Does Bank Proximity Still Matter?

Research shows physical distance constitutes and important determinant of the extent to which small firms access bank loans. To decide on a loan application, banks collect and assess information about the borrower, and borrower proximity is said to facilitate the collection of this information, especially for the so-called “soft information”.

This soft information refers to information that is hard to transmit and cannot be directly verified by anyone other than the agent who produces it (Stein, 2002). It comes in many shapes and forms. For instance, loan officers often incorporate soft information into their lending decisions by assessing the character of a firm’s manager. The officer may personally know the firm’s CEO and deem her an honest and reliable person. This constitutes information of great value for making credit decisions but at the same something intangible and hard to verify. Other examples of soft information include opinions, ideas, rumours, economic projections, statements of management’s future plans, and market commentary (Liberty and Petersen, 2018). These forms of information are usually collected in person which is why physical distance has been shown to affect lending decisions. 

As with many other industries, the internet has removed many physical barriers for trade and for the transition of information – including soft information. Online communication channels (e.g. video conferencing) have become almost as “good” as face-to-face ones. It would be reasonable then to expect physical distance not to be as important for lending decisions as reported in the past. 

Recent data released by the U.S. Treasury can serve to shed some light on this matter. The U.S. government, through the Small Business Administration (SBA), instituted a special loan program designed to provide financial assistance to small businesses impacted by the current Covid-19 crisis. This loan program, denominated Payment Protection Program (PPP), was intended to provide direct incentive for small businesses to keep their workers on payroll. To access these funds, small business would submit a loan application through any federally insured depository institution (mostly banks) under very favourable conditions (including loan forgiveness) provided the loans were used for eligible payroll costs. More than five million loans were generated according to the data released. This loan data is useful because it contains (uncommon) detailed information about each loan, including the borrower’s address and the name of the originating bank.

I gathered a random sample of more than 200k loan records and pinned down the exact geolocation of each borrower (firm) using Google’s geolocation API. Then, using the geographic location of each bank branch in the U.S., I calculated the geodesic distance between each borrower and the nearest branch of the lending bank. The following map shows the location of each PPP borrower colour-coded by distance to their corresponding lender.

Roughly 90% of the sampled borrowers obtain their loans from banks within a 100 km radius (in blue), whereas approximately 5% borrowed from lenders located between 100 km and 1,000 km away (in red). This is consistent with the notion that physical distance matters for lending, especially for small business loans whose approval relies heavily on soft information. Surprisingly, however, the remaining 5% corresponds to borrowers who obtained PPP funds from lenders more than 1,000 km away. In fact, the vast majority of those loans correspond to borrower-lender distances of more than 3,600 km, roughly the distance between the U.S. East and West coasts. 

The following histogram offers more insight into the distribution of the PPP borrower-lender distances. Because this distribution is heavily right-skewed, I plot the x-axis in a logarithmic scale. This means that moving a unit distance along the x-axis corresponds to multiplying the previous number (say 10 km) by ten. The histogram shows that most firms borrow from lenders located nearby. In fact, 75% of these borrowers purchased loans from banks less than 10 km away. However, it also shows that the distribution of distances has a right fat tail, which corresponds to firms whose lending bank’s nearest branch is located more than 3,000 km away.  Of these long-distance loans, the majority were originated by Cross River Bank. This is a relatively small bank with a single branch in Fort Lee, New Jersey that was featured in Fortune for originating $5.6 billion in PPP loans – No. 12 in the list of PPP loan originators – despite its small size. 

It turns out that Cross River Bank managed to accomplish this impressive amount of loan origination by partnering with FinTechs to automate its loan application process and serve businesses online. So, instead of relying on a broad branch network – which is how small businesses have traditionally accessed credit in the past – Cross River offered its loan services virtually, and quite successfully given the staggering amount of PPP loans supplied.

So, the PPP data suggests that more and more the so-called soft information can and is being transmitted online. This has important implications for small firms’ access to financial services. By removing the reliance on proximity for the acquisition of information, online lending allows for small businesses to have equal access to fairly priced loans, irrespective of their geographic location. Nevertheless, given the uniqueness of PPP loans (e.g.  no credit risk), it is unclear to what extent soft information was collected for loan approvals. Still, this may be an example of how the future of lending – and the acquisition of soft information – may look in the future.


  • Stein, J. C. (2002). Information production and capital allocation: Decentralized versus hierarchical firms. The Journal of Finance57(5), 1891-1921.
  • Liberti, J. M., & Petersen, M. A. (2019). Information: Hard and soft. Review of Corporate Finance Studies8(1), 1-41.

The Decline of Physical Banking

New technological developments are reshaping the way financial services are delivered. From new peer-to-peer lending platforms to cryptocurrencies, the so-called FinTech industry seems to start gaining steam and its full potential is still uncertain.

