# The Impact of Fintech Lending on Credit Access for U.S. Small Businesses
**Giulio Cornelli**
Bank for International Settlements
**Jon Frost**
Bank for International Settlements
**Leonardo Gambacorta**
Bank for International Settlements
**Julapa Jagtiani**
Federal Reserve Bank of Philadelphia Supervision, Regulation, and Credit
Department
**ISSN:** 1962-5361
**Disclaimer:** This Philadelphia Fed working paper represents preliminary research that is being
circulated for discussion purposes. The views expressed in these papers are solely those of
the authors and do not necessarily reflect the views of the Federal Reserve Bank of Philadelphia
or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.
Philadelphia Fed working papers are free to download at: https://philadelphiafed.org/researchand-data/publications/working-papers.
WP 22-14
PUBLISHED
April 2022
**DOI:** https://doi.org/
10.21799/frbp.wp.2022.14
---
# The Impact of Fintech Lending on Credit Access for U.S. Small Businesses
Giulio Cornelli
Bank for International Settlements
Jon Frost
Bank for International Settlements
Leonardo Gambacorta
Bank for International Settlements
∗
Julapa Jagtiani
Federal Reserve Bank of Philadelphia
April 21, 2022
## Abstract
Small business lending (SBL) plays an important role in funding productive investment and
fostering local economic growth. Recently, nonbank lenders have gained market share in the SBL
market in the United States, especially relative to community banks. Among nonbanks, fintech
lenders have become particularly active, leveraging alternative data for their own internal credit
scoring. We use proprietary loan-level data from two fintech SBL platforms (Funding Circle and
LendingClub) to explore the characteristics of loans originated pre-pandemic (2016‒2019). Our
results show that fintech SBL platforms lent more in zip codes with higher business bankruptcy
filings and higher unemployment rates. Moreover, fintech platforms’ internal credit scores were
able to predict future loan performance more accurately than the traditional approach to credit
scoring. Using Y-14M loan-level bank data, we also compare fintech SBL with traditional bank
business cards in terms of credit access and interest rates. Overall, fintech lenders have a potential
to create a more inclusive financial system, allowing small businesses that were less likely to
receive credit through traditional lenders to access credit and to do so at lower cost.
*Keywords:* fintech credit, peer-to-peer (P2P) lending, marketplace lending, small business lending
(SBL), Funding Circle, LendingClub, alternative data, credit access, credit scoring,
*JEL Classification*: G18, G21, G28, L21
* Please direct correspondence to Julapa Jagtiani at julapa.jagtiani@phil.frb.org. The authors thank Asani
Sarkar, Barbara Lipman, Bill Spaniel, Mitchell Berlin, and Bob Hunt for their helpful comments and suggestions.
Thanks to Adam Lyko, Erik Dolson, and Drew Taylor for their research assistance.
Disclaimer: The opinions expressed in this paper are the authors’ own views and do not necessarily represent
the views of the Federal Reserve Bank of Philadelphia, the Federal Reserve System, or the Bank for
International Settlements. Any errors or omissions are the responsibility of the authors.
---
## I. Introduction
Small business lending (SBL) plays an important role in funding productive investment and
fostering local economic growth (Beck and Demirgüç-Kunt, 2006). In the United States, community
banks are known to have comparative advantages in SBL through their personal relationships with
small business owners in their own local area. Until recently, the soft information that community
banks have about their local small businesses and business owners were not easily accessible to
outside lenders. Of the approximately 6,000 banks in the United States, about 90 percent are small
local banks that exist to serve the people and businesses in their local community.
In the last several years, the financial landscape for SBL has changed significantly. Fintech
lenders and other technology companies have shaken up the traditional ways of doing business.
Lending by fintech and big-tech firms has become increasingly important as a source of finance for
both consumers and small businesses around the world; see Financial Stability Board (2019),
Cornelli et al. (2019; 2020) and Ziegler et al. (2020). Soft information about businesses and
entrepreneurs can now be obtained from nontraditional channels. For example, customer ratings
and satisfaction about businesses may be available online. Information on the credibility and
reputation of business owners is also available through several data aggregators and artificial
intelligence (AI) vendors. In addition, through their use of digital platforms, some lenders can
incorporate various types of alternative data, including those related to online footprints, phone
and email history, location, etc. Digital platforms have allowed fintech lenders to serve borrowers
that may otherwise be unserved or underserved by incumbent financial institutions, even in
economies with relatively deep credit markets, like the United States.
While it seems that large U.S. banks have been increasing their SBL, this is true only when
compared with the overall banking industry. The origination and funding of SBL overall has shifted
dramatically over the past few years toward the nonbank (“shadow banking”) sector. This is partly
because of the increased regulatory burden since the financial crisis (e.g., from the Dodd‒Frank Act
of 2010 and the rising cost of small loan origination). While nonbank lenders are subject to some
consumer protection and other compliance requirements, they are not subject to the same rigorous
supervisory examination as banks, allowing nonbank lenders to compete with banks in SBL.1At the
same time, technological advances and the post-crisis pressure on bank business models may be
important drivers to the shift.
1ank lenders are, however, subject to significantly higher funding cost than banks Non-b since they do not
have access to low-cost funding through insured deposits.
---
Fintech lenders have increasingly become an important part of the nonbank SBL sector.
Funding Circle and LendingClub are examples of large fintech lenders that use big data and complex
algorithms such as AI/machine learning (ML) models to evaluate the credit risk of small businesses
and business owners,2and to make lending decisions at a much faster speed than traditional
lenders. Research has shown that fintech lenders are more efficient in making consumer loans than
traditional lenders operating at the same scale. For instance, Hughes, Jagtiani, and Moon (2022)
find LendingClub to be more efficient than traditional peer lenders (for consumer loans) of the
same size. One factor that contributes to enhanced lending efficiency at fintech platforms is their
ability to digitally collect and analyze nontraditional data, including what used to be referred to as
soft information in relationship lending. This allows them to capture a more complete financial
picture of the borrowers than traditional lenders can. This can improve access to credit. Jagtiani
and Lemieux (2018, 2019) find that fintech lenders have helped some below-prime consumers to
gain greater access to credit and at lower cost, compared with what they would have been able to
get through traditional channels.
Alternative data, which have been increasingly used by lenders to identify “invisible prime”
borrowers in consumer lending, have also been used to price credit risk in SBL. However, empirical
evidence on fintech lending efficiency has so far focused on consumer credit. In this paper, we
explore the roles of alternative data and the impact on credit access to small businesses.
In the United States, some fintech lenders have competed successfully with community banks.
In addition, fintech lenders have also helped to fill the SBL credit gap in certain communities
because of the SBL pullback and reduced market share by traditional banks. Fintech lenders often
have access to their own proprietary big data from payment platforms that gives them a bird’s-eye
view of the business, industry, and location in which a firm operates. Several big-tech payment
platforms, such as Amazon, and fintech payment firms, such as Square³ and PayPal, have also lent to
business owners who may have thin credit files, but whose cash flows and payment transactions
have been established through their payment platform. Other fintech SBL lenders, such as Kabbage,
2Fintech lenders, like Funding Circle and LendingClub, use AI/ML in developing the models that are
ultimately presented in the form of traditionally structured logit regression models. Thus, they are not
black-box models but using more complex algorithms and more data to achieve credit decisions that
would be explainable to investors and potentially regulators.
3In December 2021, Square rebranded itself and changed its name to “Block,” as the group aims to
emphasise business lines beyond its seller business (still branded as Square). Its ticker on the New York
Stock Exchange will remain SQ at least for some period of time. This rebranding is like that of other bigtech firms, such as Google that placed itself under the parent company Alphabet in 2015 and Facebook
placing itself under parent company Meta in October 2021.
---
OnDeck, Funding Circle and LendingClub use other alternative data in their lending decisions
(Goldstein, Jagtiani, and Klein (2019)).4
The higher cost of originating small loans has been overcome through a digitised credit
application and decision process, where the fixed cost of originating small short-term business
loans has become trivial, relative to the cost incurred by traditional lenders. However, there have
also been concerns about the potential impact of these disruptive business models on consumers,
business owners, and financial stability, especially if the fintech credit scoring techniques do not
prove to be valid in a different stage of the economic or financial cycle (such as a deep recession).
In this paper, we focus on fintech SBL similar to SBL originated by traditional lenders with loan
terms that are longer (typically three years or longer) and with interest rates that are comparable
with those offered by banks. We explore the capacity of fintech firms to facilitate access to credit for
small business owners who are headquartered in less financially developed areas and the
subsequent performance of such loans. Specifically, we ask the following research questions: Has
fintech lending enhanced credit access to small business owners who are likely to be “underserved”
by traditional lenders? Are there measurable differences in fintech credit scores with respect to
other traditional credit scores? What is the value of alternative data in credit risk evaluation and
credit decisions?
To do this, we use detailed microdata from Funding Circle’s small business platform, and we
compare this with the LendingClub SBL platform and traditional (business) credit cards data from
Y-14M (submitted monthly to the Federal Reserve by CCAR banks for stress testing purposes). Our
results show that, also in the SBL space, fintech lenders can serve borrowers who were less likely to
receive credit from traditional banks and that they employ alternative data to improve credit
scoring. Specifically, we find that fintech SBL platforms lent more in zip codes with higher business
bankruptcy filings and higher unemployment rates. Moreover, fintech platforms’ internal credit
scores were able to predict future loan performance more accurately than the traditional approach
to credit scoring. The Y-14M loan-level bank data allow us to compare fintech SBL with traditional
bank business cards in terms of credit access and interest rates. We confirm that fintech lenders
provide credit to additional borrowers at lower cost.
4During the coronavirus Pandemic, several fintech SBL lenders (namely, Square, PayPal, Intuit
Quickbooks, Funding Circle, and OnDeck) received approval by the U.S. regulators to originate Paycheck
Protection Program (PPP) loans to small businesses under the CARES Act of March 2020. Other fintech
platforms (Kabbage, Fundera, Lendio, Brex, Fundbox, Bluevine, and Nav) work with partner banks (such
as Cross River Bank, Celtic Bank, Radius Bank, and Sunrise Banks) to assist with the PPP loan approval
and origination.
---
It is important to note that our results in this paper, based on two specific fintech lenders, may
not be applicable to the entire fintech lending industry. The reason is that not all SBL products are
the same, and they could have a dramatically different impact on borrowers and the economy
overall. For example, some fintech lenders specialize in very small and short-term loans, with the
intention to help business owners’ deal with unexpected liquidity needs. Other fintech lenders
specialize in longer-term loans like those provided and supported by the U.S. Small Business
Administration (SBA). Within the fintech SBL space in the United States, loan products vary
significantly in terms of loan amounts ($5,000 to $500,000), maturity (60 days to five years or
longer), interest rates (7 percent to 200 percent annual percentage rate (APR)), and other features.
Still, the use of proprietary data from two major fintech lenders, and a comparison with supervisory
data from U.S. banks, allow for a more granular view of fintech credit than has been available in the
past.5This represents one step in a broader research agenda.
