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Online Peer-To-Peer Lending: A Review of the Literature
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Online Peer-To-Peer Lending: A Review of the Literature
Shabeen Afsar Basha, Mohammed M. Elgammal, Bana M. Abuzayed
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https://doi.org/10.1016/j.elerap.2021.101069
ELERAP 101069
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Please cite this article as: S. Afsar Basha, M.M. Elgammal, B.M. Abuzayed, Online Peer-To-Peer Lending: A
Review of the Literature, Electronic Commerce Research and Applications (2021), doi: https://doi.org/10.1016/
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Online Peer-To-Peer Lending: A Review of the Literature
Shabeen Afsar Basha1
Mohammed M. Elgammal1, 2
Bana M. Abuzayed1
Abstract:
This study reviews the literature of online peer-to-peer (P2P) lending from 2008 until 2020 as an
emergent but fast spreading phenomenon in the context of digital finance. Previous literature is
geographically skewed towards United States and China with focus on determinants of funding
success and loan attributes. Recent studies shift from using logit and survival analysis methods to
examine funding success and default predictions, towards applying artificial intelligence. There
is a controversial debate regarding adopting a self-regulatory approach versus stricter financial
institutions-based regulations with a few studies suggesting a hybrid approach. We suggest
several avenues for future research, such as examining the determinants and performance of P2P
lending platforms in emerging and developing markets; regulatory differences, the effects of
behavioral characteristics such as cultural impact, language, information technology literacy, and
the innovation quotient on P2P funding attributes; and the relationship between P2P lending and
traditional finance channels.
JEL classification: G21, G23, G29
Keywords: Fintech, funding innovation, internet finance, peer-to-peer lending, crowding finance,
alternative finance, microfinance
Acknowledgment: Authors would like to thank participants in APMAA 15th annual meeting
(2019) in Doha for their valuable comments and advices. We would also like to thank Professor
Christopher C. Yang, Ph.D., Editor‐in‐Chief, Electronic Commerce Research and Applications
and anonymous associate editor and reviewers for their valuable constructive feedback, which
gave us the opportunity to take further the quality of the paper to more rigid and robust level of
systematic review. All remained errors are the responsibility of the authors.
Online Peer-To-Peer Lending a Review of the Literature
Abstract:
This study reviews the literature of online peer-to-peer (P2P) lending from 2008 until 2020 as an
emergent but fast spreading phenomenon in the context of digital finance. Previous literature is
geographically skewed towards United States and China with focus on determinants of funding
College of Business and Economics, Qatar University, Doha, Qatar
Correspondence author: Finance and Economics Department, College of Business and Economics
Qatar University, Qatar, Tel: (+974) 4403 – 5098. Email: m.elgammal@qu.edu.qa. Dr. Elgammal in a sabbatical
leave from Menoufia University, Egypt.
1
2
success and loan attributes. Recent studies shift from using logit and survival analysis methods,
to examine funding success and default predictions, towards applying artificial intelligence.
There is a debate regarding adopting a self-regulatory approach versus stricter financial
institutions-based regulations with a few studies suggesting a hybrid approach. We suggest
several avenues for future research, such as examining the determinants and performance of P2P
lending platforms in emerging and developing markets; regulatory differences, the effects of
behavioral characteristics such as cultural impact, language, information technology literacy, and
the innovation quotient on P2P funding attributes; and the relationship between P2P lending and
traditional finance channels.
JEL classification: G21, G23, G29
Keywords: Fintech, funding innovation, internet finance, peer-to-peer lending, crowding finance,
alternative finance, microfinance
Acknowledgment: Authors would like to thank participants in APMAA 15th annual meeting
(2019) in Doha for their valuable comments and advices. We would also like to thank Professor
Christopher C. Yang, Ph.D., Editor‐in‐Chief, Electronic Commerce Research and Applications
and anonymous associate editor and reviewers for their valuable constructive feedback, which
gave us the opportunity to take further the quality of the paper to more rigid and robust level of
systematic review. All remained errors are the responsibility of the authors.
Online Peer-To-Peer Lending a Review of the Literature
1. Introduction
Online peer-to-peer (P2P) lending platforms provide individuals and small businesses
alternative credit options. These platforms possess many competitive advantages that have led to
substantial growth in lending volume and in the number of such platforms. P2P lending offers
better rates of return to lenders and greater access to credit at affordable costs to borrowers who
may have limited access to banks; therefore, this type of lending can outperform the
conventional lending in the retail sector (Milne & Parboteeah, 2016; Wardrop et al., 2016). P2P
lending also offers a feasible alternative to traditional lending organizations, which are
comparatively disadvantaged in terms of technological expertise and rigid financial systems.
Financial technology (Fintech) innovations can lower borrowing costs by using fully
automated algorithms to price and underwrite loans via appropriate systems (Balyuk, 2018;
Philippon, 2016). Online P2P lending can also eliminate the inefficiencies and overhead
borrowing costs by removing barriers to borrowers who have limited access to credit due to low
creditworthiness (Foo et al., 2007). A stream of research has considered the potential of P2P
lending in replacing traditional funding (e.g., Liu, H., et al., 2020), confirming that online P2P
lending can complement traditional lending channels by acquiring high quality, low-risk
customers who are less served by traditional channels. Although the ease of obtaining loans can
draw small business borrowers towards online P2P lending, the high interest rates associated
with this form of lending may lead small business back to traditional finance options. Access to
credit through P2P lending channels, albeit at occasionally higher interest rates, largely augments
small businesses’ working capital and growth needs (Mach et al., 2014; Segal, 2015; Lerong,
2018).
