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2021-01-24 03:52
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2021年1月24日发(作者:announce)
文献信息:

文献标题:
Evaluating credit risk and loan performance in online
Peer-to-Peer (P2P) lending
(点对点
(P2P)
网络借贷的信用风险与贷款绩效评< br>估)

国外作者:
Riza Emekter, Yanbin Tu, Benjamas Jirasakuldech, Min Lu
文献出处:

Applied Economics

, 2015, 47(1):54-70
字数统计:
英文
3063
单词,15818
字符;中文
5110
汉字



外文文献:


Evaluating credit risk and loan performance in online
Peer-to-Peer (P2P) lending
Abstract


Online Peer-to- Peer (P2P) lending has emerged recently. This micro
loan
market
could
offer
certain
benefits
to
both
borrowers
and
lenders.
Using
data
from the Lending Club, which is one of the popular online P2P lending houses, this
article explores the P2P loan characteristics, evaluates their credit risk and measures
loan performances. We
find that credit grade, debt- to-income ratio, FICO score and
revolving
line
utilization
play
an
important
role
in
loan
defaults.
Loans
with
lower
credit grade and longer duration are associated with high mortality rate. The result is
consistent with the Cox Proportional Hazard test which suggests that the hazard rate
or the likelihood
of the loan default increases
with the credit risk of the borrowers.
Finally, we
find that higher interest rates charged on the highrisk borrowers are not
enough to
compensate for higher probability of the loan default. The
Lending Club
must
find
ways
to
attract
high
FICO
score
and
high- income
borrowers
in
order
to
sustain their businesses.
Key words:
Peer-to-Peer lending; credit grade; FICO score; default risk

uction
With
the
advent
of
Web
2.0,
it
has
become
easy
to
create
online
markets
and
virtual communities with convenient accessibility and strong collaboration.
One
of
the
emerging
Web
2.0
applications
is
the
online
Peer-to-Peer
(P2P)
lending marketplaces, where both lenders and borrowers can virtually meet for loan
transactions. Such marketplaces provide a platform service of introducing borrowers
to
lenders,
which
can
offer
some
advantages
for
both
borrowers
and
lenders.
Borrowers can get micro loans directly from lenders, and might pay lower rates than
commercial
credit
alternatives.
On
the
other
hand,
lenders
can
earn
higher
rates
of
return compared to any other type of lending such as corporate bonds, bank deposits
or certificate of deposits. One of the problems in online P2P lending is
information
asymmetry between the borrower and the lender. That is, the lender does not know the
borrower's
credibility
as
well
as
the
borrower
does.
Such
information
asymmetry
might result in adverse selection (Akerlof, 1970) and moral hazard (Stiglitz and Weiss,
1981). Theoretically, some of these problems can be alleviated by regular monitoring,
but this approach poses a challenge in the online environment because the borrowers
and the buyers do not physically meet. Fostering and enhancing the lender's trust in
the borrower can also be implemented to mitigate adverse selection and moral hazard
problems. In the traditional bank-lending markets, banks can use collateral, certified
accounts, regular reporting, and even presence of the board of directors to enhance the
trust
in
the
borrower.
However,
such
mechanisms
are
difficult
to
implement
in
the
online environment which will incur a significant transaction cost.
To
reduce
lending
risks
associated
with
information
asymmetry,
current
online
P2P lending has the following arrangements. First, the Lending Club screens out any
potential high- risk borrowers based on the FICO score. The minimum FICO score to
be
able
to
participate
is
640.
Second,
the
typical
size
of
the
loans
produced
in
this
market is small, which is under $$35 000 at the Lending Club. Therefore, these loans
are essentially microloans which pose a relatively small loss in case of default. Third,
the
market
maker
offers
matchmaking
systems
which
can
be
used
to
generate
portfolio recommendations and minimize lending risks. Fourth, if a borrower fails to
pay,
the
market
maker
will
report
the
case
to
a
credit
agency
and
hire
a
collection
agency
to
collect
the
funds
on
behalf
of
the
lender.
Although
there
are
certain
structures
imposed
in
the
online
P2P
that
help
to
minimize
the
risk,
this
form
of
lending
is
inherently
associated
with
greater
amount
of
risk
compared
to
the
traditional lending.
The purpose of this article is to evaluate the credit risk of borrowers from one of
the largest P2P platforms in the United States provided by the Lending Club, which
help lenders to make more informed decisions about the risk and return efficiency of
loans
based
on
the
borrowers'
grade.
There
are
two
related
research
questions
this
article
will
address:
(1)
What
are
some
of
the
borrowers'
characteristics
that
help
determine
the
default
risk?
and
(2)
Is
the
higher
return
generated
from
the
riskier
borrower large enough to compensate for the incremental risk? Lenders can allocate
their investments more efficiently if they know what characteristics of the borrower
affect the default risk. Each borrower is classified by credit grade with corresponding
borrowing
rate
assigned
by
the
Lending
Club.
To
make
an
efficient
allocation,
a
lender should know whether the higher interest rates set for high-risk borrowers are
sufficient to compensate the lenders for the higher probabilities of a potential loss.
Our findings suggest that borrowers with high FICO score, high credit grade, low
revolving line utilization and low debt-to-income ratio are associated with low default
risk. This finding is consistent with the studies by Duarte et al. (2012) who report that
borrowers
with
a
trustworthy
characteristic
will
have
better
credit
scores
but
low
probability of default. This result also suggests that besides the loan applicants' social
ties and friendship as reported by Freedman and Jin (2014) and Lin et al. (2013), the
four factors discussed above are also important in explaining the default risk. When
comparing with US national borrowers, the results show that the Lending Club should
continue to
screen out
the borrowers with
lower FICO score
and attract
the highest
FICO score borrowers in order to significantly reduce the default risk. In relating the
risk to the return, it shows that higher interest rate charged for the riskier borrower is
not
significant
enough
to
justify
the
higher
default
probability.
Our
finding
here
is
consistent
with
the
study
by
Berkovich
(2011)
who
reports
that
high
quality
loans
offer excess return.