The rest of the paper is organized as follows. Section II reviews the literature and discusses
findings that are especially relevant to the roles of fintech in SBL. Section III describes the
proprietary loan-level data from Funding Circle’s small business lending platform. This section
highlights stylized facts and presents summary statistics of the data. We compare some of these
facts with aggregate data from the LendingClub SBL platform and from Y-14M bankcard data from
the CCAR monthly submission, to evaluate differences. Section IV discusses the empirical findings
related to the roles of alternative data in fintech SBL and the impact on small business owners to
access funding. Section V discusses conclusions and policy implications.
## 2. Related Literature
There is a growing body of research on the drivers of fintech consumer credit, on the impact on
credit access by consumers and on consumer privacy. However, the literature has been sparse on
fintech SBL and how it impacts credit access by small businesses, small firm performance, local
communities and the overall banking and economic outcomes. This section provides an overview.
A branch of the fintech literature has attempted to investigate the impact of fintech lending on
consumer credit access, and in some cases, asking if fintech lending is a substitute or complement
to bank credit. For U.S. consumer credit markets, Jagtiani and Lemieux (2018) find that
LendingClub consumer lending has penetrated areas underserved by traditional banks (e.g., in
5The two data sets used for this paper — Funding Circle and LendingClub — both offer interest rate in the
lower end and with their own self-imposed ceiling of 36 percent APR, with loan maturities ranging from
one to five years.
---
highly concentrated markets and areas with fewer bank branches per capita). Similarly, Erel and
Liebersohn (2021) examine fintech lending to small businesses through the Paycheck Protection
Program (PPP) in the U.S. during the pandemic. They find that fintech was disproportionately used
in zip codes with fewer bank branches, lower incomes, and more minority households, and by small
businesses with fewer banking relationships.6Another paper that examines the PPP program is
Howell, Kuchler, Snitkof, Stroebel, and Wong (2022). They find that fintech SBL lenders had a
higher minority share among the PPP loans and that fintech can reduce racial disparities among
small business owners. As for fintech mortgage lending, Jagtiani, Lambie-Hanson, and Lambie-
Hanson (2021) find that mortgage loans are more likely to be originated by a fintech lender in areas
in which there was a higher mortgage denial rate by traditional lenders in the previous period.
For both personal loans and mortgage loans, Dolson and Jagtiani (2021) find that fintech
lenders are more likely than other lenders (including both banks and other non-bank lenders) to
reach out and offer credit to non-prime consumers. Tang (2019) finds that online lending
substitutes for bank lending by serving marginal borrowers in the United States, but it
complements bank lending with respect to small loans. De Roure, Pelizzon, and Tasca (2016) use
credit market data in Germany, and they show that fintech lenders serve the segment of riskier
consumers who need small loans and are underserved by traditional banks. Thus, they conclude
that fintech lenders substitute traditional banks for high-risk consumer loans. Much of the
literature looks at the role of alternative data, including factors not traditionally considered to be
closely related to the ability to pay (e.g., digital footprints (Berg, Burg, Gombović, and Puri (2020)).7
Another strand of literature compares the behavior and pricing of fintech lenders with that of
traditional banks. Buchak, Matvos, Piskorski, and Seru (2018) compare the pricing of online
(fintech) lenders in the U.S. mortgage market with that of banks and (non-fintech) shadow banks.
They find that fintech lenders charge a premium of 14‒16 basis points relative to bank mortgages.
The reason is that fintech lenders use more comprehensive data about consumers to identify those
who likely prioritize convenience and faster services and are willing to pay a premium. Jagtiani,
Lambie-Hanson, and Lambie-Hanson (2021) find consistent results for conventional mortgages but
point to the opposite findings when focusing only on Federal Housing Administration (FHA) loans.
6The PPP was created by the U.S. government as a response to the lockdown during the COVID-19
pandemic. It was intended to assist small businesses by giving out loans to small businesses to keep their
employees on their payroll during the pandemic. The loans would be forgiven if they were used for the
intended purposes.
7See also Allen, Gu, and Jagtiani (2021) for a comprehensive literature survey of fintech research.
---
They conclude that conventional mortgage borrowers (who are generally well served in the
financial system) tend to pay an interest rate premium to fintech lenders in exchange for
convenience and faster services. FHA mortgage borrowers do not pay a premium rate but benefit
from fintech lenders through increased funding access.8Fuster, Plosser, Schnabel, and Vickery
(2018) find that fintech mortgage lenders process loan applications about 20 percent faster than
traditional lenders. Like mortgage borrowers, Mach, Carter, and Slattery (2014) find that peer-topeer lenders charge a premium (up to two times higher) for small business lending when compared
from traditional sources. Gambacorta, Huang, Li, Qiu, and Chen (2020) find that big-tech credit in
China is less sensitive to house prices than bank credit, as big data take the place of collateral in
mitigating asymmetric information in credit markets.
Traditional business lending could introduce biases based on a loan officer’s perception of loan
applicants, which affects loan approval and loan size. Carter, Shaw, Lam, and Wilson (2007) extract
four criteria used by the loan officer and compare these with the sex of the loan applicant. Loan
officers were more likely to assess female loan applicants on whether they had thoroughly
researched the business, while male applicants were assessed more on whether they had thorough
information about the business financial history, the business opportunity, and their personal
characteristics. Bellucci, Borisov, and Zazzaro (2010) find that female entrepreneurs face tighter
credit availability but do not differ in interest rates to their male counterparts. They also find that
female loan officers restrict credit to unestablished borrowers more than their male colleagues.
However, female loan officers are shown to ask female borrowers for collateral less often. Female
loan officers are also more concerned with the applicants’ marital status than male counterparts, as
it may suggest financial responsibility; see Carter, Shaw, Lam, and Wilson (2007). Atkins, Cook, and
Seamans (2021) explore the impact of race using data from the PPP during the pandemic and find
evidence supporting the hypothesis that fintech could reduce racial disparity in SBL. While Blackowned businesses received smaller PPP loans than White-owned businesses, the racial impact
became smaller and later disappeared as changes were made to allow for entry by fintech firms in
the second round of the PPP.
Evidence of discrimination in SBL is apparent elsewhere, too. Borrowers at traditional lenders
may be discouraged and simply not apply even if they need a further loan. Mijid and Bernasek
(2013) calculate a 38 percent loan denial rate for minorities and 14 percent for Whites, where firm
8FHA mortgage borrowers are more likely to be underserved, based on lower average income and lower
average risk scores, and they generally receive lower rates.
---
and owner characteristics can explain 24 percentage points of loan denial. Bates and Robb (2015)
find that firms in minority neighborhoods that need credit but do not apply are more common than
firms that do apply for bank loans. Han, Fraser, and Storey (2009) find evidence that
discouragement is an effective self-rationing mechanism. Risky borrowers filter themselves based
on demographics of the entrepreneur and business. However, Cole and Sokolyk (2016) find that for
every three discouraged firms, one would have been approved for a loan had they applied. This
represents a large inefficiency that fintech lending may help solve.
Literature on the use of small business credit scoring (SBCS) largely confirms that quantitative
scoring has expanded credit availability to small businesses. Frame, Srinivasan, and Woosley
(2001) find a positive relationship between the portfolio share of banks’ SBL and the use of credit
scoring models. Berger and Frame (2007) also associate SBCS with expanded credit quantities, but
they find that SBCS leads to greater average risk, along with increased lending to low-income areas,
over greater distances, and longer loan maturity. The introduction of SBCS aimed to give investors a
better understanding of borrower creditworthiness but leaves out important information. Using
only SBCS (ignoring a small business owner’s personal credit risk) could lead to inaccuracies in loan
decisions. Community banks are known to rely on soft information for lending decisions, and they
tend to use SBCS to supplement their credit decisions when evaluating small business credit.
Berger, Cowan, and Frame (2011) confirm that community banks use SBCS but also find that they
tend to use consumer credit scoring more than SBCS to evaluate small business loans.
In addition, the use of alternative data and ML has been shown in several cases to improve
credit assessments. Jagtiani and Lemieux (2019) find that rating grades from the LendingClub
consumer platform (based on all available information including alternative data) perform well in
predicting loan performance during the two years after loan origination date. The correlation
between the rating grades and the FICO scores declined over time from 2007 to 2015. Frost,
Gambacorta, Huang, Shin, and Zbinden (2019) show evidence that nontraditional data from
Mercado Libre in Argentina help to predict defaults relative to the traditional credit bureau. Lu
(2018) examines the credit assessment at Ant Financial Services Group (part of the Alibaba group,
the largest fintech firm in China), which helps an online-based bank make a credit assessment in
less than three minutes. MYbank (part of the Ant Financial Services) served over 20 million small
businesses as of 2019. More than three-quarters of MYbank loan users had previously never
received business loans from traditional banks. By using the Alibaba e-commerce network to track
small business trading history, MYbank is able to predict borrowers’ creditworthiness in minutes
(with zero human interaction), while its competitors (mostly larger banks) refuse to lend to these small businesses due to their lack of sufficient credit information. Gambacorta, Huang, Qiu, and
Wang (2019) find, with data from a Chinese fintech credit platform, that ML-based credit scoring
was better able to predict default than traditional indicators after the 2017 regulatory shock in
China.
Another strand of literature has looked at the impact of alternative data on credit access and
firm performance. For example, Hau, Huang, Shan, and Sheng (2018) and Huang, Lin, Sheng, and
Wei (2018) find that big-tech credit in China has reduced supply frictions in credit markets and that
Chinese firms with access to big-tech credit experience higher performance than their small
business firm peers. Dice and Liebersohn (2020) examine the response of fintech and nonbank
lenders to financial services demand created by the introduction of the PPP during the COVID-19
pandemic. They find that online banks and nonbank financial institutions are disproportionately
used by small businesses in areas with fewer banking services (measured by bank branches and
businesses with little banking relationship) and that borrowers were more likely to get a fintechenabled loan if they are in zip codes in which local banks were unlikely to originate PPP loans.
Overall, the efficiencies in digitizing various services by the banking industry can potentially
improve upon or replace the traditional credit scoring and soft information at the center of
relationship lending. By using big data on borrower demographics, nonbank lenders can implement
advanced algorithms to quickly and effectively risk-rank applicants.
## 3. Data and Stylized Facts
## Fintech Loan-Level Data
We use proprietary data on fintech SBL from Funding Circle, and later LendingClub. The data set
from Funding Circle contains loan-level data with a unique ID for each loan as well as
characteristics of the loans and borrowers. This includes the credit rating of the business owner
(FICO, VantageScore), the fintech credit rating of the business itself, firm-specific data (firm size,
age, revenues, profitability, and number of employees), business funding needs (number of recent
credit inquiries), and loan features (loan size, maturity, APR, fees, delinquency status, etc.). We then
9
match local economic factors for each loan based on the zip code or county location of the loan.
We also observe credit performance of each loan as of 24 months after its origination date. We
flag the loan as being delinquent if it is at least 60 days past due (60+ DPD) within the first 24
9There are 41,683 zip codes in the United States, and 3,141 counties and equivalent entities. Thus,
counties are generally larger. That said, zip codes can include parts of different counties; there is no oneto-one mapping.
---
months after the origination date. Note that loan maturities vary, and they are generally three to
five years.10
One important characteristic of our dataset is that we observe several risk ratings of each loan.
