Old Program, New Banks: Online Banks in Small 

Business Lending 

Elizabeth Bickmore Andrew MacKinlay Yessenia Tellez* 

March 5, 2024 

Abstract 

While banks historically offer a litany of different credit and depository products to their local customers, technological innovation and consumer preferences have spurred growth of 

online banks specializing in particular activities across broad geographic areas. This paper 

analyzes the unintended consequences of online banks’ specialized lending model on small 

business lending. We use loans in the SBA program, which provides guarantees to motivate 

partner-lenders to lend to higher-risk borrowers. We fnd that online banks expand credit 

access in the most economically troubled counties. However, this effect is not driven by 

minorities or women-owned businesses. Online banks target higher guarantees, generating 

a cross-subsidy from traditional lenders, borrowers, and the government to online lenders. 

JEL Code: G21, G28, H81, L38 

Keywords: Online Banking, Small Business Lending, Credit Access, Loan Guarantees. 

*Elizabeth Bickmore: Pamplin College of Business, Virginia Tech, Blacksburg, VA 24061 Email: lizzybickmore@vt.edu; Andrew MacKinlay: Pamplin College of Business, Virginia Tech, Blacksburg, VA 24061. Email: 

acmackin@vt.edu; Yessenia Tellez: Pamplin College of Business, Virginia Tech, Blacksburg, VA 24061 Email: 

yesseniatellez@vt.edu. We thank seminar participants at Virginia Tech, the Federal Reserve Bank of New York, 

the University of Virginia, and Utah State University. We also thank discussants at the following conferences: 

European Finance Association (EFA), Eastern Finance Association, Financial Management Association (FMA), 

and The Economics of Financial Technology conferences (University of Edinburgh). 
In recent decades, changes to bank regulation and innovation in fnancial technology have 

caused a dramatic shift in the lending landscape away from traditional brick-and-mortar banks 

to shadow banks, fntech lenders, and online banks (Buchack, Matvos, Piskorski, and Seru, 

2018; Gopal and Schnabl, 2022; Jagtiani and Lemieux, 2018). These new lenders differ from 

traditional banks in terms of borrower profle (Bao and Huang, 2021; Balyuk, Berger, and Hackney, 2022) and the scope of their product offering (Benetton, Buchak, and Robles-Garcia, 2022) 

leading to a more specialized business model. Much of the research has focused on differences 

in regulatory burden and the role of nonbank fntech lenders on consumer lending, but advances 

in fnancial technology and differences in business models have also produced a new class of 

bank whose operations are primarily online and lend to small businesses. These online banks 

are depository institutions that use streamlined platforms and technology to make lending decisions and reach a broader pool of borrowers. Like nonbank fntech lenders, online banks are 

not restricted by geography and may use a specialized model to target specifc assets or loan 

characteristics that they view as attractive (Erel and Liebersohn, 2022; Di and Pattison, 2023). 

This specialization is somewhat at odds with banks generating profts by undertaking a broad 

set of depository activities, lending activities, and exploiting the synergies between them (Egan, 

Lewellen, and Sunderam, 2021). A commonly cited beneft of banks is the ability to cross-sell 

products to customers. For larger frms, this cross-selling is in the form of future loans, debt 

underwriting, and other investment banking services (Yasuda, 2005; Ljungqvist, Marston, and 

Wilhelm Jr., 2006; Bharath, Dahiya, Saunders, and Srinivasan, 2007; Neuhann and Saidi, 2018). 

For retail and small business customers, the cross-selling is typically additional loans or credit 

cards, brokerage accounts, or other non-credit services (Puri and Rocholl, 2008; Santikian, 

2014; Benetton, Buchak, and Robles-Garcia, 2022). 

In this paper, we hypothesize that online banks focus on a narrow scope of products for a 

set of borrowers that are riskier and not ripe for cross-selling. Indeed, we observe that online 

banks feature a more specialized business model than traditional banks and target specifc loan 

profles. In doing so, these banks expand credit access to small businesses. The behavior of 

1 
online banks mirrors that of fntech nonbanks, suggesting that it is technology and specialization 

rather than solely differences in regulatory burden that drives fntech behavior. 

We test for any unintended consequences of online banks’ specialization in small business 

lending using data from the Small Business Administration (SBA) 7(a) program. We fnd that 

online banks appear to utilize features of the SBA loan program to secure the highest guarantee 

rates. At the same time, these lenders target higher interest rates, effectively lowering the cost of 

default and increasing revenue from interest. We fnd that online bank loans are more likely to be 

charged-off and come with higher default amounts. Given the higher guarantees, these increased 

default costs are primarily borne by the SBA. As the costs paid by lenders to participate in the 

SBA program do not depend on the probability of default, this behavior generates a transfer from 

traditional lenders, borrowers, and the government to online banks. We fnd SBA guarantees 

to generally be higher in economically disadvantaged counties. Therefore, in targeting high 

guarantees, online banks lend more in poor and underserved areas, effectively increasing credit 

supply in these regions. We fnd that this credit expansion is not driven by increased lending to 

minorities and women. 

We focus our analysis on small business lending because it is an economically relevant area 

that is being transformed by non-traditional lenders. As of 2019, small businesses generated 

44% of all economic activity in the US (Offce Of Advocacy, 2019). Within the universe of 

small business loans, SBA lending is estimated to account for about 8% of all loans in 2016 

(Gopal and Schnabl, 2022). The SBA provides comprehensive information on its loans that is 

typically unavailable in other data sources. SBA lenders are required to report the loan amount, 

interest rate, recent loan status, term length, borrower name, address, and industry, and the 

lender name and address. This granularity is unique among small business lending data sources. 

The SBA operates by incentivizing its partner-lenders to make loans to high-risk small business borrowers by guaranteeing a percent of the loan. Only SBA-approved lenders can participate in these programs and lenders are required to pay a small “guarantee fee” which is a 

fxed upfront cost based on loan amount and term length. SBA loans come with maximum loan 

2 
amounts, interest rates, term lengths, and fund uses that depend on the specifc program. As a 

government program, the SBA discloses how much government funding was needed each year. 

The SBA has the goal of achieving a “zero-subsidy rate,” meaning that it generates adequate 

funds through guarantee fees and recovered collateral to offset the cost of paying the guarantees 

on defaulted loans. During our sample period, the SBA had a zero-subsidy rate from 2014 to 

2019 but required supplemental government funds from 2010 to 2013 (Small Business Administration, 2022). The guarantee fees are based only on loan size and term length and do not 

depend on the percent of the loan guaranteed or the borrower’s risk. This guarantee fee acts 

as a transfer from lenders with low levels of SBA default to lenders with high levels of SBA 

default. As a result, there is cross-subsidization from lenders and borrowers, and in years where 

supplemental funds were needed, the government, to other lenders. This provides an additional 

facet to our analysis in understanding how online banks interact with other, more traditional, 

lenders. 

Within the SBA data, we classify lenders into one of fve mutually exclusive categories with 

online banks being the category of interest. We defne online banks as FDIC-insured institutions 

that do not require any face-to-face interaction to secure a small business loan. We then examine 

which markets they lend to. We fnd that the market share of online banks is higher in counties 

with low per capita income, high unemployment, and low levels of competition from banks. 

These areas are typically associated with lower levels of credit supply and are underserved by 

existing fnancial institutions (Jagtiani and Lemieux, 2018). In addition to higher market share, 

we fnd that online banks lend more dollars in these economically depressed areas. In the lowest 

per capita income tercile counties, online banks lend about 30% more than in other counties. 

This suggests that online banks increase the credit supply to disadvantaged communities. Although the total credit supply increases in these geographies, we document that this effect is not 

explained by increased credit access to minorities or women-owned businesses. 

Next, we examine the motivation behind online banks lending to underserved markets. The 

literature has shown that lenders generally take loan guarantees into account when pricing loans 

3 
and making lending decisions (Bachas, Kim, and Yannelis, 2021). We hypothesize that online 

banks may target particular loan characteristics, such as high guarantees and higher interest 

rates, that they view as attractive. We suspect that online banks are best able to locate borrowers 

that ft these loan profles in economically disadvantaged areas. Analyzing the relationship between SBA guarantees and local economic variables, we fnd higher guarantees are associated 

with lower per capita income, higher unemployment, lower competition from fnancial institutions, lower credit scores, higher rates of subprime borrowers, and higher poverty rates. Given 

the guarantee percentages are not explicitly based on these factors, we take these fndings as 

evidence of online banks’ targeting strategy. 

In comparing the behavior of online banks and other lenders in the SBA 7(a) program, we 

fnd that, on average, online bank loans are larger in size and longer in term. Additionally, online 

bank loans come with higher interest rates. As hypothesized, we fnd that online bank loans have 

on average four percentage points higher SBA guarantee rates (0.2 standard deviations). This 

implies that online bank loans, on average, have a lower cost of default. As expected with a 

lower cost of default, we fnd that online banks are associated with higher rates of charge-offs 

(default) and higher charge-offs as a percent of loan. Given their high guarantees, online bank 

loans end up costing the SBA more on average than loans from other lenders. 

The SBA reimburses lenders for 5% of online bank loans compared to 2% of loans from 

other lenders. Since the SBA often breaks even through fee and collateral collection, this implies 

that online lenders are benefting from the fees paid by other lenders and the collateral recovered 

from borrowers. Since the guarantee fee is based on loan size and term rather than on guarantee 

percent, we interpret this as a transfer from other lenders, borrowers, or, in times where the SBA 

does not have a zero-subsidy rate, the government, to online banks. Additionally, we fnd that 

the charge-off rates and SBA losses of online bank loans are indistinguishable across economic 

markets, suggesting that online banks do target specifc loan characteristics across geographies. 

This paper relates to three major areas of the literature. First, it contributes to the literature 

that documents changes in lending driven by advances in technology. Nonbanks, primarily 

4 
driven by fntech lenders, have flled credit gaps following the 2008 fnancial crisis (Gopal and 

Schnabl, 2022). Implementation of algorithms, big data, and digital footprints allow lenders to 

make decisions based on hard information faster and more effectively (Berg, Burg, Gombovic, ́ 

and Puri, 2019; Fuster, Plosser, Schnabl, and Vickery, 2019; Balyuk, Berger, and Hackney, 

2022; Bartlett, Morse, Stanton, and Wallace, 2022). Since the COVID-19 pandemic, the growth 

in digital banking has accelerated as lenders have been compelled to improve online services to 

meet customer needs and expectations, changing the way borrowers approach fnancing (Pearce 

and Borkenhagen, 2021). The majority of papers in this space focus on fntech nonbanks but 

He, Jiang, Xu, and Yin (2021) document that banks are increasing their spend on information 

technology to remain competitive in an environment full of fntech lenders. We differ from 

previous literature as we examine the effects of the growth and behavior driven by specialization 

and technological innovation of online banks rather than that of nonbanks driven by regulatory 

differences. 

Second, an important aspect of this paper is the SBA program. Much work has been done 

analyzing the effcacy of the SBA program and its impact on the US economy (Krishnan, Nandy, 

and Puri, 2014; Brown and Earle, 2017). There is a recent literature on government guarantees 

and subsidies and the effect of these policies on lender behavior. Bachas, Kim, and Yannelis 

(2021) show bunching around guarantee thresholds and fnd that raising guarantee rates causes 

the per-loan amount to increase. These guarantees create a redistribution of credit from lowrisk to high-risk frms. Because of the decreased cost of default, lenders take on greater levels 

of risk and are less motivated to collect information on their borrowers (Stillerman, 2021). In 

concentrating on the behavior of online banks, which can target borrowers across markets, we 

expand the understanding of the effcacy of the SBA in modern lending, how lenders view 

guarantees, and whether the guarantees are having their intended effect. 

Third, this paper relates to work on credit access. There is a wide literature that examines 

the impact of new lender types and increased competition on credit availability. The consensus 

is that the entrance of a new lender typically increases credit access (Black and Strahan, 2002; 

5 
Cetorelli and Strahan, 2006; Rice and Strahan, 2010). Fintech lenders specifcally have been 

found to expand consumer credit access into highly concentrated and economically challenging 

markets (Jagtiani and Lemieux, 2018; Cornelli, Frost, Gambacorta, Rau, Wardrop, and Ziegler, 

2020). This suggests that fntech lenders have the potential to penetrate underserved markets, 

increasing credit availability. Di and Pattison (2023) present evidence of remote industryspecialized lenders in SBA lending, fnding that entry of specialized lenders is associated with 

increased credit access within the industry. Though it is not always the case that new lenders 

expand credit access. For example, Gormley (2014) fnds foreign lender entry has a negative 

effect on credit supply as foreign lenders tend to lend to only the best borrowers, reducing 

credit to all other frms. Therefore, while additional lenders usually lead to an expansion of 

credit access, certain lenders such as those that practice cream skimming may have the opposite 

effect. This paper adds to this literature by focusing on the increased credit access to small 

businesses by online lenders as an unintended consequence of their specialized lending model. 

Most importantly, to the extent of our knowledge, this is frst paper to document that such an 

increase in credit access implies a cross subsidization from traditional lenders, borrowers, and 

the government to online lenders. 

The rest of the paper continues as follows. Section I describes the institutional background 

of the SBA program and the data used in analysis. Section II provides results related to bank 

specialization and SBA loan characteristics. Section III looks at credit access implications, 

Section IV looks at loan performance, and Section V provides concluding remarks. 

Institutional Background and Data 

I.A SBA 7(a) Program 

The SBA (Small Business Administration) was founded in 1953, its goal is to “grow businesses 

and create jobs” (Small Business Administration, 2012). The SBA attempts to realize this goal 

6 

I 
through its central programs, 7(a) and 504. These programs provide fnancing to small businesses that would otherwise have diffculty accessing credit. SBA funding is primarily used 

to support working capital, make PPE purchases, expand into new markets, refnance existing 

debt, and create or acquire new business. These loans are made by participating fnancial institutions, with the SBA guaranteeing a portion (typically 50-90% depending on loan size and 

type) of the loan to offset the additional risk being taken on by the fnancial institution. For 

example, if an SBA loan with a guarantee of 75% defaults with a $50,000 remaining balance, 

the SBA assumes $37,500 of the loss and the fnancial institutions assumes $12,500. 