The volume of traditional lending originations (i.e., new loans) has declined since 2007 in the
U.S., especially for small business loans due to the global financial crisis and heavy regulations
on financial institutions. Yet P2P issuances have increased in volume and number, indicating
their growing importance in small business lending (Segel, 2015). Similar annual global trends
appeared in comprehensive geographical reports issued by the Center for Alternative Finance of
Cambridge University for the years 2013–2020. Ziegler et al. (2018) found that 17% of
alternative market share was held by P2P lending in Europe. Wardrop et al. (2017) claimed that
business lending via alternative online channels comprised 1.26% of traditional lending in the
U.S. in 2015, up from 0.24% in 2013 and it had grown exponentially in the rest of the Americas
from 2014-2016.
One of the advantages of P2P platforms is the availability of huge credit information.
Lenders can capitalize on available soft and hard financial information to assess borrowers’
creditworthiness, which affects lending rates (Iyer et al., 2016; Wang, Zhang, Zhao, & Wang,
2019). Several empirical studies have examined the P2P lending phenomenon using hard credit
information such as borrowers’ economic status (e.g., credit grades or rating), credit inquiries,
and bankruptcy records (Bachmann et al., 2011; Klafft, 2008; Lin, 2013; Pope & Sydnor, 2011;
Prystav, 2017). Soft credit information such as identity claims, loan explanations, physical
appearance, group membership, friendships, and other relationship networks are found to be
determinants of granting loans and corresponding interest rates.
Regulatory control over these platforms varies between countries. Online P2P is
characterized to be self-regulation in the UK, regulation by the Securities and Exchange
Commission in the U.S. (Wardrop et al., 2016), and increasing government regulation given the
growth and irregularities of such platforms in many other countries. P2P lending platforms have
flourished with exceptional growth, specifically in China during the last decade. Barriers to
credit access have likely spurred this expansion for individuals, SMEs, and avenues for
investment to middle-income groups without the generally high charges and fees incurred from
other investments.
The spread of online P2P lending creates a need to review the existing research relevant to
this phenomenon. Conducting a literature review helps future research to build on the existing
studies in this area and to develop a further research agenda. To the best of our knowledge, only
one literature review has examined the online P2P lending. Bachmann et al., (2011) reviewed a
selected group of published until 2010, focusing on the determinants and effects of demographic
characteristics on P2P loans. Later, Zhao et al. (2017) reviews the P2P platforms, advances and
prospects, discusses the different business models, functioning of the platforms, and some key
literature including both charitable and non-charitable platforms. Our work contributes to the
literature by systematically reviewing 198 online P2P research papers published in 2008-2020
and providing in-depth analysis of the theoretical background, methodological approaches,
geographical focus, and journal classification of relevant articles. Furthermore, a literature
review on online P2P lending is needed given that this type of lending constitutes a major part of
alternative finance volumes as reported by global annual surveys from the Cambridge Center for
Alternative Finance (CCAF). Moreover, regulatory changes and uncertainties have sparked
increased interest in this topic as evidenced by the rising number of published papers. This
timely review of the P2P lending literature can guide stakeholders, policymakers, and academics
in identifying determinants of this alternative form of lending in addition to regulatory
challenges and gaps to be addressed in future research. In doing so, we reveal key themes thrown
up by a quantitative and a qualitative review of the published literature about the online P2P and
put forward an issue-based research agenda that is relevant for newly entering institutions and
stakeholders in this market.
Our findings show that the P2P lending literature is geographically skewed towards the U.S.
and China. Financial demographic and social determinants are the foci of empirical determinant
research. The availability of P2P lending platform data may also be a reason for the skew, as
loan information has been regulated to be available in the public domain in the U.S. Future
scholars could gather data from P2P lending platforms in addition to examining new theories and
platforms globally if they have access to the data.. The CCAF, which collects annual selfreported data on alternative finance volumes (including P2P lending) globally, has undertaken
noteworthy efforts in this regard.
Our review found that the majority of the publications examined financial determinants of
funding success, loan amounts, interest rate and default, but only 8% examine macroeconomic
factors affecting P2P. Regulatory discussions and working papers have provided emerging
theories, and online P2P lending policy guidance appears in more than 20% of the reviewed
literature. While 33% of reviewed papers involved multiple regression models with binary
dependent variables, 40% of the publications propose some new models or apply
multidisciplinary methodologies to fulfill their study objectives as of stage hazard models (9%)
and experimental methodologies (13%). Recent studies demonstrated a methodological shift in
examining determinants and predicted default by adopting artificial intelligence techniques.
In general, financial determinants such as loan amounts, duration, and interest rates have
been highlighted as strong predictors of funding success, whereas demographic determinants
such as gender, race, and education level tend to be inconclusive and have a geographical varied
impact on P2P lending. Social relationships and borrowers’ efforts in offering persuasive
justification for their loan purposes also substantially affect the likelihood of raising necessary
funds. Our results uncover research gaps in the literature that can work as suggested research
agenda. Still studies that explore macroeconomic and country-level determinants of P2P lending
are scarce. Furthermore, reputation status, gender and wider research geographical coverage are
not conclusive and requires more attention from future researchers.
The rest of this paper is structured as follows. Section 2 outlines our research motivation,
objectives, and questions. Section 3 details the research data and methodology and classification
of selected literature, and Section 4 presents the analysis of our classification and findings.
Discussion and conclusions are presented in Section 5.
2. Research Motivation, Objectives and Questions:
Early articles on P2P lending focused on the financial, demographic, and social determinants
of loan amounts and funding success in online P2P lending as a form of alternative finance. The
initial review by Bachmann et al. (2011) provided insight into these aspects of online P2P
lending; however, the phenomenon has evolved since their review. Recent research has delved
deeper into the concepts and determinants of online P2P lending, including novel approaches to
default prediction, application of machine learning techniques, and varied discussions around the
regulatory challenges and roles of online P2P lending as an alternative to traditional lending
channels. This study considers the aforementioned factors to delineate the evolution of this topic
in the Fintech literature and provides an analysis of research lacunae to guide future studies. This
surge in the Fintech literature, specifically in terms of online P2P lending, requires a review to
understand its status and gaps. The current review classifies the literature by theory,
methodology, and empirical findings with respect to determinants and diverse regulatory
settings. We have taken stock of current online P2P lending research (198 published articles) to
provide insights based on the following classification criteria: theories guiding studies of online
P2P lending; methodologies adopted in such research; platform domains addressed in P2P
lending studies; publication outlets; and chronology. Our study addresses two research questions:
1. How has the literature on P2P evolved over time? To attempt to answer this question, we
identified the most influential studies; the basic references influenced these studies, the main
outlets /Journal Classification/Rating/ Geographical Domain for the studies and show how the
number of these publications evolved over time.
