Fintech and banks in transition
Digitalisation is transforming the global financial system, with fintech innovators such as peer-to-peer lending platforms starting to compete with banks. This chapter uses unique survey data to look at the ways in which banks across the EBRD regions are responding to the risks and opportunities presented by fintech. On the one hand, banks themselves have now started to make substantial investments in new technologies – particularly digital wallet solutions, biometric identification systems and sophisticated algorithms for screening borrowers. On the other hand, they have also responded by expanding their online banking services, while pruning their branch networks. Such expansion of digital infrastructure has improved access to credit for small businesses and allowed households to access a broader palette of financial services.
Introduction
Digitalisation is transforming the global financial system at a rapid pace. Digital innovators such as crowd-funding platforms and big-tech firms are becoming strong competitors for traditional deposit-taking institutions.1 Fintech firms are breaking up and unbundling the financial value chain by specialising in specific products and services, such as cross-border mobile payments and screening technologies based on big data. At the same time, they also offer aggregation services that allow customers to see all of their financial products with different providers in one mobile phone app.
What is fintech?
Fintech – financial technology – uses new technologies to improve financial services and make them accessible to more firms and households. Such new technologies range from digital wallets (which allow people to store their payment cards on their mobile phones) to robo-advisers and stock-trading apps. Fintech firms use specialist software and algorithms on computers and smartphones to deliver such services faster and more efficiently. Those firms are often start-ups, which disrupt incumbents in the finance industry by using technology to reduce operational costs and reach previously underserved markets. This allows consumers to “mix and match” services from various providers and re-bundle them according to their personal preferences (for example, by having a standard deposit account at a bank but using a mobile payment app such as Klarna or PayPal to make domestic and international payments).
Digitalisation and alternative finance
Digitalisation has enabled the emergence of a broad range of alternative finance models, which involve internet-based financial channels and instruments falling outside of the traditional financial system (outside of regulated banking and capital markets, for example). These models fall into three main categories. First, peer-to-peer (P2P) and marketplace lending platforms allow individuals or businesses to borrow directly from individual lenders or, increasingly, institutional investors. At the same time, leading big-tech firms in the fields of e-commerce, social media and internet search have started to provide credit by leveraging the wealth of information that they collect on consumers and businesses.3 Second, equity crowdfunding allows individuals or institutions to invest in unlisted shares or securities issued by firms (often SMEs). And third, non-investment-based models such as donation crowdfunding allow funds to be raised for projects without the organiser being under any obligation to provide a monetary return. In addition to those three main categories, there are large numbers of other alternative finance models, such as mini-bonds, digital property funding and online invoice trading.
Source: Cambridge Centre for Alternative Finance (CCAF), World Development Indicators and authors’ calculations.
Note: This chart shows annual averages over the period 2016-20 for the top 25 economies only.
Source: CCAF, World Development Indicators and authors’ calculations.
Note: Average alternative finance comprises both P2P lending and capital raised through investment-based and non-investment-based crowdfunding. The chart shows annual averages over the period 2016-20.
- EBRD regions
- Comparator economies
Source: CCAF, World Development Indicators and authors’ calculations.
Note: This chart shows annual averages over the period 2016-20. The sample is restricted to economies with at least two years of data between 2016 and 2020.
Digital lending versus digital equity
Overwhelmingly, the EBRD regions are still reliant on debt – rather than equity – financing. Economic contractions in the wake of the global financial crisis, as well as large-scale emergency lending programmes during the recent Covid-19 pandemic, have resulted in high debt levels for many households and firms.6
- EBRD regions
- Comparator economies
Source: CCAF and authors’ calculations.
Note: This chart shows annual averages over the period 2016-20. The sample is restricted to economies with at least two years of data between 2016 and 2020.
- EBRD regions
- Comparator economies
Source: CCAF, World Development Indicators and authors’ calculations.
Note: This chart shows annual averages over the period 2016-20. The sample is restricted to economies with at least two years of data between 2016 and 2020.