The effects of the SBA program have been examined by the literature. Krishnan, Nandy, 

and Puri (2014) fnd that the SBA program increases access to credit. Through this increased 

credit access, small businesses create jobs in local economies and participate in “productive 

projects that may otherwise not be taken up.” Brown and Earle (2017) estimate the cost per job 

from SBA 7(a) and 504 loans to be between $21,580 and $25,450. They compare this to the 

cost per job of other government programs including the American Recovery and Reinvestment 

Act at $158,000 to $407,000 (Neumark, 2011) and the New Jobs Tax Credit at $37,500 to 

$75,000 (Bartik and Erickcek, 2010). This suggests that SBA loans are a comparatively lowcost government method of job generation. 

We focus on the SBA’s original and most common loan program, the 7(a). To qualify for 

a 7(a) loan, a small business must meet the following requirements. It must be a for-proft 

business, conduct business within the United States, and ft the SBA’s defnition for a small 

business. This defnition varies across industry but is based on the frm’s number of employees 

and its annual receipts (“total income plus cost of goods sold”). These businesses must also 

show that they have need for the loan, cannot get reasonable terms for funding elsewhere, have 

equity invested in the business, and have already expended alternative funding including personal resources. Additionally, any business that is delinquent on existing debt obligations to the 

U.S. government is ineligible for a 7(a) loan. 

The maximum loan amount for any 7(a) loan is $5 million. Interest rates are decided by the 

7 
lender and can be fxed or variable but must not exceed the SBA maximums which are pegged 

to the LIBOR rate. Within the 7(a) program, there are several loan types, referred to by the SBA 

as “delivery methods", that differ on loan purpose, loan amount, eligibility requirements, SBA 

turnaround time, and guarantee percentage. Descriptions of the main delivery methods with 

their accompanying guarantee rate can be found in Table A.2. For example, the Standard/Small 

7(a) loan has a maximum loan amount of $5 million and a turnaround time of 5 to 10 business days. The SBA guarantees 85% of Standard 7(a) loans that are less than $150,000 and 

75% of those greater than $150,000. Alternatively, the SBA Express program features a SBA 

turnaround time of 36 hours, a maximum loan amount of $500,000, and a maximum guarantee 

of 50%. In exchange for a lower guarantee, banks that use the SBA Express program are permitted to use internal documentation and loan-vetting procedures rather than the standard 7(a) 

documentation. 

Only SBA-certifed lenders are eligible to make 7(a) loans. An experienced SBA lender 

can become a PLP (Preferred Lenders Program). A lender with PLP status has the authority to 

generate SBA loans without SBA review. Using their PLP status, these lenders can make loans 

through any of the subprograms including the Standard/Small 7(a) or SBA Express. Of the 15 

programs in our sample, about one-third of the loans are Standard/Small 7(a) loans originated 

using PLP status and about half are through the SBA Express program. 

Lenders that participate in the SBA are subject to certain fees which we describe in detail 

in Section I.B. These fees depend on loan size, maturity, and current SBA legislation. The SBA 

uses these fees to operate and fund its programs and administration. The SBA is required to 

report the amount of government funding it receives each year. The goal of the SBA is for 

its programs to generate adequate funds through fees and collateral repossession to break even 

on defaults and other organizational costs. The SBA 7(a) program has a defcit from 2010-

2013 but had a “zero subsidy rate,” meaning that it broke even, from 2014 to 2019. (Small 

Business Administration, 2022). 

8 
I.B SBA Fees 

To participate in the program, lenders are required to pay fees to the SBA on all 7(a) loans. 

These fees include an upfront guarantee fee, an annual servicing fee, and a prepayment fee. It 

is important to note that lenders can pass these fees on to the borrower. The SBA regularly 

revises these fees, releasing updates each October. Exact fee percentages by year can be found 

in Table A.3. 

The guarantee fee is paid upfront to the SBA to cover potential defaults. It is generally 

deducted from the initial loan amount or added into the total cost of the loan and paid by the 

borrower. This fee ranges from 0-3.75% of the guaranteed portion of the initial loan amount. 

The exact percentage depends on loan size and loan maturity with smaller, shorter-term loans 

requiring lower fees. We use initial loan size, guarantee rate, loan maturity, and the SBAreleased fee percentages to calculate the upfront guarantee fee for each loan. 

SBA loans include a yearly servicing fee ranging from from 0-0.55% paid to the SBA on the 

guaranteed portion of the outstanding balance. This fee goes toward funding SBA operations. 

We assume that the ongoing servicing fee is paid every 12 months after the initial disbursement 

date on the guaranteed portion of the outstanding balance. We use the sum of these annual fees 

to generate the total ongoing servicing fee amount paid over the life of the loan. 

The SBA requires a prepayment fee on all loans that have a maturity of more than 15 years 

that are paid in full within the frst 3 years after disbursement. This fee is 5% of the prepayment 

amount if prepaid in the frst year, 3% of the prepayment amount if prepaid in the second year, 

and 1% of the prepayment amount if prepaid in the third year. Prepaying on loans that do not 

fulfll this criteria does not induce fees. 

Since we are unable to observe the individual loan payments, we assume that all borrowers 

follow a traditional monthly payment schedule calculated using initial amount and interest rate. 

We assume that borrowers pay this calculated amount each month until the loan is either paid 

in full or charged off. If a loan is paid prior to its given maturity, we assume that the loan is 

9 
paid following the normal schedule until the fnal month of payment in which we assume that 

the entirety of the remaining balance is paid. If a loan is classifed as charged off, we assume 

that the loan is paid following the normal schedule until 90 days (3 months) prior to the charge 

off date, after which we assume that the borrower stops paying the remaining balance. 

I.C Lender Classifcation 

We classify the SBA lenders into one of fve mutually exclusive categories: online banks, big 

banks, traditional banks, credit unions, other lenders. The lender group of focus is online banks 

as they are rapidly gaining market share and their lending is not limited by geographic location. 

This group is comprised of deposit-taking institutions whose lending operations do not require 

any in-person interactions (Buchack, Matvos, Piskorski, and Seru, 2018). To identify whether 

a bank requires in-person interaction, we conservatively classify online banks as having less 

than ten physical branches and less than 10% of their loans coming from the state in which 

they are located. Big banks are deposit-taking institutions in the 99th percentile for size as 

measured by assets. Traditional banks are deposit-taking institutions that are not classifed as 

online banks or big banks. Credit unions are lenders who are overseen by the National Credit 

Union Administration. The other lenders category comprises lenders that do not ft into one of 

the prior categories. This includes the group of “SBA supervised lenders” that are not deposittaking institutions. 

Online banks are responsible for 5.5% of the loans in our sample. The ten largest online 

banks by number of loans are listed in Table A.4. Of the other lenders, big banks make up about 

30.6%, traditional banks make up about 56.7%, credit unions make up about 2.9%, and other 

lenders make up about 4.5%. 

While online banks make up 5.5% of the total loans, in Figure 2, we note that its market 

share is increasing over the period from about 1% in 2010 to about 9% in 2019. Currently, 

this is the principal mechanism for new fntech lenders to enter the SBA 7(a) market. For non10 
depository institutions, the SBA has only 14 licenses delegated to “Small Business Lending 

Companies” (SBLCs), and have had a moratorium on creating new licenses since 1982. The 

other non-depository categorization, state-regulated lenders called “Non-Federally Regulated 

Lenders” (NFRLs), are largely restricted to lending within their respective state. So as opposed 

to other lending markets, the SBA 7(a) program has enabled fntech activity through online 

banks. These banks either develop their own lending franchises or partner with fntech lenders. 

For example, all loans made prior to October 2020 by Kabbage, a major fntech small business 

lender, were issued by Celtic Bank, an online Utah-Chartered Industrial Bank (Kabbage, 2022). 

Therefore, any SBA 7(a) loans originated by Kabbage would be reported by Celtic Bank. Gopal 

and Schnabl (2022) describe these partnerships between a fntech lender and a “funding bank.” 

They explain that these fntech lenders attract borrowers online but that the funding bank is who 

makes the loan. These funding banks often immediately sell the loan to the fntech lender and 

are frequently located in areas with lighter regulation but they make loans nationwide such as 

Celtic Bank being headquartered in Utah. 

I.D Data 

The primary source of data is the SBA 7(a) program. The SBA requires all its lenders to 

report any 7(a) loan applications. This data can be accessed publicly through the Freedom of 

Information Act (FOIA) on the SBA website. The overall sample contains all 545,751 SBA 7(a) 

loans made from 2010 to 2019 by 3,397 unique lenders. Because the SBA requires lenders to 

report all loan applications, there are loans in the sample that are canceled prior to disbursement. 

To account for this, we remove any loans with a status of canceled. This leaves us with 481,018 

loans and 3,274 unique lenders. We remove observations that are missing key variables of 

analysis, giving us our sample of 459,725 loans and 3,218 unique lenders. The SBA 7(a) data 

contains information on the borrower, lender, and loan terms. This includes borrower name, 

location and industry; lender name and location; and loan interest rate, amount, term in months, 

11 
type, status, and the number of jobs supported. 

We carefully match lenders with bank-level control variables from FDIC Summary of Deposits (SOD), Call Reports, and Statistics on Depository Institutions (SDI) data by bank name 

and location. Using SDI bank branch data, we generate the number of SBA bank branches in 

the borrower’s zip code and the physical distance between each SBA borrower’s zip code and 

the zip code of the nearest SBA bank branch. All FDIC-insured institutions are required to fle 

quarterly updates on fnancial, demographic, and structural information. Because our sample 

consists of banks, credit unions, and nonbanks, not all lenders have these variables available. 

Over 90% of the loans in our sample come from FDIC-insured institutions therefore, the majority of observations are matched. 

To allow for economic location analysis, we match borrower location by county and zip 

code with data from the US Census, the Bureau of Economic Analysis (BEA), County Business 

Patterns (CBP), and a major credit bureau. This economic data allows us to include locationspecifc characteristics such as income, employment rates, credit scores, rate of subprime borrowers, and poverty rates, in our analysis. 

We additionally pull data from the USAspending.gov website which supplies a comprehensive report of US federal, state, and local government spending, including information on SBA 

guaranteed loans. This data provides business ownership information on the individual borrowers as frms self-identify under categories such as minority, woman, veteran, and disadvantaged 

owned. We assume that if a borrower is not classifed into one of these categories that the owner 

is not a minority, woman, veteran, or disadvantaged. This is carefully merged with the original SBA data using a fuzzy merge on borrower name and location. The merged data contains 

274,458 loans and 2,998 unique lenders. 

From Table I, we see that the median loan size is $125,000, median interest rate is 6%, and 

median term is seven years. About 30% of loans in the sample are a revolving line of credit. 

Looking at borrower business type, 88% of loans are made to corporations, 11% to individuals, 

and 2% to partnerships. The borrowers have a median business age of 2.5 years. The median 

12 
borrower in our sample has 6 branches of SBA lenders in their zip code. 

Lenders are required to regularly update the status of these loans, and in our sample, 57% of 

the loans have been paid in full, 1% are undisbursed, 5% are charged off (deemed uncollectable 

by the lender), 37% are exempt (the loan has been disbursed but it has not yet been canceled, 

paid in full, or charged off). The average fees are about $8,000 for the upfront guarantee fee, 

$9,000 for the total ongoing servicing fee, and $400 for the prepayment fee. From the BEA 

data, we see that our sample has an median county per capita income of $47,970 and median 

county unemployment rate of 5.2%. Using SDI reports, we calculate our sample to have a 

median county scaled HHI of 0.039. 

From the matched data, we report that 0.8% of the borrowers self-identify as a woman 

owned business while 0.6% self-identify as a minority owned business. We compare the matched 

sample to the overall sample in Table II and fnd the samples to be similar in terms of loan attributes, borrower types, and loan outcomes. 

II Specialization and SBA Loans 

II.A Bank Specialization 

We argue that online banks, given the nature of their business structure, will specialize in certain 

product types rather than offering a broad set of products in a fxed geographic footprint. To 

establish if online banks indeed specialize in this way, we run the following regression for lender 

b in year t: 

Specialization Measurebt =β1Online Bankb +β2Bank Controlsbt−1 +γt +εbt , (1) 

We include seven different specialization measures. From the bank’s Call Report data, we include the HHI of asset types (Assets HHI), liability types (Liabilities HHI), income sources 

13 
(Income HHI), and types of loans and leases Loans HHI (CR). We assume a higher concentration in these measures indicates more specialization in the bank’s business model. We also 

include two alternative measure of loan activities using the SDI data: an HHI measure and the 

number of distinct loan and lease categories for which the bank reports activity (Loans HHI 

(SDI) and Loan Categories). We include the bank’s size, equity ratio, deposit ratio, and ROA 

as controls, along with year fxed effects. 

Table III presents the results. We fnd that online banks have a more specialized balance 

sheets than other banks. The HHI of assets, liabilities, and income sources are all higher for 

online banks, controlling for other bank characteristics. These results are statistically signifcant 

at the 1% level for assets (column 1) and at the 10% level for liabilities and income sources 

(columns 2 and 3). 

Turning to the type of loans that banks originate, we again fnd that online banks are more 

concentrated. This holds for both the HHI measures and the number of loan categories online 

banks report. The effects are economically meaningful: the difference between online banks 

and other lenders for Loans HHI (CR) is 75% of the sample standard deviation in this measure 

and statistically signifcant at the 1% level (column 4). The similar calculation for the alternative 

loan HHI measure (Loans HHI (SDI)) is about 50% of a sample standard deviation (column 5). 

As the average bank reports loans in 11 different categories, the fact that online banks on average 

have two fewer categories is also meaningful (column 6). Together, we take these fndings as 

evidence that online banks are more specialized in their overall balance sheet assets and the 

types of lending they undertake. 