2. What are the main P2P subjects and issues that has been researched? To analyze this
question, we have investigated and categorized the literature based on Thematic Classification,
and Methodological Classification.
3. Data and Methodology
Following Milian et al. (2019) among others , we searched the Scopus and Web of Science
(WOS) databases for English language journal articles with key words “online peer to peer
lending” , “online P2P lending” or “online peer-to-peer lending” in the subject areas of
Computer sciences ("COMP"), Business ("BUSI”), Economics ("ECON"), Decision sciences
(“DECI"), Social sciences ("SOCI”), Mathematics ("MATH") or Multidisciplinary (“MULT").
Only articles classified as ‘journal’ or ‘review’ are included in the Scopus and Web of science
Figure 1-Publications selection process
search. As online P2P is a relatively new
research area, we included more articles from
secondary sources that are not Scopus or Web
of science.
Articles in the process of publication, as
identified in the Social Science Research
Network (SSRN) and relevant published
reports, were also included to provide a more
comprehensive overview of the emerging concept of online P2P lending. Online P2P lending
articles have appeared in journals from different disciplines including business, economics,
computers, decision sciences, mathematics, social sciences and multidisciplinary sources. These
disciplines examine different attributes of the online P2P such as financial, social and
demographic
determinants,
information
technology
architecture,
decision-making
and
mathematics disciplines proposing and testing models for credit risk evaluation, probability of
default, funding success and determinants of funding. The publications selection process has
been illustrated in figure 1 and in Table 1. Considering both Nvivo software and manual
classification we end up by 198 papers (113 indexed in both WOS and Scopus, 33 indexed only
in Scopus, 12 indexed only in WOS, and 40 articles from secondary sources).
Table 1-Publication search and finalization of articles for review
Scopus
Web of Science
Search String
(TITLE-ABS-KEY(( ( online AND peer
TOPIC: ((( ( online AND peer AND to AND peer AND lending
AND to AND peer AND lending ) OR (
) OR ( online AND p2p AND lending ) OR ( online AND peer-
online AND p2p AND lending ) OR (
to-peer AND lending ) ))) Refined by: DOCUMENT TYPES: (
online AND peer-to-peer AND lending ) ))
ARTICLE OR REVIEW ) AND [excluding] PUBLICATION
AND DOCTYPE(ar OR re) AND
YEARS: ( 2021 ) AND WEB OF SCIENCE CATEGORIES: (
PUBYEAR < 2021 AND ( LIMIT-TO (
BUSINESS OR SOCIOLOGY OR MANAGEMENT OR
SUBJAREA,"COMP" ) OR LIMIT-TO (
AUTOMATION CONTROL SYSTEMS OR COMPUTER
SUBJAREA,"BUSI" ) OR LIMIT-TO (
SCIENCE INFORMATION SYSTEMS OR ECONOMICS OR
SUBJAREA,"ECON" ) OR LIMIT-TO (
BUSINESS FINANCE OR OPERATIONS RESEARCH
SUBJAREA,"DECI" ) OR LIMIT-TO (
MANAGEMENT SCIENCE OR INFORMATION SCIENCE
SUBJAREA,"SOCI" ) OR LIMIT-TO (
LIBRARY SCIENCE OR MULTIDISCIPLINARY SCIENCES OR
SUBJAREA,"MATH" ) OR LIMIT-TO (
MATHEMATICS OR COMPUTER SCIENCE
SUBJAREA,"MULT" ) ) AND ( LIMIT-
INTERDISCIPLINARY APPLICATIONS OR MATHEMATICS
TO ( LANGUAGE,"English" ) ) AND (
INTERDISCIPLINARY APPLICATIONS OR COMPUTER
LIMIT-TO ( SRCTYPE,"j" ) ) )
SCIENCE ARTIFICIAL INTELLIGENCE OR SOCIAL SCIENCES
INTERDISCIPLINARY OR INTERNATIONAL RELATIONS OR
LAW OR SOCIAL SCIENCES MATHEMATICAL METHODS OR
COMPUTER SCIENCE CYBERNETICS OR COMPUTER
SCIENCE HARDWARE ARCHITECTURE OR COMPUTER
SCIENCE SOFTWARE ENGINEERING OR COMPUTER
SCIENCE THEORY METHODS ) AND LANGUAGES: (
ENGLISH )
Number of articles
176
155
Discarded (not relevant, or
27 +3
28 +1+1 retracted
Articles included in review
33 unique and 113 common with WOS
12 unique and 113 common with Scopus
Articles from secondary
40
duplicate and not available)
sources
Total articles
198 (113+33+12+40)
In order to address the two objectives of this study: describing the current research
landscape of the P2P and developing a future research agenda, we followed a two-stage
approach. This approach combined quantitative and qualitative content analysis. In the first
stage, the quantitative review sought to describe and summarize the current research productivity
based on key descriptive categories in accordance with a predesigned coding frame.