Fintech and banks: threats and opportunities
Digitalisation and the emergence of fintech are providing opportunities for banks across the EBRD regions, but they are also posing challenges. Fintech companies have been specialising in financial services for which they do not need access to a large balance sheet of their own. As a result, those firms have often had the advantage of being less heavily regulated than banks.8 By chipping away at parts of the financial value chain, they are contributing to the gradual disintegration of the traditional banking model.9 On the upside, however, advances in big-data analytics and artificial intelligence are giving banks new tools, helping them to reach out to market segments that have previously been difficult to lend to.
Source: BEPS III and authors’ calculations.
Note: These figures are weighted averages across economies.
Source: BEPS III and authors’ calculations.
Note: These figures are weighted averages across economies.
Source: BEPS III and authors’ calculations.
Note: The size of each circle is proportionate to the percentage of banks for which the relevant technology (horizontal axis) is at the developmental stage in question (vertical axis).
Drivers of banks’ fintech strategies
In order to measure how advanced banks are in terms of their active engagement with fintech, this chapter creates three bespoke indices. The first index (“fintech use and development”) gauges a bank’s use and development of fintech technologies, with scores ranging from 0 (no development) to 4 (commercial use), as shown in Chart 4.8. These answers are aggregated and standardised as z-scores ranging from 0 to 1, with higher scores indicating that a bank is more digitally advanced.
Source: BEPS III and authors’ calculations.
Note: Based on OLS models regressing the three indices (fintech use and development, fintech investment, and digitalisation concerns) on (i) bank size (log of total assets), (ii) dummy variables for foreign and state ownership, (iii) a dummy variable indicating whether the CEO believes that the culture of their bank is geared towards innovation, transformation and agility, (iv) a dummy variable indicating whether the CEO believes themselves to be an innovator, an entrepreneur and a visionary, and (v) a dummy variable indicating whether the CEO has a Master’s degree or a PhD. Other controls include the CEO’s gender and region fixed effects. The 90 per cent confidence intervals shown are based on robust standard errors.
Fintech and branch networks
Banks across the EBRD regions are already experiencing strong competition from internet banks, non-bank online lenders and non-bank finance companies. These three types of alternative lender are more likely to be regarded as strong competitors in retail lending than lending to SMEs (see Chart 4.10). For instance, 31 per cent of bank CEOs across the EBRD regions consider internet banks to be a strong competitor in retail lending, compared with just 21 per cent for lending to SMEs.
Source: BEPS III and authors’ calculations.
Note: This chart indicates the percentages of banks in each economy which regard internet banks, non-bank online lenders and non-bank finance companies as strong competitors in the areas of retail lending (horizontal axis) and lending to SMEs (vertical axis). Data for Greece, Kosovo, Montenegro, Morocco, North Macedonia, Serbia and Tajikistan are not included.
Source: BEPS III and authors’ calculations.
Note: This chart shows the percentages of banks that expect their branch networks to decline, remain the same and increase in size over the next five years.
Source: BEPS III and authors’ calculations.
Note: These figures are weighted averages across economies.
Digitalisation and access to credit
Thus, digitalisation in the banking sector may be a double-edged sword for firms and families looking for a loan. On the one hand, fintech lenders are increasing competition in credit markets and banks are responding by accepting credit applications online. On the other hand, however, banks have started to reduce the size of their branch networks, sometimes drastically. What impact, on balance, has digitalisation had on businesses’ and individuals’ access to finance? To answer that question, this section looks at one of the most transformative digital advances of the last two decades: the introduction of mobile data networks.
Mobile networks and businesses’ access to finance
The analysis in this section draws on the results of Enterprise Surveys conducted by the EBRD, the EIB and the World Bank. The data used are derived from the last three rounds of that survey (the fourth, fifth and sixth survey rounds), which were conducted in 2008-09, 2011-14 and 2018-20 respectively – periods in which the EBRD regions saw increasing adoption of first 3G and then 4G technology.
Source: Enterprise Surveys and authors’ calculations.