II.B SBA Loans: Univariate Differences 

As online banks are more specialized than other lenders, we see if this affects the characteristics 

of their SBA loans using a simple univariate test. In Table IV, we report the means and differences between online banks and all other lender groups. We fnd that online bank loans are 

14 
larger in size and longer in term with online loans being $284,000 larger and three years longer 

on average than other lender loans. Interest rates appear similar. Online banks loans have a 

higher SBA guarantee with the average online bank loan being 79% guaranteed compared to 

the average of other lender loans at 64% guaranteed. Given the difference in guarantee percents, 

it is unsurprising that 76% of online bank loans are through the Preferred Lender Program (PLP) 

(which carries a 75% to 85% rate) while only 3% of online bank loans are SBA Express (which 

carries a 50% guarantee rate). This compares to other lenders with 30% PLP loans and 51% 

SBA Express loans. 

Non-online bank lenders have signifcantly higher rates of being paid in full. This is expected as much of the online bank loans are concentrated in the later half of the data. What 

cannot be explained by an increasing market share is that online bank loans are associated with 

an increased rate and dollar amount of charge-offs. Online loans have an average charge-off 

to loan (the amount charged off divided by the total loan amount) of 7.3% compared to other 

lenders at 3.5%. Additionally, online loans have an average SBA loss to loan (the amount 

charged off times the SBA guarantee percent divided by the total loan amount) of 5.5% which 

is three percentage points higher than that of other lenders. 

Looking at the differences in fees between online banks and other lender, we see that online 

bank loans carry higher upfront, ongoing, and prepayment fees. Online bank loans have, on 

average, about $7,000 higher upfront guarantee fees, $8,000 higher ongoing servicing fees, 

and $400 higher prepayment fees than loans of other lenders. This is expected as these fees are 

determined by size and maturity and online banks loans are larger in size and longer in maturity. 

II.C SBA Loans: Amounts, Interest Rates, and Guarantees 

With a more specialized business model, online banks are expected to behave differently than 

traditional lenders and may target specifc loan features and types. The univariate evidence 

shows that these banks favor the SBA loan programs that carry higher guarantee rates compared 

15 
to other banks. It is less clear that these banks charge higher interest rates or make larger loans, 

controlling for the type of borrower, geographic location, and the year when the loan is made. 

To test this, we estimate the following equation using loan level data: 

Loan Variablelct =β1Online Bankb +β2Loan Controlslt +β3Borrower Controlsft 

+β4Bank Controlsbt +αct +τnt +εlct , (2) 

where l, f , n, b, c, and t represent loan, borrower, borrower industry, lender, county, and year, 

respectively. Loan Variable is either the Log Loan Amount, Interest Rate, and SBA Guarantee 

Percent. The independent variable of interest is Online Bank, which is an indicator equal to one 

if the lender is classifed as an online bank and zero otherwise. Loan controls include Loan Size 

Group, which is loan size grouped into terciles; Revolver Status, which is an indicator equal 

to one if the loan is a revolving line and zero if it is a term loan; Initial Interest Rate which 

is the interest rate at the time of approval; and Log Term in Months, which is the log of the 

loan term length as measured in months. Borrower controls include business type, Business 

is Corporation and Business is Individual, excluding Business is Partnership and Branches in 

Zip Code which is the number of branches of SBA lenders in the borrower’s zip code.1 Bank 

controls include Bank Log Assets, Bank Bank Deposits to Assets, Bank Return on Assets, and 

Bank Equity to Assets. We include county-year fxed effects (αct) and industry-year fxed effects 

(τnt) to control for time-variant county and borrower industry characteristics. Standard errors 

are clustered at both the lender and borrower level. 

In Table V, column 1, we consider loan size. We fnd that online banks do not give larger 

loans, when accounting for other loan characteristics, borrower characteristics, and fxed effects. 

This fnding is in contrast to the univariate evidence, which shows larger average loan sizes for 

online banks. This difference suggests that online banks target borrowers and loan types with 

1We include business age as an additional borrower control and the results remain with its inclusion but have 

removed it from the fnal specifcation as over 80% of observations are missing business age. 

16 
larger loan amounts, but offer similar loan amounts as other lenders for these borrowers. 

Another dimension of the lending decision is the interest rate on these loans. While the 

SBA mandates maximum caps on the interest rate, a substantial fraction of loans are originated 

below these caps. Indeed, only about 5% of loans are originated at the maximum cap rate. The 

average loan is priced at about 67% of its maximum. In column 2, we test whether online banks 

differentially price their loans. 

We fnd that online banks charge 0.5% higher interest rates than other banks. This difference 

is signifcant at the 1% level and is economically large, given the sample standard deviation of 

1.5%. This further supports the idea that online banks are being strategic in pricing their loans 

and points to two factors. First, insofar as the borrower, county, industry, and other controls do 

not fully capture the creditworthiness of a borrower, these banks are likely making loans to the 

riskier borrowers. Second, these banks are receiving higher income streams from these loans 

prior to default. 

In column 3, we look at differences in the SBA guarantee rates. Consistent with online banks 

utilizing programs with higher guarantees, we see that their loans have a 4.6% higher guarantee 

rate on average. This difference is signifcant at the 1% level. This supports our hypothesis that 

online banks target loans with high guarantees even after we control for the borrower’s location, 

industry, and the other loan attributes. 

In Figure 6 we show the percent of loans relative to the total number of loans approved under 

the status of Preferred Lender (PLP) by online and not online lenders in the sample. These loans 

have a guarantee rate up to 85% for loans below $150,000. After that threshold the guarantee 

rates decrease to 75%. The graph shows evidence of a jump for both type of lenders around the 

loan size of $150,000. However, the jump for online lenders is from less than 5 percent to 20 

percent at the threshold. 

For robustness, we estimate the effect of online banks on guarantee rates, applying additional fxed effects, in Table A.9. In column 1 we include county-year fxed effects and 

industry-year fxed effects as in previous regressions. In column 2, we apply borrower and 

17 
industry-year fxed effects and in column 3, we apply borrower-year fxed effects. We fnd that 

across the various specifcations, online loans are associated with 4.5-6% higher guarantee rate 

on average, which is signifcant at the 1%. 

To ensure that our results are not motivated by online banks lending to borrowers without 

local SBA lending options, we include an additional independent variable, Distance to SBA 

Lender, and its interaction with the variable Online Bank in our specifcation. Distance to SBA 

Lender is the physical distance in miles between the borrower and the nearest branch of an 

SBA lender. When including this variable and interaction in Table A.11, our results still hold, 

suggesting that they are driven by online bank behavior rather than by some demand or borrower 

effect. 

II.D SBA Loans: Charge-Offs and Losses 

Online banks charge higher interest rates and utilize programs with higher guarantees. To confrm if their loans are indeed riskier, we estimate the following equation: 

Loss Variablelct =β1Online Bankb +β2Loan Controlslt +β3Borrower Controlsft 

+β4Bank Controlsbt +αct +τnt +εlct, (3) 

where Loss Variable is either Charge-Off Status, Charge-Off to Loan, SBA Loss to Loan, or 

SBA Proft to Loan. Charge-Off Status is an indicator equal to one if the loan is deemed as 

uncollectable by the lender and zero otherwise. Charge-Off to Loan is the amount deemed as 

uncollectable divided by the total loan amount. SBA Loss to Loan is the amount deemed as 

uncollectable multiplied by the SBA guarantee percent, net of fees paid to the SBA, divided 

by the total loan amount. SBA Proft to Loan is the total sum of fees (upfront guarantee fee, 

ongoing servicing fee, and prepayment fee) net the guaranteed portion of the default amount (in 

the case of a default), divided by the total loan amount. As with Equation (2), we include the 

18 
aforementioned loan controls, borrower controls, bank controls, county-year fxed effects (αct), 

and industry-year fxed effects (τnt). 

The results of this equation can be found in Table V, columns 4-7. In column 4, we fnd that 

loans from online banks are 6.1% more likely to be charged off. This estimate is statistically 

signifcant at the 1% level and economically large, as the average charge-off rate is 5.3%. Turning to charge-off amounts, we fnd the coeffcient on Online Bank to be 4.814 with signifcance 

at the 1% level. So the average charge-off is over 4.8% higher for online lenders across their 

portfolios as a fraction of their lending. 

A central aspect to the SBA program is the guarantees. In the case of non-guaranteed loans, 

a bank with a high level of defaults would be seen as a poor performer that does not price its 

loans appropriately. In the case of guaranteed loans, lenders take into account the decreased 

cost of default when pricing loans and may be willing to accept higher rates of default. The 

guarantees allow online lenders to offset high charge-offs as the SBA covers a major portion of 

the losses. 

The results for SBA losses are presented in column 6. We fnd the coeffcient on Online 

Bank to be 3.495 with signifcance at the 1% level. The results for SBA profts are presented 

in column 7. We fnd the coeffcient on Online Bank to be -0.039 with signifcance at the 1% 

level. These results imply that much of the losses of online bank charge-offs are transferred to 

the SBA. Online banks loans cost the SBA more than loans of other lenders. Given the SBA 

has broken even during many of the years in our sample, we can view this transfer as coming 

from either the guarantee fees paid by other lenders, the borrower collateral that is recovered, 

or in some years, government funds. 

II.E Lenders Internal Rate of Return 

To analyze whether SBA loan guarantees induce higher loan proftability for online lenders, 

we estimate the lender’s internal rate of return (IRR) based on the initial loan amount and the 

19 
estimated annual cash fows that lenders might receive throughout the life of the loan. 

To calculate the lender IRR, we make various assumptions to estimate yearly cash fows 

using the loan terms provided by the SBA. We frst calculate an estimated monthly payment 

using the initial loan amount, initial interest rate, and term in months. For loans with an exempt 

status (those that are disbursed but have not yet been paid in full or charged off), we assume that 

borrowers pay this payment monthly for the full term of the loan. For loans that are paid-infull, we assume that borrowers pay the estimated monthly payment until the given paid-in-full 

date, where we assume the remaining balance of the loan is paid. For loans that are charged 

off, instead of paying the estimated monthly payment, we assume that borrowers only pay the 

difference between the initial loan amount and the reported default amount. Borrowers pay this 

difference in equal monthly payments from the disbursement date until three months before the 

charge-off date. Upon the date of default, we assume that lenders receive the guaranteed portion 

of the charge-off amount (the default amount times the SBA guarantee percent). 

In Table I of summary statistics, we estimate a mean lender IRR of 4.8% and a standard 

deviation of 4.9%. In Table IV Panel B, we show that the mean of the lender IRR for online 

lenders is signifcantly higher, almost 5% versus 4.8% for other lenders. In the top panel of 

Figure 7, we show the distributions of the lender’s IRR for online lenders and other lenders. 

We develop a simple simulation exercise assuming a reduction in the loan guarantee rates 

that the SBA pays to online lenders only. Online lenders seem to target loans with high SBA 

guarantee rates. Therefore we assume in scenario one that online lenders for all loans with 

guarantee rates above or equal to 75% will receive only 50%. In scenario two, we assume 

the same, but we increase the threshold assuming loans with guarantees rates above or equal 

to 85%. In the third scenario, we assume that online lenders will receive only 50% of the 

guarantee for loans with guarantee rates of 90% and above. Table A.10 presents the results of 

these simple simulations. For scenarios one and two, the average online lender IRR decreases 

between 70 and 80 basis points. However, online lenders still have a higher IRR for the third 

scenario. These results imply that online lenders target a higher volume of loans with guarantee 

20 
rates between 75% and 85% and less volume of loans with guarantee rates of 90% or more. 

III Credit Access 

III.A County Credit Supply 

To understand which markets online banks lend to, in Figure 3 we plot the percent of loans made 

by online banks in a county by the average of various economic market variables. The economic 

measures include: Per Capita Income, which is the average per capita income in the county; 

Unemployment Rate, which is the county’s percent of labor force that is unemployed; Scaled 

Bank HHI, which is the sum of squared shares of local branch deposits in the county scaled 

by 10,000; Credit Score, which is the average credit score at the zip code level; Household 

Income, which is the average household income at the zip code level; Percent Subprime, which 

is the percent of borrowers designated as having a subprime credit rating (below 640) at a zip 

code level; and Poverty Rate, which is the average poverty rate in a given zip code. For the 

purpose of the fgure, we average the zip-code-based economic measures to the county level. 

We fnd that county market share of online banks is negatively correlated with average per capita 

income, average credit score, and average household income. County market share of online 

banks is positively correlated with average unemployment, average bank HHI, average percent 

subprime, and average poverty rate. This implies that the market share of online banks is greater 

in economically depressed and underserved areas. 

These patterns suggest that targeting high guarantee rates may cause online banks to lend 

more in economically distressed areas. Indeed, in Figure 4, we plot the average SBA guarantee 

by the same economic market variables and notice similar patterns. The average SBA guarantee 

in a county is positively associated with unemployment rate, bank HHI, percent of subprime 

lenders, and poverty rate. It is negatively associated with per capita income, credit scores, and 

household income. Although guarantee rates are dictated by the specifc SBA program used 

21 
and not the borrower’s location, the online banks lending activities drive the strong correlations 

documented in Figure 4. 

A central question is whether online banks focus their lending supply into these more depressed regions. After all, a primary purpose of the SBA is to extend credit to borrowers that 

would not receive it otherwise. If other lenders actively avoid these regions and these regions 

have lower credit demand, this combination could drive the observed market shares. To better understand the credit allocation across counties, we organize the data into lender-countyyear observations and split the counties into terciles based on the county-level measures of Per 

Capita Income, Unemployment Rate, and Scaled Bank HHI. This allows us to see if more economically disadvantaged counties receive more credit supply from online banks.2 We estimate 

the following equation: 

Log Loan Amountbct =β1Online Bankb 

+β2Online Bankb ×Bottom Tercile Economic Variablect 

+β3Online Bankb ×Top Tercile Economic Variablect 

+β4Controlsbt +αct +εbct, (4) 

where b, c, and t represent lender, county, and year, respectively, and Log Loan Amount is the 

log of a lender’s total amount loaned in a county-year. The independent variable Online Bank 

is an indicator equal to one if the lender is classifed as an online bank and zero otherwise. For 

each economic variable, the middle tercile is the excluded category, meaning the estimates for 

the other terciles are relative to it.3 We control for bank log assets (Log Assets), total bank 

deposits divided by total bank assets (Deposits to Assets), bank ROA (Return on Assets), total 

bank equity divided by total bank assets (Equity to Assets). We include αct as county-year fxed 

effects to address time-variant county characteristics. These fxed effects control for the level 

2A similar specifcation but using continuous versions of these economic variables is presented in Appendix 

Table A.5. 