ture Review
Three
main
streams
of
research
have
emerged
in
response
to
the
growing
popularity of P2P lending. The first stream of research examines the reasons for the
emergence
of
online
P2P
lending.
The
second
stream
of
research
focuses
on
determining
the
factors
that
explain
the
funding
success
and
default
risk.
The
last
stream of research investigates the performance of online P2P loan for a given level of
the risk.
Peer group lending has been emerging in local communities and has attracted the
research in this area. Conlin (1999) develops a model to explain the existence of peer
group micro-lending programmes in the United States and Canada. He finds that peer
groups enable fixed
costs
to
be
imposed
on the entrepreneurs while minimizing the
programme's overhead
costs. Ashta and Assadi
(2008) investigate
whether Web 2.0
techniques are integrated to support the advanced social interactions and associations
with
lower
costs
for
P2P
lending.
Hulme
and
Wright
(2006)
study
a
case
of
online
P2P lending house, Zopa, in the United Kingdom. They suggest that the emergence of
online P2P lending is a direct response to social trends and a demand for new forms of
relationship in financial sector under the new information age.
There
is
extant
literature
that
identifies
the
factors
determining
the
funding
success
and
default
risk.
Using
the
Canadian
micro-credit
data,
Gomez
and
Santor
(2003) find that group lending offers lower default rates than conventional individual
lending does. Study by Iyer et al. (2009) shows that lenders can evaluate one third of
credit risk using both hard and soft data about the borrower. Lin et al. (2013) analyse
the role of social connections in evaluating credit risk and discover that strong social
networking relationship is an important factor that determines the borrowing success
and lower default risk. Lin et al. (2013) further report that applicants' friendship could
increase
the
probability
of
successful
funding,
lower
interest
rates
on
funded
loans,
and these borrowers are associated with
lower
ex post default rates
at Prosper. The
importance of social ties in determining loans funded is also examined by Freedman
and
Jin
(2014).
The
result
shows
that
borrowers
with
social
ties
are
more
likely
to
have
their
loans
funded
and
receive
lower
interest
rates.
However,
they
also
find
evidence of risks to lenders regarding borrower participation in social networks.
Several
other
studies
examine
whether
certain
borrowers'
characteristics
and
personal
information
determine
the
success
of
loan
funding
and
default
risk.
Herzenstein
et
al.
(2008)
show
that
borrowers'
financial
strength,
their
listing
and
publicizing
efforts,
and
demographic
attributes
affect
likelihood
of
funding
success.
Study
by
Duarte
et
al.
(2012)
further
argues
that
borrowers
who
appear
more
trustworthy
have
better
credit
score
with
higher
probabilities
of
having
their
loans
funded and default less often. Larrimore et al. (2011) demonstrate that borrowers who
use
extended
narratives,
concrete
descriptions
and
quantitative
words
have
positive
impact
on
funding
success.
However,
humanizing
personal
details
or
loan
justifi
cations
have
negative
in?uences