In the second stage, the study qualitatively reviewed the selected studies, In particular
thematic analysis are used to examine what research themes were studied and also “what's next”
for the future studies by proposing a research agenda concerning unanswered research questions
for P2P. Thematic analysis is a qualitative technique useful for understanding trends and patterns
in the data. This stage involved reading the gathered articles and drawing from the multistep
process of a thematic analysis (Braun and Clarke, 2006; Elbanna et al. 2020). The thematic
qualitative analysis was conducted in two steps. Step 1 involved categorizing the studies by topic
or issue, using an inductive approach to arrive at trends in the data. Step 2 involved a more
analytical attempt to categorize the studies by their main research questions in order to better
understand the current research dialogue and future research agenda. In this section, we
summarize the literature under each theme by discussing the research questions raised and the
findings from these studies and then propose avenues for future research.
To better contextualize the development of the online P2P lending literature, we
classified articles by multiple dimensions for both content analysis (thematic classification by
theories and determinants, methodological classification) and bibliometric analysis (chronology,
journal or source, and geographical context) to identify gaps and formulate conclusions for future
research in this area. Similar to Nazario et al. (2017) and Nigam et al. (2018), we divided our
sample into five broad categories and related subcategories.
3.1. Contents Analysis
The analysis consists of thematic and methodological classifications:
3.1.1 Thematic Classification
Thematic classification is used to categorize articles by their guiding theories and topics of study:
a. (1A) - Theories including economic theory, information asymmetry, social
capital, and other theories common in the P2P lending literature.
b. (1B) - Financial determinants of P2P lending such as funding success,
determinants of interest rate, duration, credit risk.
c.
(1C) -Demographic determinants such as age, marital status, race and other
demographic attributes of P2P lending.
d. (1D) - Social determinants such as belonging to a group, friendships, herding
impacts on P2P lending.
e. (1E) - Macroeconomic determinants of P2P lending.
3.1.2 Methodological Classification:
Classification by methodology is a good indicator for the state of art methodologies in
online P2P literature. Online P2P studies mainly focus on determinants of funding, success of
funding, probability of default and credit risk. Therefore, many papers, especially earlier studies,
adopt multiple regressions, survival models and ordinal (mostly binary) dependent variable
models. Other common methodologies applied in the P2P lending studies are experimental
research, proposing mathematical models to predict default and credit risk, conceptual models,
policy, regulatory discussions and theoretical discussions. We sub classify our sample by
grouping studies into the following applied methodologies:
a. Theoretical (3A) - Development of new models; machine learning, artificial intelligence,
principal component analysis, factor analysis etc.
b. Empirical (3B) - Ordinal dependent variable models (logit and probit); tobit models
c. Discussion (3C) - Policy reviews and Regulatory discussions
d. Empirical (3D) - Two-stage models for effectiveness of default probability estimation
(Heckman, Cox proportional hazard, hidden Markov)
e. Empirical (3E) - Experimental studies
f. Other (3F) - Multiple regressions, Levene’s test for comparison of variances, descriptive
analysis, generalized least squares.
3.2. Bibliometric Analysis
This analysis is based on discussion the data of Chronological, journal, and Geographical
domain classifications:
3.2.1 Chronological Classification
Chronological classification indicates the evolution of the online P2P literature from the launch
of the first platform (Zopa) at United Kingdom since 2005. The global financial crisis in 2008
generated interest in alternative finance; and motivates P2P lending publications as an alternative
financing. However, Policy discussions and regulation changes during 2016-2017 reinvented the
wheel and attracted more researchers to reexamine the growth of P2P lending subsequent to the
regulatory changes in countries such as the UK, U.S, China, and Hong Kong that contributed to
the major share of global online P2P lending (Huang, 2018).
3.2.2 Journal Classification
Publication sources may indicate the positioning of P2P lending in various publishing
areas as well as sources and avenues for future publication. Various journals may promote
paradigms largely supported by the journals and reflect acceptance of ideas and methods
appearing in published articles. As the publications reviewed are from multiple disciplines, we
choose SCImago journal ranking (SJR) that provides a quality metric for all journals indexed by
Scopus. SJR measures the citations by serial where citation weights are based on the subject field
and prestige of the citing serial and limits self-citations (SCImago (n.d), Roldan-Valadez
(2019)). In addition, we classify our sample by the Source normalized Impact per paper (SNIP),
that measures the influence of journals considering interdisciplinary citations and percent cited
metric that reports the percentage of articles in the journal with at least one citation. The current
study reviews articles from multidisciplinary sources and cite score metrics such as SJR, SNIP
(Roldan-Valadez (2019)) and percent cited metric are appropriate for examining the scientific
quality of documents. We classify the Academic Journal Guide (AJG) for articles from AJG
sources. Following Nazario (2017), we analyze productivity of authors that have published in
Scopus indexed and WOS journals. We included non-peer-reviewed articles from the secondary
sources due to the relatively nascent status of P2P lending research.
3.2.3 Geographical Domain
A unique characteristic of the empirical literature on online P2P lending is the reliance
on large datasets from a single or a few platforms to test hypotheses related to determinants and
models explaining the funding success, interest rates, and default probabilities of P2P lending.
Platforms such as Lending Club and Prosper (U.S.) made loan-wise data available globally,
therefore, many early studies examined these platforms. The studies in our sample mostly
involved data from platforms such as Lending Club and Prosper (U.S.) and PPdai and Renrendai
(China) with a few exceptions in other countries. One reason for this concentration is the public
availability of P2P data for U.S. platforms. Platform data from China do not appear to be in the
public domain at this time. We classified articles by country of study to provide valuable insights
into focal areas and avenues for future research.
4. Analysis
Table 2 presents a classification by themes, methodologies and sources of the selected 198
publications in our sample. Most papers involved empirical investigations of the key
determinants of P2P lending, discussions of regulations, and macroeconomic explanations for
growth and other P2P lending attributes. The following subsections offer an overview of our
chosen articles based on the classification criteria detailed in Sec. 3.