3The standalone versions of these tercile variables are absorbed by the county-year fxed effects. 

22 
of credit demand in a county and allows us to interpret the regression coeffcients as measures 

of credit supply (Khwaja and Mian, 2008). Standard errors are clustered at the bank level. 

The results of this equation can be found in Table VI. Column 1 includes Online Bank, the 

interaction between Online Bank and Bottom Tercile Per Cap Income, and the interaction between Online Bank and Top Tercile Per Cap Income. We fnd that the coeffcient on Online 

Bank × Bottom Tercile Per Cap Income is 0.289 and signifcant at the 1% level while the coeffcient on Online Bank × Top Tercile Per Cap Income is insignifcant. This estimate implies 

that for the counties with the lowest per capita income, online banks provide 34% more credit 

to these counties than the middle tercile counties.4 Column 2 includes Online Bank, the interaction between Online Bank and Bottom Tercile Unemployment, and the interaction between 

Online Bank and Top Tercile Unemployment. We fnd that the coeffcient on Online Bank ×

Bottom Tercile Unemployment is insignifcant but the coeffcient on Online Bank × Top Tercile 

Unemployment is 0.336 and signifcant at the 1% level. This estimate implies online banks provide about 40% more credit to the most unemployed counties compared to the middle tercile.5 

Taken together, these results show that online banks focus much of their lending in either the 

poorest or most economically-troubled counties, given they have about 30-40% more lending 

in these counties compared to the middle tercile counties. There is relatively little difference 

between the economically mid-level and strongest counties in terms of loan supply by these 

online banks. 

Turning to bank competition, column 3 includes Online Bank, the interaction between Online Bank and Bottom Tercile HHI, and the interaction between Online Bank and Top Tercile 

HHI. We fnd that the coeffcient on Online Bank × Bottom Tercile HHI is -0.213 and is signifcant at the 1% level and the coeffcient on Online Bank × Top Tercile HHI is 0.126 and is 

signifcant at the 1% level. Here the relationship is more linear—online banks provide more 

loan supply as counties have less local banking competition. Overall, the fndings in Table VI 

4The calculation is e.289 −1 = .335. 

5The calculation is e.336 −1 = .399. 

23 
suggest that online banks lend more in areas with lower income, higher unemployment, and 

lower bank competition and less in areas with higher bank competition. 

III.B Minority Credit Supply 

We fnd evidence that online banks supply more credit to economically-worse counties. This 

credit access is in line with the goals of the SBA. A second element of the SBA is to provide 

credit to groups who have traditionally been underserved, such as women-owned and minorityowned businesses. We therefore look at whether online bank behavior increases credit access 

for these groups. To test this we estimate the following equation using the matched loan-level 

data: 

Log Loan Amountlct =β1Online Bankb +β2Online Bankb ×Ownershipf (5) 

+β3Ownershipf +β4Loan Controlslt +β5Borrower Controlsft 

+β6Bank Controlsbt +αct +τnt +εlct, (6) 

where l, f , n, b, c, and t represent loan, borrower, borrower industry, lender, county, and year, 

respectively, and Log Loan Amount is the log of the total loan amount. The key independent 

variables are Online Bank, an indicator equal to one if the lender is classifed as an online 

bank and zero otherwise; Ownership, the self-identifed ownership status of the borrower such 

as minority or woman owned; Online Bank × Ownership, the interaction between the two. In 

effort to separate the relationship, we include loan, borrower, and bank controls. Standard errors 

are clustered at both the bank and frm level. 

In Table VII, we report the results for this equation. We fnd that the coeffcients on Woman 

Owned, Minority Owned, and Hispanic Owned to be positive and signifcant while the coeffcients on the interactions terms are negative and signifcant. Conditional on getting a loan, 

these groups of borrowers receive smaller loans from online banks than from other lenders. 

24 
This suggests that online banks do not create an expansion in credit supply to minorities or 

women-owned businesses.6 For robustness, in Table A.8 we repeat our analysis using only 

observations that self-identify into at least one of the SBA business type categories to ensure 

that our results are not driven by a reduced number of borrowers of online banks choosing not 

to self-identify. We fnd directionally similar results after dropping observations, although not 

statistically signifcant. 

IV Loan Performance Across Different Areas 

We have established that online banks make loans through the SBA with higher guarantees 

and higher interest rates. These loans also have higher charge-offs and SBA losses. We also 

fnd that much of the online banks’ credit is supplied to areas with the lowest income, highest 

unemployment, and lower levels of banking competition. Given this concentration of lending, 

a natural question is whether these specifc loan performance results are concentrated in these 

markets. As a fnal piece of analysis, we therefore assess how these charge-offs and losses differ 

across economic markets. We estimate the following equation: 

Loan Perf. Variablelct =β1Online Bankb +β2Online Bankb ×Economic VariableZIPt 

+β3Economic VariableZIPt +β4Loan Controlslt 

+β5Borrower Controlsft +β6Bank Controlsbt +αct +τnt +εlct , (7) 

where l, f , n, b, ZIP, c, and t represent loan, borrower, borrower industry, lender, zip code, 

county, and year, respectively. Loan Performance Variable is one of three variables: ChargeOff Status, Charge-Off to Loan, and SBA Loss to Loan. These variables are the same as used in 

Section II.D. In this analysis, we include zip code economic market measures Log Credit Score, 

6Appendix Tables A.6 and A.7 present the other loan variables (interest rate, guarantee rate, charge-off status, 

charge-off amount, SBA loss), allowing for differential effects for minority-owned and women-owned businesses, 

respectively. 

25 
Log Income, Subprime, and Poverty Rate and the interactions between Online Bank and these 

measures. The zip-code-level controls allow for a fner gradation of different areas. As with 

previous regressions, we include loan controls, borrower controls, and bank controls. To restrict 

the effect of time-variant local demand and industry characteristics, we incorporate county-year 

and industry-year fxed effects, αct and τ jt. We report standard errors clustered at the lender and 

borrower level. 

Panel A of Table VIII reports results for Charge-Off Status. Like in Table V, we see a 

positive and signifcant relationship between online bank status and charge-off status. We note 

that, as expected, higher charge-offs are associated with economically depressed areas. It is 

of interest to observe the coeffcients on the interaction terms between online bank status and 

the economic market measures, all are near zero and insignifcant. This suggests that online 

banks charge-off rates are affected in a similar manner by local economic conditions as other 

lenders. This implies that online banks target or attract a specifc loan profle or demographic 

associated with higher charge-offs. This loan profle or demographic does not vary by region 

and is consistent with online banks’ market not being defned geographically. 

Next, in Panel B of Table VIII, we turn to Charge-Off Amount. We see a positive and signifcant relationship between online bank status and charge-off to loan. The percent of the loan 

charged off is positively associated with poorer areas. The coeffcient on Log Credit Score in 

column 2 is -2.989 and is signifcant at the 1% level, the coeffcient on Log Income in column 

3 is -0.679 and is signifcant at the 1% level, and the coeffcient on Subprime in column 4 is 

0.012 and is signifcant at the 1% level. The coeffcient on Poverty Rate in column 5 is positive 

but insignifcant. Therefore, the other lenders experience higher losses in these zip codes compared to other zip codes within the same county. Turning to online banks, there is no signifcant 

relationship between the interaction terms and Charge-Off to Loan. This further solidifes the 

idea that the higher charge-offs for online bank loans are not due to some differential exposure 

to local economic conditions. 

Finally, we consider SBA Loss to Loan in Panel C of Table VIII. We see a positive and sig26 
nifcant relationship between online bank status and SBA losses, confrming that online banks 

loans cost more to the SBA than loans from other lenders. As expected, the SBA loss to loan is 

greater in economically-disadvantaged areas. The relationship between SBA Loss to Loan both 

Log Credit Score and Log Income is signifcant and negative while the relationship between 

SBA Loss to Loan and Subprime is positive and signifcant. With no signifcant relationship 

between the interaction terms and SBA Loss to Loan, we fnd that the online bank SBA losses 

evolve similarly to other lenders across geographies. This further indicates that online banks 

target certain loan attributes or demographics as their loan outcomes do not vary according to 

expected location outcomes. 

V Conclusion 

Changes in the banking and lending space, such as regulation, consumer preferences, and technological innovation, have generated entrance and expansion of a new class of non-traditional 

lender. While the literature has addressed much of the impact of shadow and nonbank fntech 

lenders, we focus on the behavior and effects of online banks who specialize in particular activities across broad geographic areas. These online banks are FDIC-insured institutions that 

perform all operations online. Unlike traditional banks, application of technology allows them 

to make lending decisions using hard rather than soft information and lend at a distance. Therefore, they are not limited to borrowers in their immediate geography and can seek loans from 

any market. 

In this paper, we examine the effect of these online banks in small business lending, using 

SBA 7(a) program data. We fnd that online banks lend more in areas that are economically 

disadvantaged and underserved by existing banks, suggesting an increase in credit access. To 

understand why online banks concentrate their lending in these areas, we examine how guarantees and other loan attributes differ across location. In addition to having a larger market share 

of online banks, economically depressed areas have higher SBA guarantee percents. 

27 
Provided that guarantees decrease the cost of default, online banks may favor loans with 

higher guarantees. We see evidence of this as online bank loans are associated with signifcantly 

higher guarantee percents. We fnd that online banks loans are also associated with higher 

rates of default and higher default to loan percent. Given the combination of higher guarantee 

percents and higher defaults, online banks loans cost more to the SBA than loans of other 

lenders. This results in a transfer of the guarantee fees of other lenders, and occasionally, funds 

from the government, to online banks. 

28 
References 

Bachas, Natalie, Olivia S. Kim, and Constantine Yannelis, 2021, Loan guarantees and credit 

supply, Journal of Financial Economics 139, 872–894. 

Balyuk, Tetyana, Allen N. Berger, and John Hackney, 2022, What is Fueling FinTech Lending? 

The Role of Banking Market Structure, Working paper. 

Bao, Zhengyang, and Difang Huang, 2021, Shadow Banking in a Crisis: Evidence from Fintech 

During COVID-19, Journal of Financial and Quantitative Analysis 56, 2320–2355. 

Bartik, Timothy, and George A. Erickcek, 2010, The Employment and Fiscal Effects of Michigan’s Mega Tax Credit Program, Working paper. 

Bartlett, Robert, Adair Morse, Richard Stanton, and Nancy Wallace, 2022, Consumer-lending 

discrimination in the FinTech Era, Journal of Financial Economics 143, 30–56. 

Benetton, Matteo, Greg Buchak, and Claudia Robles-Garcia, 2022, Wide or Narrow? Competition and Scope in Financial Intermediation, Working paper. 

Berg, Tobias, Valentin Burg, Ana Gombovic, and Manju Puri, 2019, On the Rise of FinTechs:  ́

Credit Scoring Using Digital Footprints, The Review of Financial Studies 33, 2845–2897. 

Bharath, Sreedhar, Sandeep Dahiya, Anthony Saunders, and Anand Srinivasan, 2007, So what 

do I get? The bank’s view of lending relationships, Journal of Financial Economics 85, 

368–419 The economics of conficts of interest fnancial institutions. 

Black, Sandra E., and Philip E. Strahan, 2002, Entrepreneurship and Bank Credit Availability, 

The Journal of Finance 57, 2807–2833. 

Brown, J. David, and John S. Earle, 2017, Finance and Growth at the Firm Level: Evidence 

from SBA Loans, The Journal of Finance 72, 1039–1080. 

Buchack, Greg, Gregor Matvos, Tomasz Piskorski, and Amit Seru, 2018, Fintech, regulatory 

arbitrage, and the rise of shadow banks, Journal of Financial Economics 130, 453–483. 

Cetorelli, Nicola, and Philip E. Strahan, 2006, Finance as a Barrier to Entry: Bank Competition 

and Industry Structure in Local U.S. Markets, The Journal of Finance 61, 437–461. 

Cornelli, Giulio, Jon Frost, Leonardo Gambacorta, P. Raghavendra Rau, Robert Wardrop, and 

Tania Ziegler, 2020, Fintech and Big Tech Credit: A New Database, Working paper. 

Di, Wenhua, and Nathaniel Pattison, 2023, Industry Specialization and Small Business Lending, 

Journal of Banking & Finance p. 106797. 

Egan, Mark, Stefan Lewellen, and Adi Sunderam, 2021, The Cross-Section of Bank Value, The 

Review of Financial Studies 35, 2101–2143. 

Erel, Isil, and Jack Liebersohn, 2022, Can FinTech reduce disparities in access to fnance? 

29 
Evidence from the Paycheck Protection Program, Journal of Financial Economics 146, 90– 

118. 

Fuster, Andreas, Matthew Plosser, Philipp Schnabl, and James Vickery, 2019, The Role of 

Technology in Mortgage Lending, The Review of Financial Studies 32, 1854–1899. 

Gopal, Manasa, and Philipp Schnabl, 2022, The Rise of Finance Companies and FinTech 

Lenders in Small Business Lending, The Review of Financial Studies 35, 4859–4901. 

Gormley, Todd A., 2014, Costly information, entry, and credit access, Journal of Economic 

Theory 154, 633–667. 

He, Zhiguo, Sheila Jiang, Douglas Xu, and Xiao Yin, 2021, Investing in Lending Technology: 

IT Spending in Banking, Working paper. 

Jagtiani, Julapa, and Catharine Lemieux, 2018, Do fntech lenders penetrate areas that are underserved by traditional banks?, Journal of Economics and Business 100, 43–54. 

Kabbage, Inc., 2022, Bank Relationships, https://www.kservicing.com/legal/

bank-relationships/. 

Khwaja, Asim Ijaz, and Atif Mian, 2008, Tracing the Impact of Bank Liquidity Shocks: Evidence from an Emerging Market, American Economic Review 98, 1413–1442. 