Note: This chart shows ordinary least squares estimates of the impact that the availability of 4G mobile networks at subnational region level has on financial inclusion at firm level. The 90 per cent confidence intervals shown are based on robust standard errors clustered at subnational region level. All models include subnational region, country-year and sector-year fixed effects, the population density of subnational regions, bank branch density within 5 km of a firm, and firm-level controls (with indicators for exporters, female owners, firms that have been in business for less than five years, audit status and urban/rural location). Localities with observations for fewer than five firms are excluded.
Source: Enterprise Surveys and authors’ calculations.
Note: This chart shows ordinary least squares estimates of the impact that the availability of 4G mobile networks at subnational region level has on financial inclusion at firm level. The 90 per cent confidence intervals shown are based on robust standard errors clustered at subnational region level. All models include subnational region, country-year and sector-year fixed effects, the population density of subnational regions, bank branch density within 5 km of a firm, and firm-level controls (with indicators for exporters, female owners, firms that have been in business for less than five years, audit status and urban/rural location). Localities with observations for fewer than five firms are excluded.
Source: Enterprise Surveys and authors’ calculations.
Note: This chart shows ordinary least squares estimates of the impact that the availability of 4G mobile networks at subnational region level has on financial inclusion at firm level. The 90 per cent confidence intervals shown are based on robust standard errors clustered at subnational region level. All models include subnational region, country-year and sector-year fixed effects, the population density of subnational regions, bank branch density within 5 km of a firm, and firm-level controls (with indicators for exporters, female owners, firms that have been in business for less than five years, audit status and urban/rural location). Localities with observations for fewer than five firms are excluded.
Mobile internet democratises access to finance
This next section looks at how the expansion of mobile network coverage is linked to the financial inclusion of households in terms of both (i) increased access to finance for traditionally underserved individuals (such as those living in rural locations) and (ii) reductions in the cost of financial intermediation for all households. Digitalisation may mean that individuals who were previously financially excluded are able to invest in education, save money and launch new businesses, which contributes to the reduction of poverty and fosters economic growth.24 Moreover, having a bank account facilitates asset building and wealth creation, which may allow the smoothing of consumption on retirement or when faced with economic shocks.25
We can use the 2015-20 waves of the Austrian National Bank’s Euro Survey to look at the ways in which digitalisation has affected the financial inclusion of people living in central, eastern and south eastern Europe (see Box 4.2 for details). Analysis shows that access to bank accounts has increased throughout that region in the period since 2015, but individuals living in an area with 4G are more likely to have a bank account and use online services. In addition to bank accounts, digitalisation can also broaden financial inclusion through its impact on investment products such as life insurance, equities and pension funds. Importantly, individuals who use online banking and people living in areas with 4G are much more likely to access such investment products than individuals without access to online banking and people living in areas without 4G.
Against that background, the Covid-19 pandemic provides an opportunity to leverage the positive impact that mobile internet infrastructure can have on financial inclusion. As discussed in Box 4.3, individuals who have been exposed to an epidemic in the past two decades are much more likely to make online payments and carry out banking transactions using an ATM instead of a bank branch. The post-Covid-19 recovery will probably see many more individuals making use of such financial technologies, contributing to increased competition in the field of financial services – provided that reliable digital infrastructure and sufficient levels of digital literacy are in place.
The dark side of fintech
At the same time, however, the adoption of financial technologies is not without risks to financial resilience, inclusion, and consumer privacy and welfare. For instance, fintech-based lending risks further aggravating problems of over-indebtedness in specific groups.26 Recent evidence from Tanzania, for example, shows that easily available credit accessed via mobile phones is less likely to be repaid when people borrow late at night. Moreover, many digital borrowers are repeatedly late with loan repayments, incurring large penalties, suggesting that they have got caught in a digital debt spiral.
Conclusion
Technological disruption is transforming financial services across the EBRD regions. While alternative finance is still a fairly new concept in many EBRD regions, a number of countries in those regions are relatively advanced in specific areas, such as peer-to-peer lending. Banks’ CEOs regard digitalisation as the biggest challenge that they will face in the coming years, citing competition from fintech providers in particular.