Table 2-Percentage of studies by Thematic, Methodological and source classification
Theme
%
1A
1B
1C
1D
1E
37%
39%
12%
28%
8%
Methodology %
3A
3B
3C
3D
3E
3F
40%
33%
14%
9%
13%
24%
SJR-SCImago
%
Journal Rank
0-.999
1-1.999
2-2.999
3-3.999
4-4.999
5-5.999
8-8.999
12-12.999
65%
19%
7%
3%
1%
3%
1%
1%
SNIP
%
0-.999
1-1.999
2-2.999
3-3.999
4-4.999
5-5.999
34%
41%
16%
7%
1%
1%
Percent cited %
0-10
11-20
21-30
31-40
41-50
51-60
61-70
71-80
81-90
91-100
2%
2%
3%
8%
7%
17%
17%
29%
12%
2%
4.1. Thematic Classification
Theoretically, frameworks guiding phenomena related to online P2P lending are based on
existing economic, financial, and management theories. Error! Reference source not found.
Figure 2-Tree map of number of publications in each theme
provides the percentage of studies by
classification and Figure 2 provides the
number of studies by thematic classification.
Financial determinants of P2P have been
Figure 2 provides number of articles next to theme. The number of
articles do not add-up to 198, as some publications examine more than
one theme. 1A denote theories, 1B is financial Determinants of P2P, 1C is
demographic determinants, 1D is social determinants, and 1E is
macroeconomic determinants of P2P lending.
examined
by
77
studies
(39%),
73
publications that is, 37% of the publications
discuss and empirically examine theories, and
28% and 12% from the studies investigate social and demographic P2P determinants,
respectively. Only 8% from the papers have investigated macroeconomic determinants of P2P
lending.
Error! Reference source not found.3 and Figure 3 show the number of publications by
themes and methodology. Analysis of methodologies examining the theme 1A reveal that 36
studies apply methodological classification 3A. These include proposing new models (e.g.,
Cheng, H. and Guo, R., 2020; Zhang, H., et al., 2019), employing artificial intelligence, machine
learning techniques such as decision tree models for identifying good loans (Kumar et al., 2016),
risk assessment models such as signal detection models (Iyer et al., 2016), simultaneous
modelling (Tan., F et al., 2019), and game theoretical models to examine funding success (Wei,
Z., & Lin, M, 2017). 13 studies use binary models, and 2 studies employ Heckman selection
model often as robustness analysis to compare results with new theoretical models proposed (see
for example, Caldieraro et al., 2018 ; Wei, Z., & Lin, M, 2017). Classification of themes by
context exhibited in Figure 4 and Table 4 highlight gaps in examine themes in different context
which can work as a further research agenda.
Figure 3-Themes by Methodology
Table 2-Themes by Methodology
Theme
/Methodology
3A
3B
3C
3D
3E
3F
1A
1B
1C
1D
1E
36
13
16
2
8
15
31
31
5
13
14
21
5
15
5
3
2
8
16
31
5
9
6
14
5
4
4
1
1
6
1A denote theories, 1B is financial Determinants of P2P, 1C is demographic determinants, 1D is social determinants, 1E is
macroeconomic determinants of P2P lending, 3A is theoretical methodologies, 3B is using ordinal dependent variable models,
3C is discussion , 3D is two-stage models, 3E denotes
experimental studies and 3F denotes other methodologies.
Figure 4-Themes by Context
Table 3-Themes by Context
4.1.1
Informatio
n
asymmetry
and
signaling
Theme/Country
Africa
Australia
China
Estonia
Europe
Finland
Germany
Global
India
Indonesia
Iran
Israel
Italy
Korea
Portugal
Russia
S lovakia
S pain
Taiwan
UK
US A
1A
1B
1C
1D
1E
1
0
25
0
1
0
0
7
1
4
1
1
0
0
0
0
0
0
0
3
30
0
1
46
0
0
0
2
2
0
1
0
0
0
0
1
1
0
0
0
1
27
0
0
12
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
10
1
0
32
1
1
0
1
0
0
0
0
0
0
2
0
0
0
0
0
0
20
0
0
3
0
2
0
0
1
0
2
0
0
0
0
0
0
0
0
1
2
4
theories: Information asymmetry in the finance literature concerns the mismatch of information
between stakeholders and its effect on the various attributes of corporate finance and markets
(Johnson, 1998; Myers, 1984; Myers & Majluf, 1984). In lending markets, information
asymmetry between lenders and borrowers occurs because borrowers possess more information
about their borrowing capabilities, leading to adverse selection (Emekter et al., 2015; Gavurova
et al., 2018).
Empirical (Lin, 2015, Lin et al., 2013) and experimental (Yum et al., 2018) attempts to
examine information asymmetry mitigation by borrowers via descriptions of loan types, group
membership, and friendship have produced inconsistent results. Hard and soft information
mitigate information asymmetry in US platforms while lenders in China rely more on soft
information (Chen and Han, 2012). Through the information asymmetry and corporate
governance channels, Chen, X., et al. (2020) empirically examine the cash flow implications of
P2P and find that cash flow of P2P platforms is affected by reputation, capital, and operational
structures of platforms. In general, platform level information asymmetry and determinants can
be key areas of future research in online P2P. While signaling from venture capital rounds
increase transaction volumes, regulatory compliance is not affected after the first round (Yang,
H., et al., 2010). A possible explanation could be that the platforms reach optimal research
compliance for their initial rounds of private equity resulting in the insignificant impacts after the
first round. Avenues such as the research compliance and overall platform performance, both in
terms of financial and governance call for attention in future studies on online P2P.
Text mining mechanisms are employed to determine the effect of the length of
borrower’s loan purpose (Dorfleitner et al., 2016), punctuation (Chen et al., 2018), pictures,
being members of a group, leading a group and disclosures (Michels, 2012) and find them
associated with increased funding success and lower interest rates, but more often with an
increase in default rates. “Wisdom of the crowd” is a more significant contributor when a
borrower’s credit information is limited (Yum et al., 2012) indicating transparency on the part of
borrowers reduces herding effects.