One of the financial sectors more influenced by new technological developments is the traditional commercial banking system. Many economies have certainly moved really far from the old-fashioned banking model where people would physically commute to their closest bank branch, greet their well-known bank teller and spend long periods (queuing up mostly) to complete mundane transactions such as opening a bank account, depositing money, or applying for a mortgage. Today, some of these transactions still require some sort of physical interaction with our local banks, however, most of them are now performed online or are ceasing to exist (when was the last time you made a bank deposit?)

Nevertheless, the extent of the ongoing transformation is still unclear. The EY FinTech Adoption Index provides important insights into how different markets are changing their consumption habits towards FinTech. I believe, however, that there is a much simpler way of appreciating this transformation. The next figure presents a less sophisticated (but arguably more direct) way of observing direct changes to the way financial services are consumed. At least as far as commercial banking trends go.


This figure shows the average number of bank branches in US counties over time, along with the average amount of deposits per bank also at the county level. Unlike the number of bank branches, bank deposits have increased steadily over the last 20 years. This speaks to the fact that, despite recent disruptions to the system (the GFC and FinTech competitors for instance) the banking industry has experienced growth. Nevertheless, the average number of branches per county has decreased from a high of 49.5 (per 100,000 people) in 2008 to 45.5 today. These recent developments could very well attest to the reshaping of the way financial services are consumed and the current influence of FinTech.

20 Years of Financial Access in the US

Despite recent changes in the way financial services are provided, to this day, commercial banks are still arguably the most important providers of saving and investment products. These financial services allow households to trade off consumption today and tomorrow in a process referred to as consumption smoothing. The process of trading off consumption today for consumption tomorrow (and vice-versa) allows households to “maximise” their lifetime utility (as an economist would put it), which in simpler terms means to look for a proper balance between spending and saving during different stages of our lives so that consumption is more or less “stable”.

For instance, retirement accounts enable people to set aside money during their working life to then be consumed in retirement. Similarly, a personal loan allows us to increase consumption today at the expense of a potential reduction in consumption tomorrow. These two are examples of financial services offered by commercial banks whose access (or lack thereof) could have a significant impact on households living standards and their ability to mitigate unexpected events (e.g. job loss).

Despite its importance, the extent to which households have access to financial services (also referred to as financial inclusion) is hard to measure. Most available indicators rely on survey data and unavoidably have to trade depth for breadth, providing very detailed financial access data but usually at aggregate levels (see Global Findex).


This map attempts to provide a more granular measure of financial access. One that provides geographical differences in the access to financial services across US counties. Specifically, the map characterises the number of bank branches per 100,000 people for each US county. Despite new technological developments, proximity to financial providers is still an important determinant of whether people can access basic financial services such as bank accounts and personal loans. Hence, having more branches in a specific region (after adjusting for its size) can serve as a proxy for its overall state of financial inclusion. Over the last 20 years, counties in the US have experienced important changes in this measure of financial inclusion. Relative to the late 1990s, nowadays most counties present higher levels, however, geographical differences persist and seem to be very consistent over time.

There are of course important limitations with this characterisation of financial access. One can argue these geographical differences could be “demand-driven”, that is, caused by differences in the degree to which certain regions prefer to consume these services. Nevertheless, given the aforementioned importance financial services have in allowing people to smooth consumption and mitigate risks, this measure may still provide important information on where people are able to balance their spending more effectively.

US Banks and the 10 Billion Mark

The Global Financial Crisis of 2008 unleashed a series of regulatory changes around the world aimed to overcome the market flaws that led to the collapse of several financial institutions and billions of dollars spent on bailout programs. As a direct response, in 2010 the US Congress passed the Dodd-Frank Wall Street Reform and Consumer Protection Act. Among other important changes, Dodd-Frank tightened up the regulatory framework for commercial banks, especially for large institutions considered to be an important conduit of the financial meltdown that took place between 2007-2009.

Specifically, under Dodd-Frank institutions with more than 10 billion in assets are subject to a stricter set of regulations which includes caps to fee income and the obligation to conduct annual stress tests. These more demanding regulatory requirements impose extra costs, and perhaps this exactly why the size distribution of commercial banks in the US has changed in recent years. The following figure shows the size distribution of commercial banks (roughly 200 of them) with assets between 9 and 11 billion dollars before and after Dodd-Frank was enacted.


The fact that currently a larger proportion of banks are observed below the 10 billion mark (relative to before the change in regulation) may constitute an instance of regulatory arbitrage. So as to avoid a stricter regulatory environment and higher operation costs, banks in the US purposely remain below the 10 billion mark. To do so, these financial institutions may decide to slow down their credit production or transition towards less conventional banking models such as the so-called originate-to-distribute model. Hence, lower lending growth and/or higher market risk (through bank securitisation for instance) could amount to unintended consequences of this key reform.

Earlier this year, the Trump administration successfully rollbacked some of Dodd-Frank’s most important provisions, effectively increasing the threshold for banks to be subject to stricter federal oversight. It is yet to be seen how these new changes will reshape the US banking sector.