Small firms and households both have the potential to benefit from further digitalisation in the banking sector. As the analysis in this chapter has shown, digital infrastructure in the form of high-speed mobile internet can help to ease credit constraints for businesses and extend financial inclusion to traditionally underserved sections of the population. The digitalisation of financial services is not without risks, however, with policymakers needing to pay attention to a number of specific issues in order to ensure that digitalisation increases financial inclusion in the long term while preserving financial stability.
First, while P2P and crowdfunding markets have been growing rapidly in a number of economies in the CEB region and the former Soviet Union, and cross-country platforms have been established that successfully connect EBRD regions, growth in alternative finance has overwhelmingly been debt based. Moreover, retail borrowers’ exposure to alternative debt instruments tends not to be on supervisory authorities’ radars. It is often not captured in credit registries, enabling households to “double dip” and borrow from several different sources at the same time – a risk that is particularly acute in countries with a history of excessive private-sector borrowing.31 Consequently, fintech appropriate consumer protection will be key in order to prevent households and small firms from becoming over-indebted. Credit reporting requirements and credit bureau functions also need to be updated, as the monthly reporting currently carried out by lenders is not well suited to the speed of online lending.32
Second, banks’ digitalisation and fintech strategies vary widely, with one in five bank CEOs reporting that they have difficulty identifying and establishing links with fintech companies. With that in mind, banks and fintech companies could be encouraged to try out collaborative initiatives within the protected environment of a regulatory sandbox. A regulatory sandbox allows firms to test innovative products or business models in a live market environment, while ensuring that appropriate protections are in place (see Box 4.4). This helps regulators to understand emerging fintech technologies – including their potential benefits and adverse effects on consumers – before a product or service is fully available on the market. Another important barrier to increased adoption of fintech concerns IT security and regulatory uncertainty (see Box 1.2 in Chapter 1). Clear and predictable guidelines on digital alternatives to paper documents/contracts and wet-ink signatures are essential in that regard, since clear frameworks will help fintech companies and incumbent banks alike to introduce new technologies without any fear of falling foul of regulatory or supervisory rules.
Third, as the BEPS III survey shows, many banks have themselves introduced algorithmic credit scoring. With branches closing and loan applications increasingly moving online, supported by more sophisticated credit-scoring models, it will be important for policymakers to gain a better understanding of the implications of these fintech-related trends in terms of financial inclusion. While research suggests that algorithmic lending by fintech companies can reduce discrimination relative to face-to-face lenders, such technology does not fully eliminate discrimination in loan pricing. In order to ensure greater transparency in algorithms, regulators could require lenders to demonstrate that the big-data variables used in their credit-scoring models do not disadvantage certain groups.33
Fourth, equitable access to financial services across different locations is another concern. While branch reduction is a key part of banks’ digitalisation strategies, the analysis in this chapter shows that access to mobile networks is most beneficial to businesses located in districts with relatively large numbers of physical bank branches. Thus, digitalisation has the potential to exacerbate firms’ credit constraints in regions that lack access to high-quality mobile networks and have low levels of branch density. Those regions risk being left behind in terms of both digital infrastructure and banking services, which could have long-lasting adverse effects on economic activity and inclusion.
Box 4.1. Central bank digital currencies
With cash transactions in decline and digital payments on the rise, a wave of new technological developments in the payment industry – including cryptocurrencies, stablecoins and the entry of large technology firms – has the potential to result in far-reaching changes to payment systems around the world.34 While such innovations could yield benefits in terms of cost and convenience, their ultimate impact on consumer welfare will depend on the market structure and governance arrangements that underpin them. At present, for example, cryptocurrencies are primarily speculative assets, rather than a form of money. They also facilitate illicit transactions. Moreover, the network effects that confer market power on large technology firms could lead to data silos and anti-competitive practices. This could exacerbate the stubbornly high costs of existing payment systems and hamper equal access to digital payment options. Furthermore, the combining of transaction, internet search and social media data also raises concerns about data abuse and even personal safety.
Source: Auer and Böhme (2021).