First is the *Business Owner’s Risk Score*, which comprises the FICO and Vantage scores (ranging from
300 to 850) for the business owner as of the loan application date. Second, we observe the *Business*
*Risk Score*, which is Experian’s Acquisition score assigned for the small business (rather than the
small business owner). This scale looks different than the usual risk scores as the Acquisition Score
is much more granular (from 100 to 100,000). Third, we observe the *Risk Rating Assigned by*
*Funding Circle*, which ranges from A+ to D. We use dummy variables in the regressions to indicate
that the loans are rated by Funding Circle as A-rated, B-rated, C-rated, or D-rated. The base case is
the best rating assigned by Funding Circle, which is A+.
## Bank-Level and County-Level SBL Data
In addition, we collect SBL data from the Community Reinvestment Act (CRA) reports that banks
file annually with federal regulators. Banks report the amount of SBL they originate (or purchase)
in each county and year. In addition to this flow data from the ***CRA reports***, we also collect stock
data of outstanding SBL from the ***Call Reports***, which are filed on a quarterly basis by each bank
with the federal regulators. Information on SBL originated by traditional banks is used to compute
measures of SBL concentration at the county level.
## Traditional Small Business Credit (Business Credit Cards) Data:
We use comparable business loan data (through credit cards) from the ***Federal Reserve’s Y-14M***
***reports.*** These data are reported monthly by bank holding companies with over $50 billion in
assets. We use a 1 percent random sample of all business credit card accounts reported in the Y-
14M data set. From this data set, we focus on those business card accounts that were open during
the period 2016‒2019 to match the small business loan data from Funding Circle and
10Of the more than 15,000 small business loans we have from the Funding Circle platform, about 5 percent
have a maturity of one year or less, and about 9 percent have a maturity of two years. The rest (about 86
percent of the loans) have a maturity of more than two years, with about 50 percent of all the loans being
five-year loans. See Table A1 in the Appendix for more details. In general, Funding Circle loans have a
longer maturity than other platforms. For example, the majority of the SBL originated by LendingClub
are small loans (less than $40,000), and almost all loans have a maturity of three years or less.
---
LendingClub.11For the most part, the Y-14M reports contain similar data on borrowers and other
risk characteristics as those reported in the Funding Circle and LendingClub database (e.g.,
origination date, origination amount, location of the borrowers, and borrowers’ credit scores). We
use data on business credit card loans from the Y-14M reports to compare with SBL originated by
Funding Circle and LendingClub.
We start with 548,808 business card accounts. After screening out those charge cards (no
credit limit) and those with missing business owner’s FICO scores, we are left with 453,385
accounts. We then drop those business card accounts that were opened with missing APR data or
with promotional rate of 0 percent APR. Our final sample includes 275,024 business card accounts
that were open during the period 2016‒2019 that have data on business owners’ FICO scores and
interest rate in APR.12
## Zip Code (or County)-Level Economic Factors
From the **FDIC Summary of Deposits**, we collect data on banking activity and the number of bank
branches in each local community where fintech loans are made. Other general economic factors
such as bankruptcy filings by businesses, market competition, house price indices, unemployment,
etc. are collected from *Haver Analytics*, CoreLogic database, and other sources. We collect income
data from the **American Community Survey (ACS) U.S. Census Bureau** (five-year estimates).
We then match the associated economic factors by the loan’s zip code or at the county level
(using the most granular level of data that is available). We have the house price index (HPI),
unemployment rate, business bankruptcy filings, and degree of bank competition and market
concentration at the county level, and we have median income of residents and population at the
zip code level. The Herfindahl-Hirschman index (HHI) of market concentration is calculated in two
different ways, based on shares of banks in SBL and in total bank deposits in a county. For the HHI
based on SBL share, the data on the share of SBL by each bank in a county come from the CRA
reports for banks that submit CRA reports; and from Call Reports (in conjunction with FDIC
Summary of Deposits reports) for small community banks that do not submit the CRA reports.
11We note that these data are constrained by the limited number of reporters and, as such, may not
represent the entire population of firms that issue business credit cards. However, Y-14M reporters do
represent over 80 percent of all credit cards issued by commercial banks.
12The final sample from Y-14M reports includes 65,158 business card accounts originated in 2016, 64,538
in 2017, 67,340 accounts in 2018, and 77,988 accounts in 2019.
---
Specifically, we apply the share of deposit-taking activities by each bank in each county to the
amount of SBL from the bank’s Call Reports for non-CRA reporters.
In addition to measuring market competition using the HHI based on SBL activities (by banks)
in a county, we also measure the number of bank branches per capita and changes in bank branches
at the zip code level. We estimate number of bank branches per capita (per 100,000 people) using
branching data from the FDIC Summary of Deposits reports and using population data (five-year
estimates) from ACS as reported in 2018.
**Table 1** summarizes the descriptive statistics of the variables used in the regressions. The
database covers the period 2016–2019. The first panel summarizes the variables used in the
regression that analyzes credit access. The Funding Circle SBL share has an average of 0.04 and a
maximum of 0.58, indicating that the macroeconomic relevance of such loans is generally small,
such credit is less than 1 percent of total SBL in the most served zip code.
Local economic factors include unemployment rate at the county level, the HPI at county level,
business bankruptcy filing at county level, and median income at the zip code level. Local economic
conditions are quite heterogeneous across counties or zip codes. For example, unemployment
ranges from 1.6 percent to 19 percent, while median income ranges from nearly $9,000 to more
than $243,000. As for the measure of SBL market concentration, we consider the HHI at the county
level, based on SBL by banks in each county in each year. Even in this case, conditions are quite
different across counties. In addition, we use market competition measures at the zip code level
based on banking service activities: 1) the number of bank branches per capita in each zip code; 2)
changes in the number of bank branches in each zip code from the previous year to the current
year; 3) percent changes in the number of bank branches in each zip code from the previous year to
the current year; and 4) a dummy indicator of whether the number of bank branches per capital in
the zip code has declined from the previous year to the current year.
The second panel of Table 1 describes summary statistics for the variables used in our simple
horse race models (described in the next section) that compare FICO, VantageScore, and the
Funding Circle internal risk rating score (FC risk scores). The FC risk score is assigned using the
company’s proprietary model. We use dummy indicators for each loan considering five categories,
from A+-rated (lowest risk) to D-rated (highest risk). Additional information on loan contract
maturity by Funding Circle risk bands is reported in **Table A1** in the Appendix. We define loans as
being delinquent as of 24 months (or 12 months) after origination, if the borrower has a late
payment (60+ days past due), as of 24 months (or 12 months) after origination, and zero otherwise.
---
The third panel of Table 1 includes the variables used in the regressions that model (in an
exhaustive way) the probability of default on a loan. These models include not only the three
different ratings on the borrower (FICO, Vantage, and FC risk scores) but also the business risk
score from Experian, the so-called Acquisition Score.13Moreover, the models include local
economic conditions, loan characteristics (e.g., loan amount, loan maturity in months, loan APR, and
year of origination) and borrower characteristics (such as business profits and business revenue).
The pairwise correlations between Funding Circle loan characteristics and economic factors are
presented in **Table A2** in the Appendix.
## Some Stylized Facts
Funding Circle SBL activity has increased in the period under investigation (see **Figure 1**). The data
contain loans originated from 2016 to mid-2019; thus, the volume looks smaller in the last
histogram rather than 2018 because it considers only six months (see left-hand panel). Our sample
includes a total of 15,027 loans (about $2 billion total; see right-hand panel). The amount of SBL
originated by Funding Circle in this period is quite remarkable, also considering other relevant
platforms operating in the US. For example, in the period 2015‒2018, the total of SBL originated by
LendingClub was only $540 million.
The average loan originated by Funding Circle in the period under analysis is around $134,000,
with a minimum of $25,000 and a maximum of $500,000. Just as a comparison, the average loan
originated by LendingClub is about half this value, or around $56,000, with a minimum of $2,000
and a maximum of $600,000. FC loans are directed to firms with an average number of 12
employees and gross revenues of $1.5 million (compared with 11.6 employees and $1.1 million for
LendingClub).
From the top panel of **Figure 2**, the FICO scores of the business owners range from 600 to 850
in each year, with a significant number of loans originated to below-prime business owners (red
and blue in the upper-left panel, with a FICO score below 680). About half of the loans are
associated with interest rates below 15 percent APR (green and orange in the upper-right panel).
13Different lenders may have different products and attributes from vendors such as Experian and Dun &
Bradstreet. Funding Circle uses the Experian Acquisition score (which is a rating for the business, rather
than the small business owner). The scale looks different than other scores (such as FICO or Vantage
Score), with a score of 7200 being in the 91st percentile. Unlike Funding Circle, the variable that
measures business risk score (on the LendingClub SBL platform) is called an *IP Score*. This is comparable
with the typical range used for FICO and other risk scores (from 300 to 850). Separately, for the measure
of business owner’s risk score, we have included the FICO scores and VantageScore for business owners
as of the loan application date.
---
The bottom panels of Figure 2 show that the originated loan size ranges from $25,000 to $500,000
(bottom-left panel), with maturity ranging from one year to five years. About half of the loans in
each rating grade are longer-term loans with a five-year maturity (bottom-right panel).
**Figure 3** shows that the top five states where loan are originated are the most populous:
California (CA), New York (NY), Florida (FL), Texas (TX), and Illinois (IL), although these add to less
than half of all the loan originations by Funding Circle (upper left-hand panel).14In the remaining
panels of Figure 3, it is notable that there is heterogeneity in firm profitability, firm size (as
measured by revenue), and loan maturity for each level of risk rating (A+ to D) assigned by Funding
Circle.
**Figure 4** shows that the correlation between the rating grades assigned by Funding Circle and
the traditional credit ratings assigned by FICO or Vantage Score have been almost always below 50
percent over the sample. This suggests that at least half of the variation in FC rating grades cannot
be explained by traditional credit information that is incorporated in the FICO or VantageScore.15
The correlation is slightly higher for VantageScore than for FICO. Notably, this correlation is even
lower when LendingClub credit rating is compared with FICO and VantageScore. As shown in
**Figure A3** in the Appendix, the correlation between the loan ratings assigned by LendingClub and
the traditional scores is around 30 percent.
**Figure 5** compares the distribution of FICO scores, VantageScore, and Funding Circle’s own
ratings. The plots show that the mode (median) score is 710 (715) for FICO, 650 (693) for
VantageScore; and A-rated for Funding Circle risk bands. About half of the loans received top
ratings (A or A+) from Funding Circle, although they may not be highly rated based on the
traditional credit scoring systems. In addition, **Figure 6** shows that a significant number of loans
that would traditionally be considered below prime, based on FICO score and VantageScore (red
and blue histograms), are assigned much better ratings (A or A+) by Funding Circle.
**Figure 7** presents the relationship between Funding Circle risk rating and the FICO/Vantage
Score ratings. This figure is divided into two panels: the left panel for FICO scores and the right
panel for Vantage scores. The size of the bubbles is in proportion to the share of the firms in each
14The full geographical distribution of SBL lending activity by state is reported in **Figure A1** of the
Appendix. The distribution is not too different from that of LendingClub (see **Figure A2** in the Appendix).
Just under half of LendingClub loans are to small businesses in the same five (most popular) states, but in
a slightly different order: California, Florida, Texas, New York, and Illinois.
15Specifically, at least half of the variations in FC rating grades cannot be explained by traditional credit
scores (FICO and Vantage scores in this case). However, some of the variation in FC rating grades may be
explainable by the same credit-related information that are used in the traditional credit-scoring process,
but they are used in a different way by fintech lenders.