Herding by group members and friends positively influences funding success
(Herzenstein et al., 2009, 2011; Liu et al., 2015), possibly due to the borrower’s moral
responsibility associated with belonging to a group, thus ensuring repayment. However,
platforms can intervene to build consensus and match borrowers and lenders that have not been
successful by using mathematical models (Zhang, H., et al., 2019). Lee and Byungtae (2012)
identified significant but diminishing marginal effects of herding on a Korean online P2P lending
platform. In an experimental setting, Prystav (2016) confirm that providing more information
positively influence funding success. Kgoroeadira et al. (2019) found that borrower’s pitch
targeting funding success in an attempt to reduce information asymmetry, however, this could
also mean that borrowers can manipulate lenders by providing information that ensure funding
success especially in economies where P2P platform lenders rely more on soft information.
Other studies contend that the effects of group social capital are inconsistent but belonging to a
group can be detrimental to a borrower’s funding success and is negatively associated with
repayment (Chen, et al., 2016).
4.1.2 Economic Theories: Few studies in the P2P lending literature have explored
macroeconomic activity relationships and the explanatory power of economic theories. Foo et al.
(2017) investigated the macroeconomic effects of P2P lending and concluded that credit markets
are affected by macroeconomic activity. Foo et al. (2017) identified six factors that tend to affect
lending activity: 10-year bond yield; unemployment; consumer price index; economic expansion;
market uncertainty; and market value. Foo et al. (2017) based their hypothesis on the substitute
investment option to equities provided by P2P lending. Jagtiani and Lemieux (2018) presumed
that Fintech lenders penetrate areas not reached by conventional banks and confirmed the reach
of Lending Club3 to domains where local market credit was not accessible to borrowers through
traditional lending channels. This is more pronounced in economically weaker areas, confirming
3
A leading lending platform in the U.S.
the effect of P2P lending in improving access to credit. Macroeconomic activity was expected to
significantly affect loan originations. As the phenomena of Fintech is still in its early
development, high-income countries with stronger information technology infrastructure and
better business indicators should demonstrate high loan originations overall. Comparatively, high
participation of financial institutions may be associated with lower P2P loan originations, as the
need for alternative finance may be minimal. Atz and Bholat (2016) discussed the effects of
online P2P lending on conventional banks and the plausible benefits of such competition for the
customer; the penetration of online P2P lending was higher in areas with a low bank
concentration and lower economic growth (Jagtiani and Lemieux, 2018).
4.1.3 Social Capital Theory: Structural and relational aspects of P2P lending may
deviate from findings on the relationship between social capital and economic outcomes due to
network decentralization and investors’ independent decision making (Lin et al., 2013). Trust is a
key aspect determining funding success, however perceived information quality can mitigate
perceived risk and enhance trust on the platforms (Chen, et al., 2015). Chen, X., et al. (2018)
considered the role of punctuation as a measure of borrowers’ self-control and cognitive ability
and found that overuse of punctuation reduced funding likelihood. Lu, et al. (2020) introduced a
theoretical model based on the unique features of P2P lending, namely social collateral, and soft
information, to show how P2P lending can outperform traditional lending markets. Social capital
can be expanded to include factors such as social status, education, social class, loan purpose,
and age (Wang, Zhang, Zhao, & Wang, 2019; Yao et al. 2019).
4.1.4 Portfolio Theory: Traditional lending institutions value credit risk of loans with
lower credit rates and adjust their interest rates accordingly. Conversely, online P2P lending
platforms are available to eligible borrowers with low credit worthiness, leading to the problem
of adverse selection by platform lenders. Lenders on these platforms fail to value credit risk
while increasing the chance of default; high default risk is not sufficiently covered by the high
interest rate (Emekter et al., 2015). Herding and increased information effects seem to exacerbate
this circumstance, as lenders may be led by group behavior and borrower manipulation to lend
based on lower interest rates, independent of expected default rates (Chen et al., 2014; Chen et
al., 2016; Chen, J. et al., 2018; Friedman & Jin, 2014; Herzenstein et al., 2009; Iyer et al., 2016).
Machine learning models (Caglayan et al. 2020), kernel regression and instance-based models
(Guo et al., 2016) are some of the methods employed to examine portfolio theory.
4.1.5 Discrimination Theory: Early work in the P2P lending literature, such as by
Herzenstein et al. (2009), discussed the lower likelihood of discrimination based on race and
gender on P2P lending platforms because individual lenders tend to be fair when lending directly
to consumers. The relationship between funding success and interest-rate discrimination, based
on various attributes such as race, age, gender, and appearance, has returned mixed results based
on context. Barasinka et al. (2014) used data from the leading German P2P lending platform
(Smava) and found that gender did not affect a borrower’s funding success. Chen et al. (2017)
evaluated gender discrimination in the Chinese lending market and noted that although women
were more likely than men to obtain funds, they were expected to pay higher premiums despite a
lower default risk. Gender inequality is significant on online P2P depending on the contexts.
Female business loan applicants are more likely to be rejected especially if they are attractive
(Kuwabara and Thébaud, 2017; Li, X., et al., 2020), female and older borrowers are more likely
to default; interest rates are negligently impacted by age and gender (Tao et al. 2017). Borrowers
who appeared trustworthy had a greater likelihood of being funded (Duarte et al., 2012).
4.1.6 Financial Regulations and Other Theories: Possible regulatory models for P2P
lending range from self-regulation to bifurcate and consumer protection–based models
(Mateescu, 2015). Financial regulations influence the potential for P2P lending (Segal, 2015),
strengthen performance (Song, 2018), and complement current approaches to promote new areas
(Philippon, 2016). Existing regulatory approaches vary by country. For instance, the regulatory
body overseeing P2P lending activities in the UK mainly consists of a combination of
governmental and self-regulation; in the U.S., the federal and state governments have adopted a
consumer protection approach to regulating P2P lending.