Box 4.2. Digitalisation and financial inclusion
While we know that bank accounts can be a major gateway to broader financial inclusion, this box looks at other financial products, asking whether digital access affects financial inclusion when it comes to investment products such as life insurance, personal pensions, equities, bonds and mutual funds. The analysis in this box is based on data from the 2015 20 waves of the Austrian National Bank’s Euro Survey, which covers at least 1,000 adults in each of the following countries: Albania, Bosnia and Herzegovina, Bulgaria, Croatia, the Czech Republic, Hungary, North Macedonia, Poland, Romania and Serbia.40
Source: Euro Survey.
Dependent variable | Bank account and investment product | Contractual savings products | Capital market investment | |||
---|---|---|---|---|---|---|
Bank account and life insurance | Bank account and pension fund | Bank account and equities | Bank account and mutual fund | Bank account and bond | ||
Internet at home | 0.068*** | 0.055*** | 0.031*** | 0.008** | 0.019*** | 0.011*** |
(0.007) | (0.006) | (0.006) | (0.004) | (0.004) | (0.003) | |
Owns mobile | 0.008 | 0.002 | 0.001 | 0.001 | -0.010* | -0.003 |
(0.013) | (0.011) | (0.011) | (0.006) | (0.005) | (0.004) | |
Quality and duration of mobile coverage |
0.169*** | 0.129*** | 0.073*** | 0.041*** | 0.005 | 0.021** |
(0.028) | (0.022) | (0.021) | (0.014) | (0.011) | (0.009) | |
Local economic activity | 0.012*** | -0.002 | 0.013*** | 0.004* | 0.001 | 0.001 |
(0.004) | (0.003) | (0.003) | (0.002) | (0.002) | (0.001) | |
Number of observations | 22,292 | 22,419 | 22,394 | 22,388 | 22,374 | 22,373 |
Country fixed effects | Yes | Yes | Yes | Yes | Yes | Yes |
Additional controls | Yes | Yes | Yes | Yes | Yes | Yes |
SOURCE: Euro Survey and authors’ calculations.
NOTE: Average marginal effects derived from bivariate probit regressions, with clustered standard errors in parentheses. *, ** and *** denote statistical significance at the 10, 5 and 1 per cent levels respectively. All specifications control for age, gender, marital and labour market status, household income, home ownership, risk aversion, financial literacy, experience of hyperinflation and financial losses during transition, the log of the population size and the log of the distance to the nearest bank branch. The quality and duration of mobile coverage ranges from 0 (no mobile coverage) to 1 (4G coverage since 2012) and is based on annual maps in Collins Bartholomew’s Mobile Coverage Explorer. Local economic activity is proxied by the log of the VIIRS average stable night light within a 20 km radius of an individual’s place of residence (see Henderson et al., 2012).
Box 4.3. Digital divides during epidemics: evidence from the adoption of fintech
Throughout history, epidemics have triggered crucial breaks in technological trends. For instance, by killing at least a quarter of Europe’s population during the 14th century, the Black Death precipitated the adoption of capital-intensive agricultural technologies such as the heavy plough and the watermill, with labour becoming scarce and expensive. More recently, Covid-19 has already been shown to have increased remote working, online shopping and the provision of telehealth services.42
Source: Global Findex and Ma et al. (2020).
Note: These estimates are derived from individual-level models regressing binary variables capturing the use of various technologies on exposure to an epidemic. All specifications control for individual characteristics and country and year fixed effects and use the Global Findex sampling weights. The 95 per cent confidence intervals shown are based on robust standard errors clustered at country level.
Box 4.4. Fintech inside a sandbox
Playgrounds often feature a small box on the ground filled with sand, where children play under the watchful eyes of their parents. This is where the term “sandbox” – one of the most common words in the fintech universe – originates from. A “regulatory sandbox” provides a protected environment in which eligible firms can experiment with the introduction of new products and services, allowing businesses to see whether their innovative solutions comply with regulatory requirements without any risk to financial stability. Regulators supervise such testing closely on the basis of predefined parameters and timeframes and provide feedback.
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