---
rating distribution based on origination amount. Each of the panels presents a risk matrix with
Funding Circle credit rating compared with the two traditional ratings one at the time. On the
vertical axis, the panels report the delinquency rate (i.e., the percentage of loans more than 60 days
past due relative to the total number of loans). As the FICO and the Vantage scores are continuous
variables, we have segmented them into three different risk bands and then compared these with
the five different Funding Circle risk ratings (D through A+).
In the left panel, for a given FICO risk band (i.e., high risk), the expected loss rate is strictly
monotonic with the Funding Circle credit ratings (i.e., the patterns of the dots show that the
Funding Circle risk bands rank orders for expected loss). Conversely, given an internal rating (i.e., B,
C, or D), the delinquency rate is not strictly monotonic with the FICO score. For example, the dot
associated with the D Funding Circle risk bands for the low-risk FICO band indicates a higher risk
than the corresponding D rating in the medium-risk FICO band. Moreover, the Funding Circle risk
bands have a narrow range of default for each rating grade: high-default rates for D-rated and lowdefault rates for A-rated. In contrast, the range of default rate is broader based on FICO or Vantage
scores, ranging from delinquency rates of 1.7 percent to 21.4 percent for the low-risk FICO band.
Most important, by using its proprietary scoring model, Funding Circle has been able to make credit
available to those high-risk borrowers (based on FICO scores).
**Table 2** presents the delinquency rate for loans in the various risk buckets, based on Funding
Circle risk bands versus the traditional risk bands (FICO scores). The last column of Table 2
presents the portfolio share by FICO risk bands. As shown, 12.6 percent of the portfolio of loans
originated by Funding Circle would fall into the high-risk FICO cluster. Banks use a mix of FICO
score information and soft information from loan officers, but in general, they would not lend to
these borrowers in the U.S.16With its more granular scoring model, Funding Circle can offer credit
and in turn help these borrowers gain access to the SBL market.
**Table 3** reports the APR by Funding Circle rating grades and by FICO buckets. There is little
variation of interest rates across Funding Circle risk grades. For example, for loans with Funding
Circle D-rated, the associated APRs are almost the same regardless of their FICO scores, ranging
from 31.0 percent APR for the low-risk FICO band to 31.3 percent APR for the high-risk FICO band.
In contrast, we observe a wide variation of APR across FICO buckets. For example, for the high-risk
FICO bucket, interest rates range from 11.5 percent APR to 31.3 percent APR. This characteristic is
16Anecdotally, many U.S. banks use a cutoff and do not lend to borrowers with FICO credit scores below 580.
---
similar but less pronounced compared with the same distribution for LendingClub SBL platform
(see **Table A3** in the Appendix).
These simple statistics indicate that the internal rating system of Funding Circle differentiates
between borrowers more than the traditional credit ratings like FICO scores, thus allowing Funding
Circle to extend loans to borrowers who would otherwise be excluded from credit markets.
However, two aspects remain to be assessed. First, we need to verify whether Funding Circle
lending improves financial inclusion in underserved areas of the country. Second, we need to test
whether the Funding Circle rating system based on ML techniques and big data outperform (ex
post) the more traditional rating/scoring in predicting defaults. We perform this analysis in the
next section.
## 4. Empirical Analysis
## Credit Access and Financial Inclusion
In the first step of the empirical analysis, we want to assess where fintech SBL is more extensive, to
shed light on its role to improve credit access and thus financial inclusion of previously underserved
businesses and geographies. The estimation is specified as follows:
$$
{F C\,S B L\,r a t i o_{z c t}=\beta U n e m p l o y m e n t_{c t}+\gamma H P I_{c t}+\delta B B F_{c t}+\theta I n c o m e_{z c t}+\theta H H I_{c t}+\theta}\,{}
$$
(1)
where z, *c*, *t* stand for zip code, county, and time, respectively. Our dependent variable,
𝐹𝐹𝐹𝐹 𝑆𝑆𝑆𝑆𝑆𝑆 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑧𝑧𝑧𝑧𝑧𝑧is the ratio of SBL loans originated by the Funding Circle SBL platform in zip code *z*,
in county *c* in year *t* relative to total SBL loans originated by Funding Circle in year *t*. To control for
time-invariant state characteristics (such as state-specific unemployment benefits, property
taxation, or corporate rules), we include state fixed effects (𝛼𝛼).17
𝑠𝑠
$$
\mathbf{z},c,
$$
$$
F,C\,S B L\,r a t i o_{z c t}
$$
$$
Z,
$$
$$
(\alpha_{s}).
$$
The unemployment rate (𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈 𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈), house price index (𝐻𝐻𝐻𝐻𝐻𝐻), and level of business
bankruptcy filings in the last 24 months (BBF) are calculated at the county level, while the median
level of income (𝐼𝐼𝐼𝐼 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼) is at the zip code level. These variables capture economic factors that
could influence the development of fintech SBL in a specific geographic location. The HHI measures
the level of market concentration and is calculated as the sum of the squared share of SBL lending
by each bank in each county. To control for demographic factors, we also include the percentage
17We do not include origination year fixed effects because we want to focus on whether loans go into more
underserved areas — overall — rather than comparing within each origination year. We include
origination year fixed effects in the analysis at the loan level.
---
share of population in each zip code (𝑃𝑃𝑃𝑃𝑃𝑃 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃) as more loans are expected to be granted in an
area with a larger population. The results are presented in **Table 4**.
As a first-pass analysis, in the first column (Model 1) of Table 4, we correlate the FC SBL ratio
with a simple model that includes only the 𝑃𝑃𝑃𝑃𝑃𝑃 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 share. This model can explain 6 percent of
the variability of the FC SBL ratio.
In the second column (Model 2) of Table 4, we include the other (time varying) county/zip code
characteristics. The unemployment rate in a county is positively correlated with FC SBL share. A
one standard deviation increase in the unemployment ratio can be associated with an increase in
the FC SBL share by 0.002 percentage points in a specific zip code (1.07*0.002). This is
economically relevant as it represents 5 percent of Funding Circle credit in an average zip code.
An increase in house prices is positively correlated with the FC SBL share, but the effect is not
statistically significant. This may reflect the fact that fintech SBL is not typically collateralized (see
also Gambacorta et al., 2020). By contrast, the effect of the median income (calculated at the zip
code level) is statistically positive and significant, reflecting changes in demand conditions for firms
that translate into higher demand for credit by firms. A one standard deviation increase in median
income is associated with a rise in the FC SBL share by 0.004 percentage points (0.33*0.012). This
is also economically relevant as it represents around 10 percent of Funding Circle credit in an
average zip code.
Interestingly, fintech SBL origination is positively associated with a higher rate of business
bankruptcy filings, supporting our hypothesis that fintech SBL lenders could expand credit access
to more small business owners (especially those with little track record) through their use of
alternative data. The effect is economically relevant. A one standard deviation increase in BBF is
associated with a rise in the FC SBL share of 0.005 percentage points (0.0001*48.29). This
represents around 12 percent of Funding Circle credit in an average zip code. Market competition
(based on the share of SBL lending by each bank in each county) does not affect the FC SBL ratio.18
The last three columns (Models 3 to 5) of Table 4 control for changes in the structure of bank
branches. In particular, the third column (Model 3) considers a dummy that takes the value of 1 for
those counties that experienced a decrease in bank branches from the previous year, and zero
elsewhere. We find that in these counties, the FC SBL share is significantly higher. However, the
18Similar results are obtained using the share of deposit-taking activities by each bank in each zip code for
non-CRA reporters to estimate their SBL as a measure of market concentration (see **Table A4** in the
Appendix).
---
effect is positive but not statistically significant when considering the *percentage* decrease in bank
branches (Model 4) and the *percent change* in branches (Model 5).
In **Table 5**, we show the Shapley value decomposition of the statistical contribution to explain
the FC SBL share. This indicates that the population share and state fixed effects capture,
respectively 55 percent and 19 percent of the FC SBL share variability. Median income at the zip
code level explains 9 percent, while unemployment and bankruptcy filings explain a total of 12
percent, taken together. These last two variables represent a good proxy for the overall
contribution of financial inclusion factors that can be associated with FC SBL share.
## Ex-post Default Performance
As a second step of the analysis, we assess whether the Funding Circle rating system based on ML
techniques and big data outperforms (ex post) the more traditional rating scoring in predicting
defaults.
First, we compare the performance of the Funding Circle credit scoring model versus
traditional FICO and Vantage scores. Specifically, our goal is to assess whether the fintech credit
scoring model (based on ML plus big data) is better able to predict borrowers’ defaults than
traditional credit scoring models. The analysis is performed at the (more granular) loan level.
We start by estimating the following model to predict defaults:
$$
p(D_{i,t})=\phi\big(\alpha C r e d i t\,S c o r i n g_{i,t}+\mu_{S}+\mu_{T}+\varepsilon_{i,t}\big)
$$
𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 (2)
where 𝑝𝑝 (𝐷𝐷𝑖𝑖,𝑡𝑡) indicates the probability for the loan not to be repaid (and to generate a loss). The
credit scoring refers to borrower *i* at time *t*. We consider — one at the time — the FICO score, the
VantageScore, and the Funding Circle risk grades. The FICO and Vantage scores are continuous
variables, while the Funding Circle rating grade is organized into buckets. The model includes state
(𝜇𝜇𝑆𝑆) and time origination (𝜇𝜇𝑇𝑇) fixed effects.
$$
p(D_{i,t})
$$
$$
\left(\mu_{S}\right)
$$
$$
\left(\mu_{T}\right)
$$
The results are presented in the first three columns of **Table 6**. Panel (a) considers delinquency
as of 12 months from origination, while Panel (b) analyzes the effects as of 24 months after
origination. All models are estimated using a Logit regression model. Credit scores are always a
highly significant predictor of delinquency. However, the pseudo R² of the model that uses FC risk
grades (column 3) is significantly higher than that obtained using the FICO score (column 1) and
the VantageScore (column 2). The results are similar for both considering a 12-month and 24-
month delinquency horizon.
$$
\mathbb{R}^{2}
$$
**Table 7** compares the performance of the three different credit scoring approaches. The table
is divided into two panels, with two different delinquency rate horizons (12 months and 24 months after origination). Each panel reports the area under the receiver operating characteristics curve
(AUROC) for every credit scoring method. The AUROC is a widely used metric for judging the
explanatory power of credit scores. The AUROC ranges from 50 percent (purely random prediction)
to 100 percent (perfect prediction). The formal test on the difference in performance across the
models can be done comparing the 95 percent confidence interval reported in the last two columns
of Table 7, with significant improvement in predictive ability when moving from either FICO or
Vantage scores to Funding Circle rating grades.
**Figure 8** shows the receiver operating characteristics (ROC) curve for each credit scoring. The
ROC curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at
various threshold settings. The TPR is also known as *sensitivity*. The FPR is also known as the *fall-*
*out* or *probability of false alarm* and can be calculated as (1 − specificity).
The left-hand panel of Figure 8 reports the results for the three different credit scores
searching for unpaid loans as of 12 months after origination, while the right-hand panel repeats the
analysis for a 24-month performance window after origination. In both cases, the results show that
the Funding Circle risk grades perform better than the other two rating approaches. The difference
between the FICO and the Vantage scores is marginal, with the first one performing slightly better.