In other parts of the world, P2P lending is regulated under the umbrella of either
securities or non-banking financial corporations’ laws. Tsai (2018) compared regulations around
online P2P lending in Taiwan, China, and the U.S., pointing out that the U.S. asked platforms to
comply fully with existing securities regulations. As many other countries, although China was
initially hands-off, a series of P2P failures drove regulators to limit P2P lending platforms as
information intermediaries. In a similarly reactive policy, Taiwan’s Financial Supervisory
Commission directed the P2P lending industry to follow the existing regulatory and business
structures. Tsai (2018) further contend that the Taiwanese government issued the Financial
Technology Development and Innovative Experimentation Act (the “FinTech Sandbox Act”) as
a proactive legal framework under consumer protection laws. However, this act failed to solve
the regulatory dilemma between prudential regulation and financial competition and innovation
given a lack of institutional incentives to replace the existing regulatory regime with a truly
proactive model. Tsai (2018) thus recommended a structural change by reallocating the authority
of financial competition and innovation to an independent financial institution.
Finally, some countries have imposed total prohibition to mitigate the potential risks of
P2P lending channels. Interim regulations in China in 2016 were intended to promote this
business model while protecting consumers (Huang, 2018) and have sanitized lending channels
in the country. Despite the boom in alternative finance regulation since 2015, online P2P is
regulated only in 22% of jurisdictions (World Bank, & Cambridge Centre for Alternative
Finance, 2019) as opposed to 39% for Equity crowdfunding. Academic research has not captured
the changes in regulation except for few regulatory discussions. Event studies that examine the
impact of online P2P in local contexts following regulatory announcements is a robust avenue
for future research. A possible reason for the skewed focus of past studies on USA and China are
the availability of loan data that is available from Prosper and Lending Club in USA and
Renrendai, PPdai and a few other platforms in China. Future studies can also consider explore
obtaining data for empirical research from platforms in other contexts. Regulatory changes and
discussions during the past 5 years have also provided avenues for researchers to assess the
impacts of changes on various aspects of P2P lending.
4.2. P2P Determinants
Determinants of funding success, measured as the likelihood of being funded, are
dominated in the literature by studies examining platforms in the U.S. This trend is likely due to
availability of loan data in the public domain in the U.S., followed by Europe and China.
Herzenstein et al. (2009) constructed a theoretical framework to outline the financial and
demographic determinants of P2P funding success based on loan data from Prosper.com. They
found that financial indicators such as credit scores, borrowers’ efforts in obtaining a loan, an
explanation of the loan’s purpose, and borrowers’ repayment ability determined funding success.
By contrast, social and demographic factors such as gender, race, and marital status exerted little
impact on the likelihood of funding success. Many studies using data from different platforms
and contexts came to similar conclusions (Barasinka & Schafer, 2014; Dongyu et al., 2014; Feng
et al., 2015; Greiner et al., 2009; Lin et al., 2013). Cai et al. (2016) report that being a first-time
borrower versus a repeat borrower does not affect the funding determinants in the Chinese
market. Ding et al. (2019) noted that lending decisions are driven by borrower reputation; that is,
borrowers with good historical performance generally have more access to loans at lower costs
as well as a lower likelihood of default. The following subsections address different determinants
of funding success as highlighted in the literature.
4.2.1 Financial Determinants: 77 publications () that is 39% of the studies (Error!
Reference source not found.) examine the financial determinants of P2P lending including
probability of funding success, financial determinants of funding, credit risk, and probability of
default. A borrower’s loan amount, interest rate, and credit grade are significant determinants of
funding success and default, whereas duration may not be significant (Cai et al. 2015; Emetker et
al., 2015; Feng et al., 2015; Herzenstein et al., 2008; Traci et al., 2014). Similar to the traditional
lending channels, credit grade is a significant predictor of funding success; although borrowers
with poor credit grades have a higher chance of being serviced via online P2P lending platforms.
Furthermore, voluntary disclosures positively influence funding success and reduce interest rate
especially when loan application does not contain personal information (Li, Y., et al., 2020).
Proposing and examining credit scoring and default prediction models is a key focus area of
publications classified as financial determinates studies (for e.g. Liu, Y., et al., 2020; Wang, Han,
Liu, & Luo, 2019; Niu et al., 2019; Zhang, Z, et al., 2020). Testing proposed models on different
platforms and contexts can not only unpack contextual differences if any, but also provide with
validated models that can be transferred to industry for application. Approximately 90% of the
financial determinants studies focus on one or both of the two contexts , China and USA. Future
studies can examine other contexts to solidify theories related to financial determinants of online
P2P.
4.2.2 Demographic Determinants: 24 publications () , 12 % of the studies (Error!
Reference source not found.) examine the impact of gender, race, age, and other demographic
features in different contexts on funding success, interest rates , loan amounts , probability of
default and other loan attributes. Blacks and overweight persons tend to have lower funding
success, whereas women with military backgrounds are more often favored (Pope & Sydnor,
2011). Women were found to experience lower funding success and higher interest rates even
with lower default rates in China (Chen et al., 2017; Dongyu et al., 2013). Chen et al. (2019)
further identified no effect of gender on funding success rates, although female borrowers paid
more to maintain funding likelihood. Funding success was found to function independently of
gender in Germany (Barasinkha & Scafer, 2014). Gonzalez & Kamorova (2014) argued that age
and attractiveness may affect loan success. The inconsistent results across different contexts
stress the need for future empirical studies to
explore further the effects of demographic
determinants on P2P lending and to explain the variation of results based on cultural,
socioeconomic, and political systems. Future empirical studies could further explore the effects
of demographic determinants on P2P lending, as the results of existing studies are inconsistent
across different contexts.