Krishnan, Karthik, Debarshi K. Nandy, and Manju Puri, 2014, Does Financing Spur Small Business Productivity? Evidence from a Natural Experiment, The Review of Financial Studies 28, 

1768–1809. 

Ljungqvist, Alexander, Felicia Marston, and William J. Wilhelm Jr., 2006, Competing for Securities Underwriting Mandates: Banking Relationships and Analyst Recommendations, The 

Journal of Finance 61, 301–340. 

Neuhann, Daniel, and Farzad Saidi, 2018, Do universal banks fnance riskier but more productive frms?, Journal of Financial Economics 128, 66–85. 

Neumark, David, 2011, Spurring Job Creation in Response to Severe Recessions: Reconsidering Hiring Credits, Working Paper 16866 National Bureau of Economic Research. 

Offce Of Advocacy, U.S., 2019, Small Businesses Generate 44 Percent Of U.S. Economic 

Activity, Press Release No. 19-1 ADV. 

Pearce, Peter, and Malte Borkenhagen, 2021, How Banks Can Redefne the Digital Experience, 

The Wall Street Journal. 

Puri, Manju, and Jörg Rocholl, 2008, On the importance of retail banking relationships, Journal 

of Financial Economics 89, 253–267. 

Rice, Tara, and Philip Strahan, 2010, Does Credit Competition Affect Small-Firm Finance?, 

The Journal of Finance 65, 861–889. 

30 
Santikian, Lori, 2014, The ties that bind: Bank relationships and small business lending, Journal 

of Financial Intermediation 23, 177–213. 

Small Business Administration, U.S., 2012, Agency fnancial report, Fiscal year 2012, . 

Small Business Administration, U.S., 2022, Small Business Administration 7(a) Loan Guaranty 

Program, . 

Stillerman, David, 2021, Loan Guarantees and Incentives for Information Acquisition, Working 

paper. 

Yasuda, Ayako, 2005, Do Bank Relationships Affect the Firm’s Underwriter Choice in the 

Corporate-Bond Underwriting Market?, The Journal of Finance 60, 1259–1292. 

31 
Figure 1: Online Bank Market Share by Number of Loans Over Time 

This table displays the percent of loans in the SBA 7(a) program that are made by online banks each 

year from 2010 to 2019. 

32 
Figure 2: Online Bank Market Share by Loan Amount Over Time 

This table displays the percent of total loan amount in the SBA 7(a) program that are made by online 

banks each year from 2010 to 2019. 

33 
Figure 3: Percent Online by Average Economic Measures 

Employing county-level data, these fgures present the average of various economic measures by the 

percent of loans that are made by online banks. The measures used are the per capita income, 

unemployment rate, bank concentration (scaled HHI), credit score, household income, percent of 

borrowers that are classifed as subprime, and poverty rate. 

34 
Figure 4: Average SBA Guarantee by Economic Variables 

Employing county-level data, these fgures display the average percent guaranteed on SBA loans by the 

various local economic measures. The measures used are the per capita income, unemployment rate, 

bank concentration (scaled HHI), credit score, household income, percent of borrowers that are 

classifed as subprime, and poverty rate. 

35 
Figure 5: Percent of SBA Express Loans by Loan Amount 

This table displays the percent of total loans by loan amount of SBA Express loans for online banks and 

for non-online bank lenders. We mark $150,000 on the x-axis as the guarantee percent threshold for 

Standard/Small loan to visualize any bunching around the threshold. 

36 
Figure 6: Percent of PLP Loans by Loan Amount 

This table displays the percent of total loans by loan amount of Standard/Small loans made with PLP 

status for online banks and for non-online bank lenders. We mark $150,000 on the x-axis as the 

guarantee percent threshold for Standard/Small loan to visualize any bunching around the threshold. 

37 
Figure 7: Estimated IRR and IRR Simulations 

This fgure shows in the top panel the histograms of the estimated lenders’ Internal Rate of Return 

(IRR) based on the loan cash fows. The second panel shows the lenders’ simulated IRR assuming a 

reduction of loan guarantees for online lenders from 75% or more to 50% (scenario 1). The third panel 

shows the lenders’ simulated IRR assuming a reduction of loan guarantees for online lenders from 85% 

or more to 50% (scenario 2). 

38 
Table I: Summary Statistics 

This table reports summary statistics of the main analysis variables. Our sample is from 2010-

2019. The columns report the mean, standard deviation, 25th percentile, median, 75th percentile, and number of observations, respectively. Loan Variables consist of SBA 7(a) loan observations, excluding loans with a cancelled status and those missing primary variables. Bank 

Variables are reported at the year level for each lender. County Variables are constructed at 

the county-year level. Zip Code Variables are constructed at the zip-code-year level. MinorityMatched Loan Variables are reported at the loan level for each of the matched loan observations. 

For variable descriptions, see Appendix Table A.1. 

Mean Std Dev 25th Pctile Median 75th Pctile # Obs. 

Loan Variables 

Online Lender .0547 .23 0 0 0 459,722 

Loan Amount ($ thousands) 377 671 40 125 360 459,722 

Initial Interest Rate (%) 6.54 1.5 5.5 6 7.4 459,722 

SBA Guarantee (%) 64.9 15 50 75 75 459,722 

Charge Off .0531 .22 0 0 0 459,722 

Charge Off Amount ($) 6,745 63,045 0 0 0 459,722 

Charge Off to Loan (%) 3.92 17 0 0 0 459,722 

Securitized .28 .45 0 0 1 367,438 

SBA Loss to Loan (%) 2.41 11 0 0 0 459,722 

SBA Proft to Loan (%) -.00131 .12 .0022 .019 .039 459,722 

Lender IRR (%) 4.78 4.9 5 5 6 459,693 

Revolver Status .311 .46 0 0 1 459,722 

Term in Months 122 79 84 84 120 459,722 

Business is Corporation .875 .33 1 1 1 459,722 

Business is Individual .107 .31 0 0 0 459,722 

Business is Partnership .0187 .14 0 0 0 459,722 

Business Age 2.12 1.4 .5 2.5 2.5 61,074 

Branches in Zip Code 6.64 5.2 3 6 10 401,156 

Distance to SBA Lender .564 4 0 0 0 406,428 

Total Fees (%) 2.47 2.3 .52 1.9 3.9 459,722 

Guarantee Fee (%) 1.14 1.1 0 1 2.3 459,722 

Ongoing Fee (%) 1.29 1.5 .24 .88 1.7 459,722 

Prepayment Fee (%) .0355 .3 0 0 0 459,722 

39 
Table I: Summary Statistics - Continued 

Mean Std Dev 25th Pctile Median 75th Pctile # Obs. 

Lender Variables 

Log Assets 13.1 1.5 12 13 13.8 13,677 

Deposits to Assets .833 .057 .8 .84 0.88 13,677 

Return on Assets .94 .72 .62 .95 1.30 13,677 

Equity to Assets .105 .023 .089 .1 0.12 13,677 

Balance Sheet Asset HHI 1,326 502 992 1,186 1500.6 13,677 

Balance Sheet Liabilities HHI 6,137 1,105 5,373 6,108 6876.1 13,677 

Income HHI 3,051 1,139 2,241 2,849 3581.7 13,677 

Loans HHI (CR) 2,056 780 1,545 1,841 2293.0 13,677 

Loans HHI (SDI) 2,838 1,008 2,136 2,610 3226.9 13,677 

Loan Categories (SDI) 10.9 2 10 11 12 13,677 

County Variables 

County Per Cap Income ($) 40,757 11,975 33,566 38,527 45059 21,190 

County Unemployment (%) 6.29 2.7 4.2 5.7 7.90 21,190 

County Scaled HHI .174 .16 .068 .13 0.23 21,190 

County Per Cap Establishments .0231 .0082 .018 .022 0.026 21,190 

Zip Code Variables 

Zip Code Credit Score 681 41 654 685 710.8 114,737 

Zip Code Income 84.9 26 67 80 98.0 114,737 

Zip Code Subprime Rate 39.8 17 28 38 50.5 114,737 

Zip Code Poverty Rate 12.6 7.8 6.7 11 16.7 114,737 

Minority-Matched Loan Variables 

Online Lender .0588 .24 0 0 0 274,463 

Woman Owned .00829 .091 0 0 0 274,463 

Minority Owned .00674 .082 0 0 0 274,463 

Black Owned .0018 .042 0 0 0 274,463 

Hispanic Owned .00222 .047 0 0 0 274,463 

40 
Table II: Matched Versus Total Sample 

This table reports univariate differences between the matched and total sample. The columns 

report the mean of matched loan observations, mean of total sample loan observations, difference between the means, and t-statistic, respectively. 

Matched Total Matched - Total 

(1) (2) (1-2) 

Mean Mean Diff. t-stat 

Initial Interest Rate (%) 6.67 6.53 .144∗∗∗ 35.51 

Term in Months 128 122 6.11∗∗∗ 29.26 

Loan Amount ($ thousands) 375 371 3.98∗ 2.30 

SBA Guarantee (%) 64.3 64.1 .201∗∗∗ 5.21 

Business is Corporation .859 .876 -.0164∗∗∗ -18.83 

Business is Individual .121 .106 .0151∗∗∗ 18.60 

Business is Partnership .02 .0187 .00127∗∗∗ 3.58 

Branches in Zip Code 6.68 6.64 .0372∗∗ 2.76 

Distance to SBA Lender .408 .414 -.00609 -1.39 

Preferred Lender Program (%) .317 .31 .00706∗∗∗ 5.90 

SBA Express Program (%) .501 .507 -.00646∗∗∗ -5.00 

Paid in Full .609 .621 -.0124∗∗∗ -9.90 

Charge Off .0521 .0516 .00051 0.89 

Charge Off to Loan (%) 3.91 3.83 .0756 1.68 

SBA Loss to Loan (%) 2.38 2.31 .0656∗ 2.38 

SBA Proft to Loan (%) -.001 -.001 .0000 0.13 

Lender IRR (%) 4.88 4.74 .136∗∗∗ 10.46 

Total Fees (%) 2.48 2.41 .0703∗∗∗ 11.60 

Guarantee Fee (%) 1.11 1.12 -.00731∗∗ -2.65 

Ongoing Fee (%) 1.33 1.25 .0733∗∗∗ 18.20 

Prepayment Fee (%) .0401 .0359 .00426∗∗∗ 5.32 

* p<.10, ** p<.05, *** p<.01 

41 
42 

Table III: Online Banks and Specialization 

The table uses data at the bank-year level from 2010-2019. The dependent variables are all measures of specializsheet assets HHI, balance sheet liabilities HHI, Income HHI, Loans HHI (CR), Loans HHI (SDI), and Loan Cprimary independent variable of interest is online bank, an indicator equal to 1 if a lender is classifed as aotherwise. We control for bank characteristics: log assets, deposits to assets, ROA, and equity to assets. We incluStandard errors are clustered at the bank level. 

Assets HHI Liabilities HHI Income HHI Loans HHI (CR) Loans HHI (SDI) 

(1) (2) (3) (4) (5) 

Online Bank 343.524∗∗∗ 503.222∗ 450.095∗ 583.812∗∗∗ 488.611∗ 

Log Assets Deposits to Assets Return on Assets 

(122.319) 

-70.510∗∗∗

(7.463) 

-1815.484∗∗∗

(270.119) 

-15.657 

(283.187) 

-192.834∗∗∗

(15.666) 

7268.164∗∗∗

(361.434) 

-10.558 

(257.324) 

-139.808∗∗∗

(16.676) 

-676.193 

(484.508) 

-122.044∗∗∗

(198.311) 

-94.260∗∗∗

(12.522) 

-2585.009∗∗∗

(410.772) 

-67.654∗∗∗

(251.340) 

-98.183∗∗∗

(16.055) 

-2370.789∗∗∗

(494.089) 

-178.313∗∗∗

Equity to Assets 

(14.749) 

1462.255∗∗

(591.201) 

(26.503) 

6954.781∗∗∗

(1026.283) 

(34.848) 

8019.706∗∗∗

(1225.105) 

(22.585) 

2854.265∗∗∗

(948.226) 

(29.508) 

5048.411∗∗∗

(1188.436) 

Year FE Yes Yes Yes Yes Yes Observations Adjusted R2 

9,953 0.093 

9,953 0.299 

9,953 

0.079 

9,953 0.083 

9,953 

0.070 

Standard errors in parentheses. * p<.10, ** p<.05, *** p<.01 
Table IV: Online Versus Other Lenders 

This table reports univariate differences between online banks and all other lender types at the 

loan level from 2010 to 2019. The columns report the mean of online bank lenders, mean of all 

other lenders, difference between the means, and number of observations, respectively. Panel A 

features the various loan programs and Panel B provides loan characteristics and outcomes. 

Panel A: Loan Programs 

Online Banks Other Lenders Online Banks - Other Lenders 

(1) (2) (1-2) 

Mean Mean Diff. Obs. 

Community Advantage 0.00 0.01 -0.01∗∗∗ 459,722 

Community Express 0.00 0.01 -0.01∗∗∗ 459,722 

Certifed Lender 0.00 0.01 -0.00∗∗∗ 459,722 

Other 7(a) Loan 0.06 0.09 -0.03∗∗∗ 459,722 

Patriot Express 0.00 0.01 -0.01∗∗∗ 459,722 

Preferred Lender 0.76 0.30 0.46∗∗∗ 459,722 

SBA Express 0.03 0.51 -0.48∗∗∗ 459,722 

Small Loan Advantage 0.14 0.05 0.09∗∗∗ 459,722 

All Other Programs 0.01 0.02 -0.01∗∗∗ 459,722 

Panel B: Loan Characteristics and Outcomes 

Online Banks 

(1) 

Mean 

Other Lenders O

(2) 

Mean 

nline Banks - Other Len

(1-2) 

Diff. 

ders 

Obs. 