The three models are statistically different at the 5 percent level, as formally verified by inspection
of the last two column of Table 7
We conduct three additional tests with a view of shedding further light on the informational
advantage of the FC rating grades. First, as the FICO and the Vantage scores are continuous
variables while the FC rating grade is expressed in dummies, we have rerun all the results using
similar risk buckets for all three approaches. We divide the FICO score and the VantageScore into
five different buckets, to be comparable with the FC rating grades A to D. The results, reported in
**Figure A4** (in the Appendix), indicate that the FC rating grade has always a greater explanatory
power than the FICO score and the Vantage score.
Second, we perform a similar test by adding to the model the information content of the interest
rates. If interest rates are simply assigned based on the credit scores, they should add no additional
information. However, it is possible that in some cases interest rates may be assigned based on
additional pieces of information other than the credit ratings. We find that the APR is closely linked
to the Funding Circle risk band (see the left-hand panel of **Figure A5** in the Appendix), but there is
still some residual variability. For this reason, we have included in the last column of **Table 6** a
regression that includes the residual of a regression of the interest rate on FC rating grades. The test
aims to control for the fact that the interest rate could contain additional information that is not already included in Funding Circle rating grades. The coefficient on the APR residual in last column
of Table 6 is not statistically significant and the AUROC improves just marginally (see that last row of
Panel A and B of **Table 7**). This indicates that adjustments to APRs are not systemically in one
direction (to riskier or less risky borrowers) and/or that all relevant information available to
Funding Circle is already included in the FC rating grade.
Third, we consider how much the Funding Circle rating grade adds to a complete model that
includes the FICO score, the VantageScore and a set of further traditional variables. The rationale of
this test is to verify the contribution of Funding Circle’s own rating above and beyond what could be
captured by traditional credit ratings. **Table 8** presents the analysis of default probability as of 24
months after origination.19The first column reports the result including the FICO, the VantageScore
and a set of traditional variables. The second column reports the results including the Funding
Circle rating grade and the same set of the traditional variables. In both columns, we also include
the Acquisition score that is assigned to the specific business, rather than the small business owner.
Interestingly, moving from the first column to the second column, the R² increases by 3 percentage
points, from 11.0 percent to 14.0 percent. In the final column, we consider a model with all the
credit scoring approaches and the set of traditional variables, and the R² increases further to 14.4
percent. By comparing the R² in the first and the third column, we can infer that the Funding Circle
rating grade explains almost one third of the delinquency behavior in this more saturated model.
To wrap up, **Figure 9** shows a comparison between small business loans originated by the two
fintech lenders and the traditional credit source through credit cards issued by large banks (data at
account level from Y-14M). There is evidence supporting our earlier findings that fintech lenders
have the potential to move toward a more inclusive financial system where small business owners
who are considered below prime could get access to business funding and could do so at a lower
cost than otherwise.
Panel A of Figure 9 compares FICO distribution for loans that were originated in 2016‒2019. It
is interesting to see that borrowers who chose fintech lenders include those with relatively high
FICO scores as well as those below-prime business owners. About half of fintech lenders’ SBL
portfolios, from Funding Circle and LendingClub, are small business loans made to business owners
with FICO scores 700 or higher. However, this is still a much smaller portion compared with bank
19We also find consistent results when we observe delinquency within a shorter window of 12 months
after origination. The test is reported in Table A4 of the Appendix.
---
credits. About 80 percent of bank business cards were issued to business owners with FICO scores
above 700, and more than half of all business card holders have FICO scores above 750.
Panel B of Figure 9 compares the funding costs faced by business owners when borrowing from
the specific fintech lenders in our sample vs. through small business credit cards. About 50 percent
of small business loans originated by Funding Circle in each year have an APR below 15percent and
a substantial share of borrowers with FICO below 675 also received loans from Funding Circle with
an APR below 15 percent.20For LendingClub, a substantial share of borrowers with FICO below 675
received an APR below 20 percent. For bank business cards (Y-14M data), only a small portion of
business cards were issued to business owners with FICO scores below 675, and the (contractual)
interest rates charged to these business owners were mostly above 20 percent APR.
## 5. Conclusions
Our analysis, based on a unique proprietary data set of two large fintech SBL platforms and further
data on comparable bank lending over 2016–2019, supports the hypothesis that fintech lenders
have been able to expand credit access to those underserved small business owners who are not
likely to receive funding from traditional lenders. This may be particularly relevant for those small
business owners with a short credit history and those in areas that face a higher local
unemployment rate and higher rate of business bankruptcy. We indeed find that Funding Circle lent
to many firms that, because of the owner’s FICO score, would not have had access to bank loans,
and that it lent more in areas with higher unemployment and business bankruptcies.
Our results also suggest that alternative data about the small businesses and their owners
could play an important role in allowing fintech SBL platforms to expand credit access. We find that
the ratings that Funding Circle assigns to each loan were important in explaining the future credit
performance of the loans over the 24-month period after loan origination. The information used by
Funding Circle (in its process to risk rank each loan) are superior to the information content of
traditional credit risk measures such the FICO and Vantage scores. In a saturated model that
includes the business credit rating by rating agencies, the business owner’s credit rating by FICO or
Vantage scores, the general characteristics of the loan terms (maturity, origination date, loan
amount), and the local economic conditions where the businesses are located, our results indicate
that Funding Circle’s credit rating contributes significantly and explains about one-third of the
variation in a loan’s default probability. This finding is consistent with previous studies for fintech
20As mentioned earlier, not all fintech lenders are the same. These findings based on Funding Circle and
LendingClub may not be applicable to other fintech SBL platforms.
---
personal lending, and it provides support for the use of alternative data in small business lending as
well.
These findings have relevance for the role of fintech lenders going forward. Outside our period
of analysis, fintech lenders also played a role in facilitating loans to small mom-and-pop shops that
did not have established banking relationship during the COVID-19 pandemic, which began in
February/March 2020. When funding supply mostly dried up during the lockdown, most fintech
SBL lenders refocused their loan originations toward the U.S. PPP loans, and many partnered with
banks to assist with fraud, identity, and know your customer (KYC)/ anti-money laundering (AML)
work. While most banks had to prioritize their existing business customers in processing PPP loan
applications, leaving smaller businesses exposed to bankruptcy risk, fintech lenders entered the
space to fill the credit gap. Fintech partnerships with community banks during the pandemic made
it possible for partnered banks to reach new customers, allowing small banks as a group to
originate a larger share of PPP loans than their share of banking assets.21
While offering similar loan products as banks, fintech lenders have been subject to a different
set of regulations. All consumer loans from banks and nonbanks are generally subject to some
consumer protection laws, but nonbank lenders are not subject to the periodic onsite examinations
to which the banks are subject. However, through recent partnerships with banks, some fintech
platforms have also been subject to examination (as a significant banking service provider) or are
indirectly subject to the rigorous standards that banks must comply. Several fintech platforms have
recently become a bank either through acquisition or being granted a banking charter, allowing
them to access low-cost funding through insured deposits. Banks have also been investing and
partnering with fintech vendors to access today’s technology. In addition, new types of
bank/fintech partnership have emerged as regulators attempted to move cryptocurrency activities
under the banking regulation umbrella. Fintech platforms increasingly partner with banks to
provide the technology needed to issue stablecoins for faster business-to-business (B2B) payments
among businesses that have an account with banks in the network. Financial authorities continue to
adapt their supervisory process to remain effective in the new financial landscape. Previous
research has documented unintended consequences and the interplay among banking
21As an example, about 65 percent of PPP loans that were originated by Funding Circle during the pandemic
were new customers with no prior business relationship.
---
regulations.22In addition, coordination and collaboration among financial regulators across
agencies and across jurisdictions would be important in designing effective fintech regulations.
There have been concerns about a potential systemic impact of fintech failure on overall financial
stability. Unlike traditional lenders, fintech lenders that follow a P2P strategy (matching borrowers
and lenders directly) more strictly were not subject to default risk during the pandemic recession, as
they did not fund their loans on the balance sheet. Some fintech platforms that carried SBL loans on
their balance sheet faced significant losses, and some were acquired by banks during the pandemic;
for example, OnDeck and Kabbage were acquired by Enova and American Express, respectively.
It is important to remember that fintech lenders are not all the same; thus, the results found in
this paper may not necessarily apply to other fintech SBL platforms. Most important, we have
demonstrated the potential of what the fintech platforms and their use of alternative data could do
to move us toward a more inclusive financial system. Overall, banks and fintech lenders are
becoming more alike over time, and they are partnering more. In this way, they could potentially
work together to rebuild the economy during the recovery period, thereby enhancing financial
inclusion and the overall economic performance.
22Jagtiani, Vermilyea, and Wall (2018) discusses basic third-party vendor risk exposure and the new
supervisory guidelines on third-party risk management. Allen, Gu, and Jagtiani (2021 and 2022) discuss
the impact of fintech and cryptocurrency activities on consumers and the financial system, as well as the
current status on fintech regulations. See also Financial Stability Board (2017), Allen, Goldstein, and
Jagtiani (2018), and Jagtiani and John (2019) for discussion on fintech and financial stability.