on
funding
success.
Qiu
et
al.
(2012)
further
reveal
that
in
addition
to
personal
information
and
social
capital,
other
variables,
including
loan
amount,
acceptable
maximum
interest
rate
and
loan
period
set
by
borrowers, significantly in
?
uence the funding success or failure.
Galak et al. (2011) further show that lenders tend to favour individual over group
borrowers
and
borrowers
who
are
socially
proximate
to
themselves.
They
also
find
that lenders prefer the borrowers who
are more
like themselves in
terms of
gender,
occupation and first
name initial.
More interestingly, Gonzalez and
Loureiro (2014)
have similar findings: (1) when perceived age represents competence, attractiveness
has no effect on loan success; (2) when lenders and borrowers are of the same gender,
attractiveness might lead to a loan failure (i.e., the

beauty is beastly' effect) and (3)
loan
success
is
sensitive
to
the
relative
age
and
attractiveness
of
lenders
and
borrowers. Herzenstein et al. (2011) find that herding in the loan auction is positively
related to its subsequent performance, that is whether borrowers pay the money back
on time.


In
this
section,
the
loan
applicants'
data
is
first
described,
followed
by
loan
distribution based on loan purposes, credit grade and loan status and it ends with the
detailed
descriptive
statistics
of
the
loan
applicants.
This
study
uses
61
451
loan
applications
in
the
Lending
Club
from
May
2007
to
June
2012
obtained
from
.
Over
the
study
period,
the
Lending
Club
lent
about
$$713
million to borrowers. To address the borrowers' behaviour in online P2P lending, we
first
examine
the
main
reasons
for
borrowing
money
from
others.
Table
1
lists
the
borrowers'
self-claimed
reasons
summarized
in
the
Lending
Club.
Almost
70%
of
loan requested are related to debt consolidation or credit card debts with a total loan
amount requested of approximately $$387 million and $$108 million, respectively. The
number of loan applications for education, renewable energy and vacation contribute
less than 1% of total loans with the total loan requested ranging from 1 to 3 million.
The borrowers state that their preferences to borrow from the Lending Club are lower
borrowing rate and inability to borrow enough money from credit cards. The second
purpose for borrowing is to pay home mortgage or to re-model home.
Table 1. Loan distributions by loan purpose (May 2007

June 2012)

Notes
: The data is obtained from 61 451 loan applicants in the Lending Club, , from
May 2007 to June 2012.
The loan-seeking persons are asked to provide the reasons for requesting loans.
The
Lending
Club
uses
the
borrower's
FICO
credit
scores
along
with
other
information to assign a loan credit grade ranging from A1 to G5 in descending credit
ranks to each loan. The detailed procedure is as follows: after assigning a base score
based
on
FICO
ratings,
the
Lending
Club
makes
some
adjustments
depending
on
requested loan amount, number of recent credit inquiries, credit history length, total
open credit account,
currently open credit accounts
and revolving line utilization to
determine the final grade, which in turn determines the interest rate on the loan.
Table 2 reports the loan distribution by credit grade. The majority of borrowing
requests
have
grades
between
A1
and
E5.
The
Highest
loan
amounts
requested
are
from borrowers with

B' credit grade, which contribute 29.56% of total amount of
loans requested. The total number of applicants for this

B' credit grade group is 18
707,
which
represents
total
loans
of
approximately
$$210
million.
The
lowest
loan
amounts
requested
are
from
borrowers
with
the
lowest

G'
credit
grade
which
accounts for 1.53% of total loans. There are only 608 loan applicants for this lowest
credit rating

G' group and it represents approximately $$11 million in total loan value.
According to the Lending Club's policy, a loan credit grade is used to determine the
interest
rate
and
the
maximum
amount
of
money
that
a
borrower
can
request.
The
higher
the
loan
grade,
the
lower
the
interest
rate.
A
borrowing
request
with
a
low
grade renders a higher interest rate as a compensation for a high risk held by lenders.
Table 2. Loans distribution by credit grades (May 2007

June 2012)

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