Initial Interest Rate (%) 6.49 6.53 -.0422∗∗∗ 357,018 

Term in Months 159 125 34.5∗∗∗ 357,018 

Loan Amount ($ thousands) 661 377 284∗∗∗ 357,018 

SBA Guarantee (%) 78.9 63.8 15.2∗∗∗ 357,018 

Business is Corporation .899 .877 .0215∗∗∗ 357,018 

Business is Individual .0878 .104 -.0161∗∗∗ 357,018 

Business is Partnership .0136 .019 -.00543∗∗∗ 357,018 

Branches in Zip Code 6.85 6.61 .237∗∗∗ 357,018 

Distance to SBA Lender .446 .413 .0328∗∗∗ 357,018 

Paid in Full .416 .593 -.176∗∗∗ 357,018 

Charge Off .0926 .0478 .0448∗∗∗ 357,018 

Charge Off to Loan (%) 7.31 3.48 3.83∗∗∗ 357,018 

Securitized .916 .229 .687∗∗∗ 357,018 

SBA Loss to Loan (%) 5.47 2.05 3.42∗∗∗ 357,018 

SBA Proft to Loan (%) -.028 .00294 -.0309∗∗∗ 357,018 

Lender IRR (%) 4.76 4.89 -.127∗∗∗ 357,018 

Total Fees (%) 3.33 2.48 .843∗∗∗ 357,018 

Guarantee Fee (%) 1.5 1.13 .367∗∗∗ 357,018 

Ongoing Fee (%) 1.77 1.32 .455∗∗∗ 357,018 

Prepayment Fee (%) .0552 .0341 .0211∗∗∗ 357,018 

* p<.10, ** p<.05, *** p<.01 

43 
44 

Table V: Online Bank Lending Behavior and SBA Losses 

The table uses data at the loan level from 2010-2019. The dependent variables are log loan amount, interest percent, charge-off status, charge-off to loan, and SBA loss to loan, and SBA proft to loan. Charge-off status is is deemed uncollectable and 0 otherwise, charge-off to loan is the charge-off amount divided by the total loan, SBguaranteed portion of the charge-off amount minus fees paid to the SBA divided by the total loan if the loan defaand SBA proft to loan is the total fees paid to the SBA net of any SBA charge-off payments. The independent vaan indicator equal to 1 if a lender is classifed as an online bank and 0 otherwise. We include loan controls: loan initial interest rate, and log term in months. We include business type (corporate or individual excluding partnerof SBA lender branches in the borrower’s zip code as borrower controls. We control for bank characteristics: loassets, ROA, and equity to assets. We include county-year and industry-year fxed effects. Standard errors are lender and borrower level. 

Log Loan Amount Interest Rate Guarantee Percent Charged Off Status Charge Off to Loan SBA Loss to Loan 

(1) (2) (3) (4) (5) (6) 

Online Bank 0.171 0.495∗∗∗ 4.629∗∗ 0.061∗∗∗ 4.814∗∗∗ 3.495∗∗∗ 

(0.204) (0.084) (1.915) (0.018) (1.581) (1.223) 

Revolver Status -0.714∗∗∗ 0.441∗∗∗ -10.156∗∗∗ -0.033∗∗∗ -1.491∗∗ -1.206∗∗∗ 

(0.116) (0.162) (1.204) (0.007) (0.593) (0.400) 

Initial Interest Rate -0.389∗∗∗ -0.260 0.016∗∗∗ 1.259∗∗∗ 0.676∗∗∗ 

(0.045) (0.177) (0.001) (0.121) (0.067) 

Log Term in Months 0.702∗∗∗ 0.102∗∗∗ 2.697∗∗∗ -0.127∗∗∗ -8.492∗∗∗ -4.830∗∗∗ 

(0.049) (0.030) (0.367) (0.011) (0.794) (0.378) 

Business is Corporation 0.008 0.050 -0.182 0.007 0.544 0.387∗∗ 

(0.016) (0.032) (0.198) (0.005) (0.346) (0.194) 

Business is Individual -0.492∗∗∗ 0.213∗∗∗ -0.577∗∗ 0.019∗∗∗ 1.377∗∗∗ 0.749∗∗∗ 

(0.029) (0.058) (0.236) (0.004) (0.297) (0.168) 

Branches in Zip Code 0.005∗∗∗ -0.001∗∗ 0.019∗∗∗ -0.001∗∗∗ -0.041∗∗∗ -0.022∗∗∗ 

(0.001) (0.001) (0.005) (0.000) (0.007) (0.004) 

Bank Log Assets -0.050∗∗∗ 0.101∗∗∗ -0.681∗∗ 0.001 0.085 -0.000 

(0.015) (0.009) (0.268) (0.002) (0.147) (0.103) 

Bank Deposits to Assets -0.267 0.813 12.448 0.053 3.516 1.824 

(0.373) (0.510) (7.921) (0.054) (4.618) (3.124) 

Bank Return on Equity -0.008∗∗ -0.002 0.125∗∗ 0.001∗∗ 0.088∗∗ 0.066∗∗ 

(0.004) (0.004) (0.051) (0.000) (0.042) (0.031) 

Bank Equity to Assets -0.234 2.684∗∗ 31.382∗∗ 0.169 16.036 14.046 

(1.164) (1.175) (14.986) (0.139) (11.932) (8.786) 

Loan Size Group -0.810∗∗∗ 4.935∗∗∗ 0.032∗∗∗ 1.842∗∗∗ 1.030∗∗∗ 

(0.130) (0.425) (0.005) (0.392) (0.235) 

County × Year FE Yes Yes Yes Yes Yes Yes Industry × Year FE Yes Yes Yes Yes Yes Yes Observations 371,695 371,695 371,695 371,695 371,695 371,695 

Adjusted R2 0.597 0.492 0.579 0.126 0.097 0.094 

Standard errors in parentheses. * p<.10, ** p<.05, *** p<.01 
Table VI: Credit Access Using Loan Amount and County Market Groups 

The table uses data at the bank-county-year level from 2010 to 2019. The dependent variable, 

log loan amount, measures the log of the total sum of loan amounts originated by a lender in a 

county-year. The independent variables of interest include online, an indicator equal to 1 if a 

lender is classifed as an online bank and 0 otherwise, and county economic measures broken 

into terciles interacted with online. We leave out the middle tercile of each county market 

variable. These market measures are bottom tercile per capita income, top tercile per capita 

income, bottom tercile unemployment, top tercile unemployment, bottom tercile HHI scaled 

by 10,000, and top tercile HHI scaled by 10,000. We include the following bank controls; log 

assets, deposits to assets, return on assets, and equity to assets. We include county-year fxed 

effects. Standard errors are clustered at the lender level. 

Log Total Loan Amount 

(1) (2) (3) 

Online Bank -0.149 -0.170 -0.057 

Online Bank × Bottom Tercile Per Cap Income 

Online Bank × Top Tercile Per Cap Income 

Online Bank × Bottom Tercile Unemployment 

Online Bank × Top Tercile Unemployment 

Online Bank × Bottom Tercile HHI 

(0.238) 

0.289∗∗∗

(0.078) 

-0.064 

(0.070) 

(0.241) 

-0.023 

(0.113) 

0.336∗∗∗

(0.106) 

(0.250) 

-0.213∗∗

Online Bank × Top Tercile HHI 

Log Assets 

Deposits to Assets 

Return on Assets 

-0.013 

(0.016) 

1.359∗∗

(0.564) 

0.025 

-0.013 

(0.017) 

1.364∗∗

(0.568) 

0.026 

(0.090) 

0.126∗∗∗

(0.044) 

-0.014 

(0.016) 

1.350∗∗

(0.561) 

0.027 

Equity to Assets 

(0.034) 

-3.324∗

(1.708) 

(0.034) 

-3.196∗

(1.714) 

(0.036) 

-3.459∗∗

(1.727) 

County × Year FE 

Observations 

Adjusted R2 

Yes 

111,815 

0.174 

Yes 

111,815 

0.174 

Yes 

111,815 

0.174 

Standard errors in parentheses. * p<.10, ** p<.05, *** p<.01 

45 
Table VII: Credit Access to Minorities 

The table uses matched data at the loan level from 2010-2019. The dependent variable is Log 

Loan Amount. The independent variables of interest include Online Bank, an indicator equal 

to 1 if a lender is classifed as an online bank and 0 otherwise, borrower ownership indicators 

including Woman Owned, Minority Owned, Black Owned, and Hispanic Owned, and the ownership indicators interacted with Online Bank. Additional Controls include the loan controls, 

borrower controls and bank controls. Standard errors are clustered at both the lender and borrower level. 

Log Loan Amount 

(1) (2) (3) (4) (5) 

Online Bank 0.146 0.148 0.148 0.147 0.147 

(0.186) (0.186) (0.186) (0.186) (0.186) 

Online Bank × Woman Owned -0.512∗∗∗

(0.063) 

Online Bank × Minority Owned -0.616∗∗∗

(0.135) 

Online Bank × Black Owned -0.426∗∗∗

(0.106) 

Online Bank × Hispanic Owned -1.072∗∗∗

(0.254) 

Woman Owned 0.178∗∗∗

(0.029) 

Minority Owned 0.200∗∗∗

(0.036) 

Black Owned 0.029 

(0.064) 

Hispanic Owned 0.244∗∗∗

(0.047) 

Additional Controls Yes Yes Yes Yes Yes 

County × Year FE Yes Yes Yes Yes Yes 

Industry × Year FE Yes Yes Yes Yes Yes 

Observations 219,360 219,360 219,360 219,360 219,360 

Adjusted R2 0.663 0.663 0.663 0.663 0.663 

Standard errors in parentheses. * p<.10, ** p<.05, *** p<.01 

46 
Table VIII: Loan Performance and Zip Code Market Measures 

The table uses data at the loan level from 2010-2019. For Panel A, the dependent variable is 

the charge-off status which is an indicator equal to 1 if the loan is deemed uncollectable by the 

lender and 0 otherwise. For Panel B, the dependent variable is the charge-off to loan which is 

the charge-off amount divided by the total loan amount. For Panel C, the dependent variable, 

SBA loss to loan, is the charge-off amount times the SBA guarantee percent divided by the 

loan amount. The independent variables of interest include online, an indicator equal to 1 if 

a lender is classifed as an online bank and 0 otherwise, zip code economic variables, and zip 

code economic variables interacted with online. The zip code economic variables are log credit 

score, log household income, percent subprime, and poverty rate. Additional Controls include 

the loan controls, borrower controls and bank controls. Standard errors are clustered at both the 

lender and borrower level. 

Panel A: Charge-Off Status 

Charge-Off Status 

(1) (2) (3) (4) (5) 

Online Bank 0.060∗∗∗ 0.061∗∗∗ 0.061∗∗∗ 0.061∗∗∗ 0.060∗∗∗

(0.017) (0.017) (0.017) (0.017) (0.017) 

Online Bank × Log Credit Score 0.000 

(0.038) 

Online Bank × Log Income -0.002 

(0.009) 

Online Bank × Percent Subprime -0.000 

(0.000) 

Online Bank × Poverty Rate -0.001 

(0.001) 

Log Credit Score -0.043∗∗∗

(0.011) 

Log Income -0.010∗∗∗

(0.002) 

Subprime 0.000∗∗∗

(0.000) 

Poverty Rate 0.000 

(0.000) 

Additional Controls Yes Yes Yes Yes Yes 

County × Year FE Yes Yes Yes Yes Yes 

Industry × Year FE Yes Yes Yes Yes Yes 

Observations 369,049 369,049 369,049 369,049 369,049 

Adjusted R2 0.119 0.119 0.119 0.119 0.119 

Standard errors in parentheses. * p<.10, ** p<.05, *** p<.01 

47 
Table VIII: Loan Performance and Zip Code Market Measures—Continued 

Panel B: Charge-Off to Loan 

Charge-Off to Loan 

(1) (2) (3) (4) (5) 

Online Bank 4.649∗∗∗ 4.669∗∗∗ 4.677∗∗∗ 4.668∗∗∗ 4.609∗∗∗

(1.478) (1.471) (1.455) (1.472) (1.451) 

Online Bank × Log Credit Score -0.282 

(2.864) 

Online Bank × Log Income -0.090 

(0.651) 

Online Bank × Percent Subprime -0.001 

(0.010) 

Online Bank × Poverty Rate -0.048 

(0.044) 

Log Credit Score -2.989∗∗∗

(0.881) 

Log Income -0.679∗∗∗

(0.196) 

Subprime 0.012∗∗∗

(0.003) 

Poverty Rate 0.003 

(0.005) 

Additional Controls Yes Yes Yes Yes Yes 

County × Year FE Yes Yes Yes Yes Yes 

Industry × Year FE Yes Yes Yes Yes Yes 

Observations 369,049 369,049 369,049 369,049 369,049 

Adjusted R2 0.091 0.091 0.091 0.091 0.091 

Standard errors in parentheses. * p<.10, ** p<.05, *** p<.01 

48 
Table VIII: Loan Performance and Zip Code Market Measures—Continued 

Panel C: SBA Loss to Loan (Net Fees) 

SBA Loss to Loan (Net Fees) 

(1) (2) (3) (4) (5) 

Online Bank 2.882∗∗∗ 2.901∗∗∗ 2.902∗∗∗ 2.900∗∗∗ 2.853∗∗∗

(1.083) (1.074) (1.065) (1.075) (1.063) 

Online Bank × Log Credit Score -2.211 

(2.194) 

Online Bank × Log Income -0.203 

(0.506) 

Online Bank × Percent Subprime 0.006 

(0.008) 

Online Bank × Poverty Rate -0.031 

(0.032) 

Log Credit Score -1.125∗∗

(0.493) 

Log Income -0.272∗∗∗

(0.105) 

Subprime 0.005∗∗

(0.002) 

Poverty Rate -0.001 

(0.003) 

Additional Controls Yes Yes Yes Yes Yes 

County × Year FE Yes Yes Yes Yes Yes 

Industry × Year FE Yes Yes Yes Yes Yes 

Observations 369,049 369,049 369,049 369,049 369,049 

Adjusted R2 0.094 0.094 0.094 0.094 0.094 

Standard errors in parentheses. * p<.10, ** p<.05, *** p<.01 

49 
A Appendix 

Table A.1: Variable Defnitions 

Variable Description 

Online Bank Indicator variable that equals 1 when a lender is classifed as 

an online bank, meaning it has less than 10 physical locations 

and less than 10% of its loans made in the state in which it is 

located, and 0 otherwise 

Initial Interest Rate The interest rate on the loan at the time of approval. SBA 7(a) 

loans may come with fxed or variable rates 

Loan Amount (Thousands) The dollar loan amount as listed by the SBA measured in thousands 

SBA Guarantee Percent The amount of the loan guaranteed by the SBA divided by the 

total loan amount 

Charge-Off Loan Status Indicator variable that equals 1 if the loan has been deemed 

uncollectable by the lender and 0 otherwise 

Charge-Off to Loan The gross charge-off amount divided by the total loan amount 

SBA Loss to Loan The gross charge-off amount, net of fees, multiplied by the 

SBA guarantee percent, divided by the total loan amount 

SBA Proft to Loan The sum of fees paid to the SBA minus the guaranteed portion 

of the default amount in the case of a charge-off, divided by 

the total loan amount 

Lender IRR The internal rate of return using the initial loan amount and 

cash fows calculated using initial interest rate, term in months, 

and payment and default information 

Revolver Status Indicator variable that equals 1 if the loan is a revolving line of 

credit and 0 if a term loan 

Term in Months The length of loan term as measured in months 

Business is Corporation Indicator variable that equals 1 if the borrower business type is 

corporation and 0 otherwise 

50 
Table A.1: Variable Defnitions—Continued 

Variable Description 

Business is Individual Indicator variable that equals 1 if the borrower business type is 

individual and 0 otherwise 

Business is Partnership Indicator variable that equals 1 if the borrower business type is 

partnership and 0 otherwise 

Branches in Zip Code The number of branches of SBA lenders within the borrower’s 

zip code. 