---
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Allen, F., X. Gu, and J. Jagtiani (2021). “A Survey of Fintech Research and Policy Discussion.” *Review*
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Allen, F., I. Goldstein, and J. Jagtiani (2018). “The Interplay Among Financial Regulations, Resilience
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---
**Table 1: Descriptive Statistics — Funding Circle**
Sample includes loan-level data from Funding Circle SBL platform for the period 2016‒2019
| | Observations | Mean | St. Dev. | Min | Max |
| --- | --- | --- | --- | --- | --- |
| Credit access analysis | | | | | |
| Funding Circle SBL share1 | 9,688 | 0.04 | 0.04 | 0.00 | 0.58 |
| County unemployment(%) | 9,688 | 3.96 | 1.07 | 1.61 | 18.80 |
| County HPI(in'00s) | 9,688 | 1.96 | 0.51 | 0.89 | 3.91 |
| County business bankruptcy filings per capita | 9,688 | 0.0001 | 0.0001 | 0.0000 | 0.0006 |
| Zip Median income(in$100,000s) | 9,688 | 0.82 | 0.33 | 0.09 | 2.43 |
| HHI:SBL concentration(in'000s) | 9,688 | 0.82 | 0.53 | 0.29 | 6.87 |
| Population(%) | 9,688 | 0.04 | 0.03 | 0.00 | 0.25 |
| Dummy, decrease in branches | 9,688 | 0.22 | 0.41 | 0.00 | 1.00 |
| Percentage decrease in branches | 9,688 | -0.03 | 0.07 | -0.75 | 0.00 |
| Percent change in branches | 9,688 | -0.02 | 0.10 | -0.75 | 2.00 |
| Defaults as predicted by FICO,Vantage Score,FC risk grade | | | | | |
| 12-month delinquency rate | 15,040 | 0.04 | 0.19 | 0 | 1 |
| 24-month delinquency rate | 15,040 | 0.07 | 0.25 | 0 | 1 |
| FICO at origination | 15,040 | 717 | 45 | 604 | 843 |
| VantageScore | 15,030 | 698 | 56 | 492 | 836 |
| FC ratingA | 15,040 | 0.31 | 0.46 | 0 | 1 |
| FC ratingB | 15,040 | 0.29 | 0.45 | 0 | 1 |
| FC ratingC | 15,040 | 0.14 | 0.35 | 0 | 1 |
| FC ratingD | 15,040 | 0.05 | 0.21 | 0 | 1 |
| APR residuals | 13,392 | 0.00 | 0.01 | -0.07 | 0.19 |
| Default probability as of 24 months after origination | | | | | |
| Delinquent loan dummy2 | 11,640 | 0.07 | 0.26 | 0.00 | 1.00 |
| Acquisition score | 11,640 | 6332 | 13856 | 100 | 99900 |
| FICO at origination | 11,640 | 715 | 45 | 604 | 843 |
| VantageScore at origination | 11,635 | 697 | 56 | 492 | 836 |
| FC ratingA | 11,640 | 0.31 | 0.46 | 0.00 | 1.00 |
| FC ratingB | 11,640 | 0.28 | 0.45 | 0.00 | 1.00 |
| FC ratingC | 11,640 | 0.14 | 0.35 | 0.00 | 1.00 |
| FC ratingD | 11,640 | 0.05 | 0.21 | 0.00 | 1.00 |
| Ln(profit) | 11,640 | 10.81 | 1.34 | 1.39 | 15.16 |
| Ln(gross revenue) | 11,640 | 13.59 | 1.09 | 9.87 | 18.09 |
| Ln(loan amount) | 11,640 | 11.53 | 0.79 | 10.13 | 13.12 |
| Loan maturity in months | 11,640 | 47.11 | 14.72 | 6.00 | 60.00 |
| County unemployment | 11,640 | 3.62 | 0.95 | 1.56 | 16.98 |
| County HPI | 11,640 | 209 | 54 | 98 | 391 |
| County business bankruptcy filings per capita | 11,640 | 0.00008 | 0.00005 | 0.00000 | 0.00150 |
Footnote:
1. Ratio of SBL loans originated (by Funding Circle SBL platform) in zip code *i* in year *t* relative to total SBL
loans (in all zip codes) originated in year *t*.
2. Takes the value1 if the loan becomes delinquent (60+DPD) as of 24 months after origination; and zero
otherwise.
Sources: Funding Circle, CRA data, FDIC Summary of Deposits, Call Reports, and Haver Analytics.
---
**Table 2: Delinquency Rate by Funding Circle Rating Grades and FICO Scores**
Delinquency rates are defined as the share in the total number of outstanding loans 60 days
or more past due, divided by the total number of loans. These are shown for different ranges
of FICO scores and Funding Circle risk bands, over the period 2016–2019. The (discrete)
Funding Circle credit ratings at origination are divided into five different risk groups (A+
through D), while the (continuous) scores of the FICO credit bureau are divided into three
corresponding to risk level: Low Risk (FICO>739); Medium Risk (FICO between 670 and
739); and High Risk (FICO<670).
| | Funding Circle Risk Grades | | | | | Total FICO | Portfolio Share |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | D | C | B | A | A+ | | |
| Low Risk | 21.4% | 11.3% | 5.1% | 3.3% | 1.7% | 3.6% | 36.3% |
| FICO Band Medium Risk | 17.4% | 10.6% | 7.1% | 4.3% | 3.5% | 6.7% | 51.1% |
| High Risk | 21.1% | 13.3% | 10.5% | 7.6% | 6.0% | 11.8% | 12.6% |
| Total FC Risk Grade | 19.3% | 11.5% | 7.3% | 4.3% | 2.6% | 6.5% | |
| Portfolio Share | 3.1% | 12.3% | 27.5% | 33.8% | 23.3% | | |
Source: Authors’ calculations based on data from Funding Circle.
**Table 3: APR by Funding Circle Rating Grades and FICO Scores**
| | Funding Circle Risk Grades | | | | | Total FICO | Portfolio Share |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | D | C | B | A | A+ | | |
| Low Risk | 31.0% | 23.1% | 18.9% | 13.9% | 11.2% | 14.4% | 36.3% |
| FICO Band Medium Risk | 31.1% | 23.1% | 18.8% | 14.0% | 11.4% | 17.4% | 51.1% |
| High Risk | 31.3% | 23.1% | 18.9% | 14.3% | 11.5% | 20.4% | 12.6% |
| Total FC Risk Grade | 31.2% | 23.1% | 18.9% | 14.0% | 11.3% | 16.9% | |
| Portfolio Share | 3.1% | 12.3% | 27.5% | 33.8% | 23.3% | | |
| Source: Authors' calculations based on data from Funding Circle. | | | | | | | |
Source: Authors’ calculations based on data from Funding Circle.
---
## Table 4: Credit Access Analysis
The sample is based on loan-level data from Funding Circle SBL Platform for the period: 2016
Q1–2019 Q2. All regressions include constant and state-level dummy indicators. Dependent
variable is Funding Circle SBL Share, which is defined as the ratio of SBL loans originated
(by Funding Circle SBL platform) in zip code *i* in year *t* relative to total SBL loans (in all zip
codes) originated in year *t*.
| | Funding Circle SBL Share | | | | |
| --- | --- | --- | --- | --- | --- |
| | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 |
| County unemployment | | 0.00197*** | 0.00199*** | 0.00197*** | 0.00197*** |
| County unemployment | | (0.000422) | (0.000423) | (0.000422) | (0.000422) |
| County HPI(in‘00s) | | 0.000124 | 0.000142 | 0.000122 | 0.000109 |
| County HPI(in‘00s) | | (0.00132) | (0.00132) | (0.00132) | (0.00132) |
| County business bankruptcyfilings per capita | | 48.29*** | 48.01*** | 48.32*** | 48.27*** |
| County business bankruptcyfilings per capita | | (7.092) | (7.070) | (7.091) | (7.096) |
| Median income(in‘00,000s) | | 0.0118*** | 0.0118*** | 0.0118*** | 0.0118*** |
| Median income(in‘00,000s) | | (0.00131) | (0.00131) | (0.00131) | (0.00131) |
| SBL concentration(in‘000s) | | 0.00106 | 0.00108 | 0.00105 | 0.00107 |
| SBL concentration(in‘000s) | | (0.000829) | (0.000829) | (0.000829) | (0.000828) |
| Population(%) | 0.289*** | 0.293*** | 0.290*** | 0.292*** | 0.292*** |
| Population(%) | (0.0170) | (0.0180) | (0.0181) | (0.0180) | (0.0180) |
| Dummy, decrease in branches | | | 0.00209** | | |
| Dummy, decrease in branches | | | (0.000963) | | |
| Percentage decreasein branches | | | | 0.00293 | |
| Percentage decreasein branches | | | | (0.00465) | |
| Percent change in branches | | | | | 0.00347 |
| Percent change in branches | | | | | (0.00323) |
| Model | OLS | OLS | OLS | OLS | OLS |
| Observations | 10,279 | 9,688 | 9,688 | 9,688 | 9,688 |
| R² | 0.074 | 0.085 | 0.085 | 0.085 | 0.085 |
Note: ***/**/* denotes results that are significant at the 1%/5%/10% levels, respectively.
Data Sources: Funding Circle, CRA data, FDIC Summary of Deposits, Call Reports, and Haver Analytics.
---
**Table 5: Economic Contribution of Factors in the Credit Access Analysis**
The economic contributions refer to the econometric models reported in Table 4. The sample
includes loan-level data from Funding Circle SBL Platform for the period: 2016–2019.
| | Model 2 | | Model 3 | | Model 4 | | Model 5 | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Regressor | Shapley Value | Percent | Shapley Value | Percent | Shapley Value | Percent | Shapley Value | Percent |
| Country unemployment | 0.00353 | 4.17% | 0.00354 | 4.16% | 0.00353 | 4.17% | 0.00353 | 4.17% |
| County HPI(in‘00s) | 0.00365 | 4.31% | 0.00368 | 4.32% | 0.00364 | 4.30% | 0.00364 | 4.30% |
| County business bankruptcy filings per capita | 0.00622 | 7.35% | 0.00617 | 7.25% | 0.00622 | 7.36% | 0.00621 | 7.34% |
| Median income(in‘00,000s) | 0.00792 | 9.37% | 0.00785 | 9.23% | 0.0079 | 9.34% | 0.0079 | 9.33% |
| SBL concentration(in‘000s) | 0.00033 | 0.39% | 0.00033 | 0.39% | 0.00033 | 0.39% | 0.00033 | 0.39% |
| Population(%) | 0.04645 | 54.94% | 0.04596 | 54.03% | 0.04643 | 54.89% | 0.04643 | 54.85% |
| Dummy decrease in branches | | | 0.00096 | 1.12% | | | | |
| Percentage decrease in branches | | | | | 0.00011 | 0.13% | | |
| Per cent change in branches | | | | | | | 0.00018 | 0.21% |
| State-level dummies | 0.01645 | 19.46% | 0.01658 | 19.49% | 0.01642 | 19.41% | 0.01642 | 19.40% |
| Sources:Funding Circle,CRA data,FDIC Summary of Deposits,Call Reports,and Haver Analytics. | | | | | | | | |
---
## Table 6: Probability of Defaults as Predicted by FICO, Vantage Score, and Funding Circle Risk Grades
The table reports the estimates for a logit regression analysis. All regressions include state
dummies and origination year dummies.
| | Panel A:12-Month Delinquency Rates | | | |
| --- | --- | --- | --- | --- |
| | (1) | (2) | (3) | (4) |
| FICO Score | -0.00943***(0.00101) | | | |
| VantageScore | | -0.00706***(0.000795) | | |
| FC Risk Grades | | | | |
| A | | | 0.575***(0.182) | 0.573***(0.194) |
| B | | | 1.188***(0.172) | 1.235***(0.182) |
| C | | | 1.830***(0.175) | 1.837***(0.186) |
| D | | | 2.361***(0.191) | 2.392***(0.200) |
| APR residuals | | | | 2.957(4.639) |
| Observations | 15,017 | 15,007 | 15,017 | 13,337 |
| Pseudo $R^{2}$ | 0.0689 | 0.0670 | 0.104 | 0.109 |
**Panel B: 24-Month Delinquency Rates**
**(II) (III)**
| | (I) | (II) | (III) | (IV) |
| --- | --- | --- | --- | --- |
| FICO Score | -0.0104***(0.000809) | | | |
| VantageScore | | -0.00774***(0.000638) | | |
| FC Risk Grades | | | | |
| A | | | 0.601***(0.134) | 0.622***(0.143) |
| B | | | 1.178***(0.127) | 1.227***(0.137) |
| C | | | 1.748***(0.133) | 1.736***(0.143) |
| D | | | 2.271***(0.150) | 2.279***(0.159) |
| APR residuals | | | | 2.862(3.688) |
| Observations | 14,961 | 14,951 | 14,961 | 13,281 |
| Pseudo R2 | 0.0916 | 0.0889 | 0.118 | 0.118 |
| Source: Funding Circle | | | | |
---
**Table 7: Receiver Operating Characteristic (ROC) Curves**
The table reports the estimates for the logit regression, which include state- and origination
year-level dummies, as reported in Table 6.
