-to-face lenders. Regarding the use of new technologies in credit markets, Berg et al. (2019) analyse the information content of the “digital footprint” (an easily accessible information for any firm conducting business in the digital sphere) for predicting consumer default. With data from a German e-commerce, they find that it equals or exceeds the predictive power of traditional credit bureau scores. Their results suggest that new technologies and new data might bring a superior ability for screening borrowers. FinTechs are also competing in the market for wealth management. The United States is the leading market for robo-advisors. In 2017, it accounted for more than half of all investments in robo-advisors (Abraham et al., 2019). Nevertheless, the amount of assets managed by robo-advisors is still a small portion of total assets under management, with average client wealth much smaller than the average in the industry (Economist, 2017). Abraham et al. (2019) argues that because they save on fixed costs (such as salaries of financial advisors or maintenance of physical offices), robo-advisors can reduce minimum investment requirements and charge lower fees.
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