Distance to SBA Lender The distance in miles between the borrower’s zip code to the 

zip code of the nearest SBA lender branch. 

Prepayment Fee The amount that SBA borrowers are required to pay on loans 

with a maturity of above 15 years that are prepaid within the 

frst 3 years 

Guarantee Fee The upfront fee amount required by the SBA that range from 

0-3.75% of the guaranteed portion of the initial loan amount 

Ongoing Fee The total amount that SBA borrowers pay toward the annual 

servicing fee over the life of the loan which ranges from 0-

0.55% of the guaranteed outstanding portion of the loan each 

year 

County Per Cap Income Total personal income, as defned by the Bureau of Economic 

Analysis (BEA), divided by the total population 

County Unemployment Rate The number of individuals in the labor force that are unemployed divided by the total labor force 

County Scaled Bank HHI The sum of squared share of local bank branch deposits of the 

banks in the county scaled by 10,000 

Zip Code Credit Score The average credit score of borrowers in a given ZIP Code 

Zip Code Income The average estimated household income in a given ZIP code 

Zip Code Subprime Rate The average ratio of borrowers with a subprime credit score 

(below 640) 

Zip Code Poverty Rate Average poverty rate based on U.S Census in a given ZIP code 

Bank Log Assets The log total assets of the fnancial institution as reported by 

the FDIC 

Bank Deposits to Assets The total deposits of the FDIC reporting institution divided by 

its total assets 

Bank Returns on Assets The return on assets as reported by the FDIC 

Bank Equity to Assets The fnancial institution’s reported equity divided by its assets 

Log Per Cap Establishments The log of number of business establishments in a county divided by the total county population. 

51 
Table A.1: Variable Defnitions—Continued 

Variable Description 

Balance Sheet Asset HHI The sum of a lender’s annual squared share of balance sheet 

asset categories and sub-categories using bank call reports 

Balance Sheet Liabilities HHI The sum of a lender’s annual squared share of balance sheet 

liability categories and sub-categories using bank call reports 

Income HHI The sum of a lender’s squared annual share of income statement categories using bank call reports 

Loans HHI (CR) The sum of a lender’s annual squared share of loans and leases 

categories using bank call reports 

Loans HHI (SDI) The sum of a lender’s squared share of loans and leases categories using Summary of Deposit data from the Statistics on 

Depository Institutions 

Loan Categories (SDI) The number of loan and lease categories a lender utilized each 

year using Summary of Deposit data from the Statistics on Depository Institutions 

Woman Owned Indicator variable that equals 1 if the borrower self-identifes 

her business as Woman Owned Business in the System for 

Award Management (SAM) Entity Registration Records, and 

0 otherwise 

Minority Owned Indicator variable that equals 1 if the borrower self-identifes 

under business code 23: Minority Owned Business in the 

System for Award Management (SAM) Entity Registration 

Records, and 0 otherwise 

Black Owned Indicator variable that equals 1 if the borrower self-identifes 

under business code OY: Black American Owned Business in 

the System for Award Management (SAM) Entity Registration 

Records, and 0 otherwise 

Hispanic Owned Indicator variable that equals 1 if the borrower self-identifes 

under business code PI: Hispanic American Owned Business 

in the SAM Entity Registration Records and 0 otherwise 

52 
Table A.2: SBA 7(a) Loan Delivery Methods 

Using information from the SBA, this table provides a brief description of the primary 7(a) programs with their rates and year active in the sample. 

Program Description Guarantee Community Advantage Community-based focus on local small businesses in 

underserved markets. Max loan amount of $350,000. 

Up to 85% for loans up to $150,000 a75% for loans greater than $150,000 

Community Express Focus on underserved communities that qualify for 

CRA. Max loan amount of $250,000. 

Up to 85% for loans up to $150,000 a75% for loans greater than $150,000 

Certifed Lender Made by SBA certifed lenders who receive expedited 

processing of loan applications. 

More fexible with a maximum of 90% 

Other 7(a) Loan The SBA’s category for all other loan delivery methods. Variable guarantees Patriot Express Focuses on veteran-owned small businesses. Max 

loan amount of $500,000. 

Up to 85% for loans up to $150,000 a75% for loans greater than $150,000 

Preferred Lender Standard/Small 7(a) loans made by SBA preferred 

lenders who have more authority to process, close, service, and liquidate SBA-guaranteed loans. 

Up to 85% for loans up to $150,000 a75% for loans greater than $150,000 

SBA Express Accelerated turnaround time for SBA review (within 

36 hours). Maximum loan amount of $500,000. 

50% 

Small Loan Advantage Focuses on small loans with a max loan amount of 

$350,000. 

Up to 85% for loans up to $150,000 a75% for loans greater than $150,000 

All Other Programs Consists of all loan programs where the sample is below 1% for both online and other lenders. This in- 75% to 90% 

cludes dealer foor plan, export working capital, export express, gulf opportunity, international trade, and rural lender advantage. 

53 
54 

Table A.3: SBA 7(a) Fees 

This table lists the various fees associated with SBA 7(a) loans and their percentages for each fscal year du

FY 2010 FY 2011 FY 2012 FY 2013 FY 2014 FY 2015 FY 2016 FY 2017 FY 

Guarantee fee - upfront fee on the guaranteed portion of loan Short term : $125,000 or less N.A.∗ 0.25% 0.25% 0.25% 0.00% 0.00% 0.00% 0.00% 0.2

Short term : $125,001 to $150,000 N.A.∗ 0.25% 0.25% 0.25% 0.00% 0.00% 0.00% 0.00% 0.2Short term : $150,001 to $700,000 N.A.∗ 0.25% 0.25% 0.25% 0.25% 0.25% 0.25% 0.25% 0.2Short term : $700,001 to $5,000,000 N.A.∗ 0.25% 0.25% 0.25% 0.25% 0.25% 0.25% 0.25% 0.2Long term : $125,000 or less N.A.∗ 2.00% 2.00% 2.00% 0.00% 0.00% 0.00% 0.00% 0.0Long term : $125,001 to $150,000 N.A.∗ 2.00% 2.00% 2.00% 0.00% 0.00% 0.00% 0.00% 2.0Long term : $150,001 to $700,000 N.A.∗ 3.00% 3.00% 3.00% 3.00% 3.00% 3.00% 3.00% 3.0

3.50% on 3.50% on 3.50% on 3.50% on 3.50% on 3.50% on 3.5

up to $1 up to $1 up to $1 up to $1 up to $1 up to $1 up 

Long term : $700,001 to $5,000,000 N.A.∗ 3.50% 

million plus 3.75% on amount 

million plus 3.75% on amount 

million plus 3.75% on amount 

million plus 3.75% on amount 

million plus 3.75% on amount 

million plus 3.75% on amount 

mipluon 

over $1 over $1 over $1 over $1 over $1 over $1 ove

million million million million million million mi

Ongoing servicing fee - yearly fee on the guaranteed portion of the outstanding loan balance 

Short term : $150,000 or less 0.55% 0.55% 0.55% 0.55% 0.00% 0.00% 0.00% 0.546% 0.5

Short term : more than $150,000 0.55% 0.55% 0.55% 0.55% 0.52% 0.519% 0.473% 0.546% 0.5

Long term : $150,000 or less 0.55% 0.55% 0.55% 0.55% 0.00% 0.00% 0.00% 0.546% 0.5

Long term : $150,001 to $700,000 0.55% 0.55% 0.55% 0.55% 0.52% 0.519% 0.473% 0.546% 0.5

Long term : $700,001 to $5,000,000 0.55% 0.55% 0.55% 0.55% 0.52% 0.519% 0.473% 0.546% 0.5

Prepayment fee - only applicable to loans with maturity of 15 years or more that prepay within the frst 3 years 

5% if prepaid in the frst year, 3% if prepaid in the second year, and 1% if prepaid in the third year 

*The Small Business Job Creation and Access to Capital Act of 2009 waived all SBA guarantee fees to encourage fnancial recovery. 
Table A.4: Largest Online Banks by Lending Volume 

This table lists the identities, loan amounts, and locations of the ten largest online banks in our 

sample period of 2010-2019. 

Bank Name No. of Loans Overall Loan Amount State Located 

Celtic Bank Corporation 6,593 $2,469,248,200 UT 

Live Oak Banking Company 6,367 $7,811,133,000 NC 

Stearns Bank National Asso- 3,853 $1,435,446,600 MN 

ciation 

Independence Bank 3,257 $456,019,200 RI 

United Midwest Savings 1,660 $676,273,300 OH 

Bank, National Association 

The Bancorp Bank 750 $559,078,600 DE 

LendingClub Bank, National 501 $390,020,400 UT 

Association∗

Coastal States Bank 362 $328,938,400 SC 

FinWise Bank 300 $224,446,100 UT 

West Town Bank And Trust 286 $360,663,700 IL 

*Previously known as Radius Bank until 2021 when it was acquired by LendingClub. 

55 
Table A.5: Credit Access Using Loan Amount and County Market Measures 

The table uses data at the bank-county-year level from 2010 to 2019. The dependent variable, 

log loan amount, measures the log of the total sum of loan amounts originated by a lender in a 

county-year. The independent variables of interest include online, an indicator equal to 1 if a 

lender is classifed as an online bank and 0 otherwise, and county economic variables interacted 

with online. These economic variables are log per capita income, unemployment rate, and 

HHI scaled by 10,000. We include the following bank controls; log assets, deposits to assets, 

return on assets, and equity to assets. We include county-year fxed effects. Standard errors are 

clustered at the bank level. 

Log Total Loan Amount 

(1) (2) (3) (4) 

Online Bank -0.110 -0.082 -0.060 -0.099 

Online Bank × Log Per Cap Income 

Online Bank × Unemployment Rate 

Online Bank × Scaled HHI 

(0.247) (0.244) 

-0.427∗∗

(0.191) 

(0.233) 

0.069∗

(0.036) 

(0.246) 

0.740∗∗∗

Log Assets 

Deposits to Assets 

Return on Assets 

-0.014 

(0.016) 

1.347∗∗

(0.564) 

0.028 

-0.013 

(0.016) 

1.358∗∗

(0.564) 

0.026 

-0.013 

(0.017) 

1.366∗∗

(0.567) 

0.025 

(0.182) 

-0.014 

(0.016) 

1.348∗∗

(0.563) 

0.028 

Equity to Assets 

(0.036) 

-3.435∗∗

(1.727) 

(0.035) 

-3.348∗

(1.714) 

(0.034) 

-3.164∗

(1.713) 

(0.036) 

-3.442∗∗

(1.728) 

County × Year FE 

Observations 

Adjusted R2 

Yes 

111,815 

0.173 

Yes 

111,815 

0.174 

Yes 

111,815 

0.174 

Yes 

111,815 

0.174 

Standard errors in parentheses. * p<.10, ** p<.05, *** p<.01 

56 
57 

Table A.6: Online Bank and Minority Borrowers 

The table uses data at the loan level from 2010-2019. The dependent variables are interest rate, SBA guarantestatus, charge-off to loan, and SBA loss to loan. The independent variables of interest include Online Bank, an if a lender is classifed as an online bank and 0 otherwise, and Minority Owned, a borrower ownership indicatorclustered at both the lender and borrower level. 