| | Panel A:ROC Curves—12-Month Delinquency Rates | | | | |
| --- | --- | --- | --- | --- | --- |
| | Observations | AUROC | Std. Err. | 95% Confidence Interval | |
| FICO Score | 13,337 | 0.7176 | 0.0107 | 0.6967 | 0.73853 |
| VantageScore | 13,337 | 0.7136 | 0.0109 | 0.69228 | 0.73487 |
| FC Risk Grades | 13,337 | 0.7645 | 0.0102 | 0.74455 | 0.7844 |
| FC Risk Grades and APR Residuals | 13,337 | 0.7676 | 0.0101 | 0.74778 | 0.78733 |
| | Panel B:ROC Curves—24-Month Delinquency Rates | | | | |
| | Observations | AUROC | Std. Err. | 95% Confidence Interval | |
| FICO Score | 13,281 | 0.7312 | 0.0084 | 0.71482 | 0.74764 |
| VantageScore | 13,281 | 0.7243 | 0.0084 | 0.70783 | 0.74086 |
| FC Risk Grades | 13,281 | 0.7643 | 0.0079 | 0.74883 | 0.77981 |
| FC Risk Grades and APR Residuals | 13,281 | 0.7665 | 0.0079 | 0.75102 | 0.78206 |
| Source:Funding Circle. | | | | | |
---
## Table 8: Default Probability as of 24 Months After Origination
The table reports the estimates for a logit regression which include a constant, state dummies,
and origination-year dummy indicators. The sample includes loan-level data from Funding
Circle SBL platform for the period: 2016‒2019. Dependent variable is a binary variable that
takes the value of 1 if the loan becomes delinquent (60+DPD) as of 24 months after origination
and zero otherwise.
| | Delinquent Loan Dummy—as of 24 Months After Origination | | |
| --- | --- | --- | --- |
| | (1) | (2) | (3) |
| Acquisition score | -0.00001*(0.000005) | -0.00001**(0.000004) | -0.00001**(0.000004) |
| FICO at origination | -0.00727***(0.00121) | | -0.00526***(0.00124) |
| VantageScore | -0.00418***(0.000970) | | -0.000370(0.00101) |
| FC rating A | | 0.609***(0.150) | 0.494***(0.152) |
| FC rating B | | 1.245***(0.143) | 1.044***(0.152) |
| FC rating C | | 1.852***(0.151) | 1.589***(0.164) |
| FC rating D | | 2.497***(0.173) | 2.165***(0.189) |
| Ln(profit) | -0.0460(0.0306) | -0.0216(0.0317) | -0.0300(0.0318) |
| Ln(gross revenue) | -0.217***(0.0525) | -0.214***(0.0528) | -0.201***(0.0533) |
| Ln(loan amount) | 0.547***(0.0734) | 0.572***(0.0760) | 0.606***(0.0771) |
| Loan maturity in months | 0.00402(0.00299) | 0.00537*(0.00307) | 0.00466(0.00310) |
| Unemployment | 0.0493(0.0377) | 0.0207(0.0388) | 0.0235(0.0388) |
| County HPI | 0.00177(0.00125) | 0.000936(0.00128) | 0.00121(0.00129) |
| County business bankruptcyfilings per capita | -1,358*(798.9) | -1,551*(802.2) | -1,487*(808.4) |
| Observations | 11,580 | 11,585 | 11,580 |
| Pseudo $ R^{2}$ | 0.110 | 0.140 | 0.144 |
Note: ***/**/* denotes results that are significant at the 1%/5%/10% levels, respectively.
Sources: Funding Circle, CRA data, FDIC Summary of Deposits, Call Reports, and Haver Analytics.
---
**Figure 1: Funding Circle SBL Activity (2016 to mid-2019)**
Data Source: Funding Circle.
---
|Number of Loans|Figure 2: Funding Circle Loan Distribution: by FICO, APR, Amount, Maturity|
|---|---|
|Borrowers’ FICO Score by Origination Year|Borrowers’ APR by Origination Year|
|Funding Amount by Origination Year Data Source: Funding Circle.|Loan Maturity Distribution|
---
**Figure 3: Funding Circle Loan Distribution: by States; Profits, Revenue, and**
## Maturity Across Credit Ratings
Number of Loans
Top 5 States by Origination Year Borrowers’ Profit by Funding Circle Risk Rating
Borrowers’ Revenue by Funding Circle Risk Rating Loan Maturity Distribution by Funding Circle Risk Rating
Data Source: Funding Circle.
---
**Figure 4: Correlation Between Funding Circle’s Risk Bands and Traditional**
**Credit Scores (FICO and VantageScore)**
Data Source: Funding Circle.
**Figure 5: Funding Circle Loan Distribution by FICO vs. VantageScore vs.**
**Funding Circle Own Ratings**
Frequencies
**Note**: Additional statistics on the distribution of credit scores:
FICO: median score = 715; mean score = 717
VantageScore: median score = 693; mean score = 698
Funding Circle: a total of 15,096 loans, the median rating (at 7,548 position) is A-rated.
Data Source: Funding Circle.
---
**Figure 6: Funding Circle Loan: Credit Score Distribution by Funding Circle’s**
**Own Rating Grades**
Number of Loans
Data Source: Funding Circle.
---
**Figure 7: Delinquency Rates Decline Significantly for Higher FC Risk Bands,**
**Controlling for FICO and VantageScore Bands**
This figure shows the delinquency rate (i.e., the percentage of loans more than 60 days past
due relative to the total number of loans). The plots are calculated based on a dummy that
takes value 1 if the loan becomes delinquent (60+days-past-due) as of 24 months after
origination and zero otherwise. The size of each bubble is proportional to the total origination
amount.
In Percent
Default Rate for FC Risk Band and FICO
Data Source: Funding Circle.
Default Rate for FC Risk Band and Vantage Score
---
**Figure 8: Predictive Power of FICO, VantageScore, and Funding Circle Risk**
**Grades**
The x-axes show the fraction of false positives, whereas the y-axes show the fraction of true positives. The
higher the curve the stronger the performance of the model. Based on the models in Table 6.
Data Source: Funding Circle.
---
**Figure 9: Comparing Interest Rates on SBL Originated by Fintech Lenders vs. Traditional**
**Banks (Controlling for Business Owners’ Credit Scores)**
**Panel A: FICO Distribution by Origination Year (2016‒2019)**
Funding Circle SBL LendingClub SBL
Sources: Funding Circle, LendingClub, and Y-14M
Banks (Business Cards)
**Note**: Borrowers from fintech lenders include those with relatively high FICO scores and those below prime.
Many creditworthy SBL owners also choose to take out loans from fintech lenders. About half of fintech lenders’
SBL portfolios, from Funding Circle and LendingClub, are small business loans made to business owners with
FICO scores of 700 or higher. In contrast, about 80 percent of bank business cards were issued to business
owners with FICO score above 700, and more than half of all business card holders have FICO above 750.
**Panel B: APR Distribution by Business Owners’ FICO Scores and Origination Year**
Funding Circle SBL LendingClub SBL Banks (Business Cards)
Source: Funding Circle, LendingClub, and Y-14M
**Note**: About 50 percent of small business loans originated by Funding Circle in each year have an APR below
15 percent (not shown here) and a significant amount of SBL with FICO below 675 also received an APR below
15 percent. For LendingClub, a significant amount of SBL with FICO below 675 received an APR below 20
percent. For bank business cards (Y-14M data), a small portion of business cards were issued to business
owners with FICO below 675, and the (contractual) interest rates charged to these business owners were
mostly above a 20 percent APR.
---
## Appendix
| FC Risk Band
Loan Maturity in Months | A+ | A | B | C | D | Total |
| --- | --- | --- | --- | --- | --- | --- |
| 6 | 0.34% | 0.15% | 0.13% | 0.06% | 0.01% | 0.68% |
| 12 | 1.20% | 1.15% | 0.87% | 0.39% | 0.13% | 3.73% |
| 24 | 2.35% | 2.44% | 2.67% | 1.17% | 0.40% | 9.04% |
| 36 | 4.99% | 6.45% | 6.96% | 3.37% | 1.03% | 22.80% |
| 48 | 2.97% | 4.16% | 4.49% | 2.33% | 1.26% | 15.21% |
| 60+ | 9.91% | 16.24% | 13.43% | 6.98% | 1.99% | 48.54% |
| Total | 21.76% | 30.59% | 28.54% | 14.30% | 4.82% | 100% |
Data Source: Funding Circle.
---
| | Loan amount | Loan maturity in months | Acquisition score | Loan APR | FICO at origination | Vantage Score | 12-month del1cy dummy1 | 24-month del1cy dummy2 | FC ratingA+ | FC ratingA | FC ratingB | FC ratingC | FC ratingD | Origination year 2016 | Origination year 2017 | Origination year 2018 | Origination year 2019 | Ln(profit) | Ln(gross revenue) | Unemployment | HPI | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Loan maturity in months | 0.16 | 1 | | | | | | | | | | | | | | | | | | | | |
| Acquisition score | -0.06 | -0.01 | 1 | | | | | | | | | | | | | | | | | | | |
| Loan APR | -0.14 | 0.02 | -0.02 | 1 | | | | | | | | | | | | | | | | | | |
| FICO at origination | 0.15 | -0.03 | 0.03 | -0.41 | 1 | | | | | | | | | | | | | | | | | |
| VantageScore | 0.15 | -0.03 | 0.02 | -0.46 | 0.72 | 1 | | | | | | | | | | | | | | | | |
| 12-month delinquency dummy1 | 0.02 | 0.00 | -0.02 | 0.15 | -0.09 | -0.08 | 1 | | | | | | | | | | | | | | | |
| 24-month delinquency dummy2 | 0.04 | 0.02 | -0.03 | 0.15 | -0.12 | -0.12 | 0.74 | 1 | | | | | | | | | | | | | | |
| FC ratingA+ | 0.05 | -0.07 | 0.02 | -0.58 | 0.32 | 0.35 | -0.07 | -0.08 | 1 | | | | | | | | | | | | | |
| FC ratingA | 0.08 | 0.05 | -0.01 | -0.36 | 0.10 | 0.11 | -0.06 | -0.06 | -0.35 | 1 | | | | | | | | | | | | |
| FC ratingB | -0.03 | 0.00 | 0.00 | 0.23 | -0.14 | -0.16 | 0.01 | 0.02 | -0.33 | -0.42 | 1 | | | | | | | | | | | |
| FC ratingC | -0.07 | 0.02 | -0.01 | 0.48 | -0.21 | -0.23 | 0.08 | 0.08 | -0.22 | -0.27 | -0.26 | 1 | | | | | | | | | | |
| FC ratingD | -0.10 | 0.00 | -0.01 | 0.63 | -0.19 | -0.21 | 0.11 | 0.12 | -0.12 | -0.15 | -0.14 | -0.09 | 1 | | | | | | | | | |
| Origination year 2016 | 0.01 | -0.13 | -0.01 | -0.01 | -0.08 | -0.05 | 0.04 | 0.09 | -0.08 | 0.00 | 0.07 | 0.01 | -0.03 | 1 | | | | | | | | |
| Origination year 2017 | 0.00 | -0.05 | 0.01 | -0.09 | -0.04 | -0.04 | 0.00 | 0.06 | 0.16 | -0.01 | -0.06 | -0.08 | -0.03 | -0.22 | 1 | | | | | | | |
| Origination year 2018 | 0.01 | 0.09 | -0.01 | 0.08 | -0.01 | -0.02 | 0.05 | 0.00 | -0.07 | 0.01 | -0.04 | 0.06 | 0.12 | -0.30 | -0.46 | 1 | | | | | | |
| Origination year 2019 | -0.02 | 0.05 | 0.01 | 0.01 | 0.12 | 0.11 | -0.09 | -0.13 | -0.02 | 0.00 | 0.06 | 0.01 | -0.08 | -0.21 | -0.32 | -0.44 | 1 | | | | | |
| Ln (profit) | 0.34 | -0.05 | -0.03 | -0.08 | 0.04 | 0.06 | 0.00 | 0.00 | 0.05 | 0.03 | -0.03 | -0.04 | -0.05 | 0.04 | 0.02 | -0.03 | -0.02 | 1 | | | | |
| Ln (gross revenue) | 0.60 | -0.09 | -0.10 | -0.15 | 0.15 | 0.14 | -0.01 | -0.01 | 0.09 | 0.06 | -0.05 | -0.08 | -0.08 | 0.09 | 0.02 | -0.06 | -0.02 | 0.40 | 1 | | | |
| Unemployment | -0.01 | 0.02 | 0.01 | 0.00 | -0.01 | 0.01 | 0.01 | 0.01 | 0.01 | -0.02 | 0.01 | 0.00 | 0.00 | 0.04 | 0.01 | 0.00 | -0.05 | -0.01 | 0.00 | 1 | | |
| HPI | 0.07 | -0.05 | 0.01 | -0.01 | 0.05 | 0.01 | 0.01 | 0.00 | -0.01 | 0.00 | 0.00 | 0.00 | 0.00 | -0.01 | 0.00 | 0.02 | 0.05 | 0.07 | -0.04 | 1 | | |
| County business bankruptcy filings per capita | 0.03 | -0.01 | 0.01 | -0.01 | 0.01 | 0.01 | 0.01 | -0.01 | 0.02 | 0.00 | -0.02 | 0.00 | 0.00 | 0.01 | 0.00 | 0.01 | 0.00 | 0.03 | 0.01 | -0.07 | 0.12 | |
Note: Bold figures indicate statistical significance at the 5 percent level.
Footnotes:
1Takes the value of 1 if loan becomes delinquent (60 days past due) as of 12 months after origination and zero otherwise.
2 Takes the value of 1 if loan becomes delinquent (60 days past due) as of 24 months after origination and zero otherwise.
Data Sources: Funding Circle and Haver Analytics.
---
**Table A3: LendingClub SBL — APR by LendingClub Rating and FICO Scores in**
**the U.S.**
This table shows APRs for different ranges of FICO score and LendingClub risk ratings, for
loans that were originated on the LendingClub SBL platform during the period 2015–2019.
| | | LendingClub Risk Rating | | | | | Total FICO | Portfolio share |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | | D | C | B | A | A+ | | |
| FICO score | Low risk | 24.0% | 22.5% | 21.3% | 17.0% | 12.5% | 17.6% | 42.0% |
| FICO score | Medium risk | 27.2% | 26.7% | 24.4% | 18.8% | 13.6% | 22.5% | 46.6% |
| FICO score | High risk | 29.0% | 28.7% | 25.2% | 19.6% | 14.6% | 25.7% | 11.4% |
| Total LC Risk Rating | | 27.2% | 26.2% | 23.8% | 18.2% | 13.0% | 21.4 | |
| Portfolio share | | 6.9% | 19.4% | 26.5% | 26.6% | 20.7% | | |
## Note:
The (discrete). LendingClub risk ratings at origination are mapped into five different risk groups (A+ for
categories R1–R2 or C1–C3, A for R3–R4 or C4–C7, B for R5–R6 or C8–C11, C for R7–R8 or C12–C15, and D for
R9–R10 or C16–C20).
The (continuous) scores of the FICO credit bureau are divided into three segments corresponding to risk level
(low, for scores higher than 739; medium, for scores between 670 and 739; and high, for scores below 670).
Source: Authors’ calculations based on data from LendingClub.
---
## Table A4: Credit Access Estimations
The dependent variable is the ratio of SBL loans originated (by Funding Circle SBL platform)
in zip code *i* in year *t* relative to total SBL loans (in all zip codes) originated in year *t*. The
regressions include a constant and state-level dummies. The sample is based on loan-level
data from Funding Circle SBL platform for the period 2016:Q1–2019:Q2.
| | Funding Circle SBL Lending Ratio1. | | | | |
| --- | --- | --- | --- | --- | --- |
| | (I) | (II) | (III) | (IV) | (V) |
| County unemployment | | 0.00197*** | 0.00199*** | 0.00197*** | 0.00197*** |
| County unemployment | | (0.000421) | (0.000422) | (0.000421) | (0.000421) |
| County HPI(in‘00s) | | 0.000159 | 0.000178 | 0.000157 | 0.000144 |
| County HPI(in‘00s) | | (0.00132) | (0.00132) | (0.00132) | (0.00132) |
| County business bankruptcy filings per capita | | 48.21*** | 47.95*** | 48.24*** | 48.19*** |
| County business bankruptcy filings per capita | | (7.098) | (7.077) | (7.097) | (7.102) |
| Median income(in‘00,000s) | | 0.0118*** | 0.0118*** | 0.0118*** | 0.0118*** |
| Median income(in‘00,000s) | | (0.00131) | (0.00131) | (0.00131) | (0.00131) |
| SBL concentration(in‘000s) | | 0.00110 | 0.00114 | 0.00109 | 0.00109 |
| based on non-CRA report | | (0.000839) | (0.000839) | (0.000839) | (0.000839) |
| Population(%) | 0.289*** | 0.292*** | 0.290*** | 0.292*** | 0.292*** |
| Population(%) | (0.0170) | (0.0180) | (0.0180) | (0.0180) | (0.0180) |
| Dummy, decrease in branches | | | 0.00210** | | |
| Dummy, decrease in branches | | | (0.000963) | | |
| Percentage decrease in branches | | | | 0.00289 | |
| Percentage decrease in branches | | | | (0.00465) | |
| Percent change in branches | | | | | 0.00343 |
| Percent change in branches | | | | | (0.00322) |
| Observations | 10,279 | 9,688 | 9,688 | 9,688 | 9,688 |
| R2 | 0.074 | 0.085 | 0.085 | 0.085 | 0.085 |
Note: ***/**/* denotes results that are significant at the 1%/5%/10% levels, respectively.
Sources: Funding Circle, CRA data, FDIC Summary of Deposits, Call Reports, and Haver Analytics.
---
## Table A5: Default Probability as of 12 months After Origination
The table reports the estimates for a logit regression including a constant, state dummies, and
origination-year dummies. The dependent variable takes the value of 1 if loan is the loan
becomes delinquent (60+DPD) as of 12 months after origination and zero otherwise. The
sample includes loan-level data from Funding Circle SBL Platform for the period 2016‒2019.
| | Delinquent Loan Dummy — 12 Months After Origination | | |
| --- | --- | --- | --- |
| | (I) | (II) | (III) |
| Acquisition score | -0.000012 | -0.000010* | -0.000009* |
| Acquisition score | 0.000008 | 0.000005 | 0.000005 |
| FICO at origination | -0.00603*** | | -0.00342** |
| FICO at origination | (0.00149) | | (0.00151) |
| Vantage Score | -0.00358*** | | 0.000815 |
| Vantage Score | (0.00125) | | (0.00131) |
| FC rating A | | 0.580*** | 0.533*** |
| FC rating A | | (0.203) | (0.206) |
| FC rating B | | 1.210*** | 1.128*** |
| FC rating B | | (0.194) | (0.207) |
| FC rating C | | 1.886*** | 1.781*** |
| FC rating C | | (0.199) | (0.217) |
| FC rating D | | 2.583*** | 2.451*** |
| FC rating D | | (0.218) | (0.241) |
| Ln(profit) | 0.00561 | 0.0287 | 0.0240 |
| Ln(profit) | (0.0407) | (0.0424) | (0.0424) |
| Ln(gross revenue) | -0.281*** | -0.271*** | -0.264*** |
| Ln(gross revenue) | (0.0630) | (0.0634) | (0.0635) |
| Ln(loan amount) | 0.454*** | 0.502*** | 0.519*** |
| Ln(loan amount) | (0.0903) | (0.0942) | (0.0949) |
| Loan maturity in months | -0.00339 | -0.00282 | -0.00313 |
| Loan maturity in months | (0.00364) | (0.00377) | (0.00379) |
| Unemployment | 0.0742 | 0.0546 | 0.0529 |
| Unemployment | (0.0461) | (0.0472) | (0.0471) |
| County HPI | 0.00247 | 0.00168 | 0.00178 |
| County HPI | (0.00151) | (0.00153) | (0.00153) |
| County business bankruptcy filings per capita | -622.6 | -555.4 | -532.0 |
| County business bankruptcy filings per capita | (1,028) | (1,023) | (1,024) |
| Observations | 11,625 | 11,630 | 11,625 |
| Pseudo R² | 0.0808 | 0.121 | 0.123 |
Note: The ***/**/* denotes results that are significant at the 1%, 5%, 10% levels, respectively. Sources:
Funding Circle, CRA data, FDIC Summary of Deposits, Call Reports, and Haver Analytics.
---
**Figure A1: SBL Lending Activity by State**
In U.S. Dollars
Funding Circle (2016–2019)
LendingClub (2015–2019)
The graphs show the ratio of the total amount lent in each state by each of the two fintech firms over the period indicated divided by the
2019 population in each State.
Sources: Funding Circle, LendingClub, U.S. Census Bureau, authors’ calculations.
**Figure A2: LendingClub Credit Portfolio by County**
Average VantageScore by County¹
## Footnotes:
1 Average VantageScore of LendingClub SBL Platform’s small business borrowers in each county.
2Average number of SBL loans originated by LendingClub SBL Platform in each county.
Sources: LendingClub, authors’ calculations.
---
**Figure A3: Correlation Between LendingClub Rating Grades and FICO and**
**VantageScore**
Data Source: LendingClub.
## Figure A4: Predictive Power of FICO, VantageScore, and Funding Circle Risk Grades
Based on estimates for a logit regression including state- and origination year-level dummies.
All credit scores are divided in 5 buckets. The FICO scores are divided into five buckets – Poor
(FICO<580), Fair (FICO between 581 and 670), Good (FICO between 671 and 740), Very Good
(FICO between 741 and 800), and Exceptional (FICO>800). The Vantage Score is also divided
into five buckets: Very Poor (scores <550), Poor (scores between 551 and 650), Fair (scores
between 651 and 700), Good (scores between 701 and 750), and Excellent (scores >750).
ROC Curve — 12-Month Delinquency Rates
ROC Curve — 24-Month Delinquency Rates
Note: The x-axes show the fraction of false positives, whereas the y-axes show the fraction of true positives.
The higher the curve the stronger the performance of the model.
Sources: Funding Circle, What Is a Good Credit Score? – Forbes Advisor.
---
**Figure A5: Funding Circle Risk Bands Are Functionally Similar to Other Risk**
**Scores**
Data Source: Funding Circle.