Guarantee Percent Interest Rate Charge-Off Status Charge-Off to Loan SBA Loss to Lo

(1) (2) (3) (4) (5) 

Online Bank 4.194∗∗ 0.526∗∗∗ 0.051∗∗∗ 3.896∗∗∗ 2.939

(1.661) (0.085) (0.016) (1.471) (1.14

Minority Owned 0.425 -0.136∗∗∗ -0.006 -0.325 -0.13

(0.498) (0.040) (0.007) (0.551) (0.35

Online Bank × Minority Owned 3.075∗∗ 0.069 0.096∗∗∗ 7.761∗∗∗ 6.437

(1.363) (0.131) (0.018) (1.394) (1.05

Loan Size Group 4.037∗∗∗ -0.922∗∗∗ 0.038∗∗∗ 2.301∗∗∗ 1.266

(0.438) (0.122) (0.005) (0.420) (0.24

Revolver Status -10.386∗∗∗ 0.542∗∗∗ -0.039∗∗∗ -2.075∗∗∗ -1.568

(1.494) (0.204) (0.009) (0.691) (0.46

Initial Interest Rate -0.355∗∗ 0.015∗∗∗ 1.177∗∗∗ 0.630

(0.170) (0.002) (0.134) (0.07

Log Term in Months 3.266∗∗∗ 0.089∗∗ -0.141∗∗∗ -9.571∗∗∗ -5.504

(0.377) (0.036) (0.012) (0.858) (0.40

Business is Corporation -0.109 0.052 0.006 0.387 0.29

(0.228) (0.042) (0.007) (0.504) (0.29

Business is Individual -0.778∗∗∗ 0.166∗∗∗ 0.008 0.434 0.20

(0.276) (0.057) (0.008) (0.583) (0.33

Branches in Zip Code 0.013∗∗∗ -0.001∗∗ -0.000∗∗∗ -0.036∗∗∗ -0.018

(0.005) (0.001) (0.000) (0.009) (0.00

Bank Log Assets -0.636∗∗ 0.096∗∗∗ 0.001 0.068 -0.01

(0.295) (0.009) (0.002) (0.148) (0.10

Bank Deposits to Assets 16.521∗∗ 0.764 0.080 5.704 3.18

(8.239) (0.609) (0.055) (4.722) (3.13

roa 1.275∗∗∗ -0.031 0.011∗∗ 0.978∗∗ 0.767

(0.443) (0.033) (0.004) (0.396) (0.29

Bank Equity to Assets 19.436 2.536∗ 0.124 11.549 9.79

(16.233) (1.416) (0.143) (12.182) (9.03

County × Year FE Yes Yes Yes Yes Yes Industry × Year FE Yes Yes Yes Yes Yes Observations 219,360 219,360 219,360 219,360 219,3Adjusted R2 0.623 0.540 0.130 0.099 0.09

Standard errors in parentheses. * p<.10, ** p<.05, *** p<.01 
58 

Table A.7: Online Banks and Women Borrowers 

The table uses data at the loan level from 2010-2019. The dependent variables are interest rate, SBA guarantestatus, charge-off to loan, and SBA loss to loan. The independent variables of interest include Online Bank, an if a lender is classifed as an online bank and 0 otherwise, and Woman Owned, a borrower ownership indicatorclustered at both the lender and borrower level. 

Guarantee Percent Interest Rate Charge-Off Status Charge-Off to Loan SBA Loss to Lo

(1) (2) (3) (4) (5) 

Online Bank 4.192∗∗ 0.526∗∗∗ 0.051∗∗∗ 3.904∗∗∗ 2.947

(1.658) (0.085) (0.017) (1.477) (1.149

Women Owned -0.012 -0.143∗∗∗ -0.016∗∗∗ -1.369∗∗∗ -0.849

(0.346) (0.042) (0.005) (0.362) (0.213

Online Bank × Women Owned 3.142∗∗∗ -0.039 0.044∗∗ 3.875∗∗ 2.695

(0.610) (0.076) (0.021) (1.761) (1.315

Loan Size Group 4.038∗∗∗ -0.922∗∗∗ 0.038∗∗∗ 2.303∗∗∗ 1.267∗

(0.438) (0.122) (0.005) (0.420) (0.246

Revolver Status -10.385∗∗∗ 0.542∗∗∗ -0.039∗∗∗ -2.072∗∗∗ -1.566

(1.493) (0.204) (0.009) (0.692) (0.462

Initial Interest Rate -0.356∗∗ 0.015∗∗∗ 1.176∗∗∗ 0.629∗

(0.170) (0.002) (0.134) (0.074

Log Term in Months 3.265∗∗∗ 0.089∗∗ -0.141∗∗∗ -9.575∗∗∗ -5.507

(0.378) (0.036) (0.012) (0.858) (0.409

Business is Corporation -0.107 0.052 0.006 0.393 0.295 

(0.228) (0.042) (0.007) (0.504) (0.294

Business is Individual -0.778∗∗∗ 0.166∗∗∗ 0.008 0.431 0.201 

(0.276) (0.057) (0.008) (0.583) (0.332

Branches in Zip Code 0.013∗∗∗ -0.001∗∗ -0.000∗∗∗ -0.036∗∗∗ -0.019

(0.005) (0.001) (0.000) (0.009) (0.005

Bank Log Assets -0.636∗∗ 0.096∗∗∗ 0.001 0.067 -0.01

(0.295) (0.009) (0.002) (0.148) (0.106

Bank Deposits to Assets 16.519∗∗ 0.764 0.080 5.690 3.179 

(8.239) (0.609) (0.055) (4.723) (3.140

roa 1.275∗∗∗ -0.031 0.011∗∗ 0.979∗∗ 0.768

(0.443) (0.033) (0.004) (0.397) (0.298

Bank Equity to Assets 19.448 2.537∗ 0.125 11.579 9.815 

(16.234) (1.416) (0.143) (12.200) (9.049

County × Year FE Yes Yes Yes Yes Yes Industry × Year FE Yes Yes Yes Yes Yes Observations 219,360 219,360 219,360 219,360 219,36Adjusted R2 0.623 0.540 0.130 0.099 0.098 

Standard errors in parentheses. * p<.10, ** p<.05, *** p<.01 
Table A.8: Credit Access to Minorities, Reporting-Only Sample 

The table uses matched data at the loan level from 2010-2019. Here we restrict the sample 

to only those borrowers that self-identify into one of the SBA business-type categories (e.g., 

veteran, disadvantaged, woman, minority). The dependent variable is Log Loan Amount. The 

independent variables of interest include Online Bank, an indicator equal to 1 if a lender is 

classifed as an online bank and 0 otherwise, borrower ownership indicators including Woman 

Owned, Minority Owned, Black Owned, and Hispanic Owned, and the ownership indicators 

interacted with Online Bank. Additional Controls include the loan controls, borrower controls 

and bank controls. Standard errors are clustered at both the lender and borrower level. 

Log Loan Amount 

(1) (2) (3) (4) (5) 

Online Bank 0.025 0.079 0.094 0.026 0.054 

(0.238) (0.270) (0.246) (0.239) (0.241) 

Online Bank × Woman Owned -0.257 

(0.231) 

Online Bank × Minority Owned -0.264 

(0.199) 

Online Bank × Black Owned 0.059 

(0.188) 

Online Bank × Hispanic Owned -0.570∗∗

(0.264) 

Woman Owned -0.160∗∗∗

(0.039) 

Minority Owned -0.095∗∗

(0.045) 

Black Owned -0.309∗∗∗

(0.094) 

Hispanic Owned -0.021 

(0.075) 

Additional Controls Yes Yes Yes Yes Yes 

County × Year FE Yes Yes Yes Yes Yes 

Industry × Year FE Yes Yes Yes Yes Yes 

Observations 5,780 5,780 5,780 5,780 5,780 

Adjusted R2 0.528 0.531 0.529 0.530 0.528 

Standard errors in parentheses. * p<.10, ** p<.05, *** p<.01 

59 
Table A.9: Online Banks and SBA Guarantee Percent 

The table uses data at the loan level from 2010-2019. The dependent variable is SBA guarantee 

percent. The independent variable is online bank, an indicator equal to 1 if a lender is classifed 

as an online bank and 0 otherwise. We include loan controls: loan size, revolver status, initial 

interest rate, and log term in months. We include business type, corporate or individual excluding partnership, as a borrower control. We control for bank characteristics: log assets, deposits 

to assets, ROA, and equity to assets. We include a variety of fxed effects. Column 1 includes 

county-year and industry-year fxed effects, column 2 includes borrower and industry-year fxed 

effects, and column 3 includes borrower-year fxed effects. Standard errors are clustered at both 

the lender and borrower level. 

Guarantee Percent 

(1) (2) (3) 

Online Bank 4.540∗∗∗ 5.786∗∗∗ 5.934∗∗∗

(1.739) (1.273) (2.206) 

Loan Size Group 4.786∗∗∗ 5.295∗∗∗ 6.305∗∗∗

(0.415) (0.420) (0.397) 

Revolver Status -10.571∗∗∗ -10.455∗∗∗ -10.303∗∗∗

(1.193) (0.840) (0.836) 

Initial Interest Rate -0.257 -0.042 0.085 

(0.175) (0.317) (0.381) 

Log Term in Months 2.555∗∗∗ 2.832∗∗∗ 3.171∗∗∗

(0.360) (0.374) (0.428) 

Business is Corporation -0.127 -1.677 -2.027 

(0.192) (1.589) (3.066) 

Business is Individual -0.485∗∗ -2.195 -0.895 

(0.231) (2.132) (3.433) 

Bank Log Assets -0.766∗∗∗ -0.959∗∗∗ -0.798∗∗∗

(0.265) (0.185) (0.301) 

Bank Deposits to Assets 9.647 -0.107 6.636 

(7.651) (4.839) (13.751) 

Bank Return on Equity 0.119∗∗∗ 0.011 0.213∗∗∗

(0.045) (0.030) (0.077) 

Bank Equity to Assets 32.186∗∗ 32.728∗∗∗ 42.126∗

(14.273) (8.638) (25.070) 

Borrower FE No Yes No 

Borrower × Year FE No No Yes 

County × Year FE Yes No No 

Industry × Year FE Yes Yes No 

Observations 421,334 92,032 59,654 

Adjusted R2 0.580 0.609 0.594 

Standard errors in parentheses. * p<.10, ** p<.05, *** p<.01 

60 
Table A.10: Online Versus Other Lenders 

This table reports univariate differences in lender IRR and IRR simulations between online 

banks and all other lender types at the loan level from 2010 to 2019. The columns report the 

mean of online bank lenders, the mean of all other lenders, the difference between the means, 

and the number of observations, respectively. Lender IRR is the estimated internal rate of return 

using the initial loan amount and the estimated annual cash fows of the loan. IRR Scenario 1 

assumes a reduction of the loan guarantee rates of online lenders from 75% and above to 50%. 

IRR Scenario 2 assumes a reduction of the loan guarantee rates for online lenders from 85% 

and above to 50%. IRR Scenario 3 assumes a reduction of the loan guarantee rates for online 

lenders from 90% and above to 50%. 

Online Banks 

(1) 

Other Lenders 

(2) 

Online Banks - Other Lenders 

(1-2) 

Mean Mean Diff. Obs. 

Lender IRR 

IRR Scenario 1 

IRR Scenario 2 

IRR Scenario 3 

4.97 

4.14 

4.23 

4.92 

4.86 

4.85 

4.85 

4.86 

0.11∗∗∗

-0.71∗∗∗

-0.62∗∗∗

0.06∗∗

459,696 

459,696 

459,696 

459,696 

61 
62 

Table A.11: Online Bank Lending and Distance to SBA Lender on Loan Terms and Outcome

The table uses data at the loan level from 2010-2019. The dependent variables are log loan amount, interest percent, charge-off status, charge-off to loan, and SBA loss to loan, and SBA proft to loan. The independent bank, an indicator equal to 1 if a lender is classifed as an online bank and 0 otherwise; distance to SBA lender, whmiles between the borrower and the nearest SBA lender branch; and the interaction between online bank and disWe include loan controls: loan size, revolver status, initial interest rate, and log term in months. We include busor individual excluding partnership, as a borrower control. We control for bank characteristics: log assets, depand equity to assets. We include county-year and industry-year fxed effects. Standard errors are clustered at borrower level. 

Log Loan Amount Interest Rate Guarantee Percent Charged Off Status Charge Off to Loan SBA Loss to 

(1) (2) (3) (4) (5) (6) 

Online Bank 0.159 0.436∗∗∗ 4.517∗∗∗ 0.051∗∗∗ 3.890∗∗∗ 2.824∗∗

(0.182) (0.089) (1.745) (0.014) (1.235) (0.975) 

Distance to SBA Lender -0.002 0.001∗ -0.005 0.000 0.004 0.002 

(0.002) (0.000) (0.004) (0.000) (0.007) (0.004) 

Online Bank × Distance to SBA Lender 0.011∗∗∗ 0.001 0.059∗ -0.001 -0.043 -0.041 

(0.003) (0.004) (0.035) (0.001) (0.049) (0.039) 

Revolver Status -0.717∗∗∗ 0.425∗∗∗ -10.572∗∗∗ -0.034∗∗∗ -1.619∗∗∗ -1.292∗∗

(0.112) (0.155) (1.193) (0.007) (0.552) (0.377) 

Initial Interest Rate -0.390∗∗∗ -0.257 0.017∗∗∗ 1.290∗∗∗ 0.697∗∗

(0.043) (0.175) (0.001) (0.125) (0.070) 

Log Term in Months 0.689∗∗∗ 0.096∗∗∗ 2.555∗∗∗ -0.125∗∗∗ -8.299∗∗∗ -4.748∗∗

(0.046) (0.031) (0.360) (0.011) (0.782) (0.372) 

Business is Corporation -0.010 0.046∗ -0.135 0.006 0.503∗ 0.364∗∗ 

(0.016) (0.027) (0.193) (0.004) (0.303) (0.170) 

Business is Individual -0.515∗∗∗ 0.207∗∗∗ -0.492∗∗ 0.018∗∗∗ 1.307∗∗∗ 0.714∗∗

(0.028) (0.053) (0.232) (0.004) (0.281) (0.157) 

Bank Log Assets -0.046∗∗∗ 0.093∗∗∗ -0.767∗∗∗ 0.001 0.067 -0.014 

(0.013) (0.011) (0.265) (0.002) (0.128) (0.090) 

Bank Deposits to Assets -0.104 0.715 9.641 0.042 2.792 1.531 

(0.369) (0.552) (7.651) (0.042) (3.597) (2.355) 

Bank Return on Equity -0.010∗∗∗ 0.000 0.119∗∗∗ 0.001∗∗∗ 0.091∗∗∗ 0.067∗∗

(0.003) (0.003) (0.045) (0.000) (0.032) (0.024) 

Bank Equity to Assets -0.553 1.883∗ 32.194∗∗ 0.104 10.054 9.427∗ 

(1.097) (1.124) (14.272) (0.094) (7.771) (5.642) 

Loan Size Group -0.796∗∗∗ 4.785∗∗∗ 0.030∗∗∗ 1.762∗∗∗ 0.997∗∗

(0.123) (0.415) (0.005) (0.360) (0.211) 

County × Year FE Yes Yes Yes Yes Yes Yes 

Industry × Year FE Yes Yes Yes Yes Yes Yes 

Observations 421,302 421,302 421,302 421,302 421,302 421,302 

Adjusted R2 0.591 0.489 0.580 0.124 0.094 0.090