4.2.3 Social Determinants: 56 publications (), 28 % of the studies (Error! Reference
source not found.) examine social relationships that play important roles on digital lending
platforms. Affinity to a group through enhanced trust amongst members increases funding
success. Due to information asymmetry between borrowers and lenders, lenders rely on signals
from group leaders and members to improve funding success, decrease interest rates, and
alleviate the effects of lower credit grades (Lin et al., 2013). Borrowers aim to maintain a good
reputation via provided information that may enhance trust (Yum et al., 2012). Friends also tend
to reciprocate bids, and the probability of funding is increased among elite lenders (Horvat et al.,
2015).
In a study of Chinese P2P lending, Chen et al. (2014) found that trust is integral to
funding success. Studies on behavioral aspects such as the predictive capabilities of sentiment
analysis, word count, and punctuation in borrowers’ posts can greatly influence behavioral
aspects on P2P lending platforms (Chen et al., 2018; Han et al., 2018; Larrimore et al., 2011).
Platform design can also attract a diverse group of lenders (Gerber et al., 2013). The impacts of
social groups may relieve constraints for borrowers and improve access to finance; however,
such groups also carry implications for regulators in the digital finance industry regarding
possible lender manipulation. Liu, Z., et al. (2020) argued that social capital can be used as
collateral to enhance the creditability of unsecured loans for low-risk borrowers. Using such
social collateral and soft information explains why P2P lending platforms can be more efficient
than traditional lending channels. In this sense, Wang, C., et al., (2019) highlighted that soft
factors predict the probability of funding as well as the default probability of a loan. They found
that older, married, and more educated borrowers experience more funding success than others
do. Yao et al. (2019) noted that the purpose of a loan has a major effect on lenders’ decisions;
borrowers must provide a clear description of the loan’s purpose to secure it and social network
information significantly contributes towards credit scoring and default prediction (Niu, B., et
al., 2019).
Binary dependent variables are the methodology preference to examine social
determinants on online P2P as in the case of financial and demographic determinants. Other
techniques employed to examine the importance of social determinants in P2P lending.
Structural equation modelling and partial least squares have been applied to examine the lenders
perceptions (Chen et al., 2015; Wan et al., 2016), funding success (Yao et al., 2019) and trust on
online P2P (Chen et al., 2014). Censored regression has been used to establish the curvilinear
relationship between status, reputation and P2P loans (Kuwabara, K., et al., 2017). Text mining
is utilized for credit risk assessment (Wang et al., 2016; Yang et al., 2017).
Latent Dirichlet
allocation model that uses text analysis (Jiang, C., et al., 2018b) , and machine learning methods
(Li, Y., et al., 2018; Ma et al.,2018) is used for default prediction. Finally, experimental studies
(Gonzalez and Komarova, 2014) assessed the impact of visual information such as photos
provided by borrowers on P2p loans.
4.2.4 Macroeconomic Determinants: Fewer publications (15 showed in ), 8 % of the
studies (Error! Reference source not found.) examine impact of macroeconomic factors on
online P2P lending. Atz and Bholat (2016) contend that entrepreneurial experience and financial
innovation in P2P lending may generate competition with traditional lenders and may thus
benefit consumers. Comparisons with aspects of traditional lending and complementary or
substitutionary effect of online P2P and traditional lending (Jagtiani and Lemieux, 2018),
financial intermediation/disintermediation (Havrylchyk and Verdier, 2018), macroeconomic
impact on default (Yoon et al., 2019) have been key focus areas in studies of macroeconomic
determinants of online P2P. Jagtiani and Lemieux (2018) found that Fintech lenders have filled
the gap in the U.S., where there are fewer traditional lenders. Foo et al. (2017) reported that
macroeconomic factors, namely 10-year Treasury bond yields and unemployment rates, were
negatively associated with P2P lending. Atz and Bholat (2016) uncovered similar trends.
However, empirical studies in the P2P lending literature remain heavily skewed towards the
impact of microeconomic factors on P2P lending; how these phenomena have contributed to the
macroeconomic context warrants further discussion.
Business borrowers’ funding success compared with that of individual borrowers has also
been examined in the literature. Online P2P lending platforms are available to borrowers with
low credit worthiness, leading to adverse selection by lenders on these platforms. Emekter et al.
(2015) found that lenders fail to value borrowers’ credit risk despite a greater chance of default;
that is, even if a higher interest rate is charged, it is not sufficient to compensate for the high risk
of default. Herding and increased information effect exacerbate the situation as lenders can be
led by group behavior and borrower manipulation to lend based on lower interest rates and
independent of expected default rates (Friedman and Jin, 2014; Herzenstein et al., 2009; Iyer et
al., 2016; Chen et al., 2014; Chen et al., 2016; Chen et al., 2018).
Methodologically, binary dependent variable and other regressions are the key techniques
used in macroeconomic determinant studies. Effectiveness of online P2P in alleviation of credit
constraint and promoting financial inclusion is an avenue that can be empirically analyzed.
Lerong, (2018) contend that alternative finance channels improve SME credit and may mitigate
their credit crisis and argue that a mix of self and government regulatory approach may be foster
the growth of alternative finance channels. Regulatory changes and the recent Covid-19
pandemic can be treated as events to conduct quasi-experiments to examine the sustainability
and effectiveness of online P2P .
Figure 5-Publications by chronology
Figure 6-Percentage of Publications by year
4.3. Chronological Classification
Figures 5 and 6 present the chronological classification of our selected publications.
Early publications on P2P lending appeared in 2008 with a marginal increase in the number of
articles in 2015. By 2016-2017, regulatory challenges emerged probably increasing research
interest on the topic as evidenced by over 75% increase in the number of articles in 2018 and
over 56% of the publications reviewed belonging to the period 2018-2020.
Regulation of P2P lending started in the U.S. as early as 2008 with the Securities and
Exchange Commission’s requirement to regulate platforms under its purview; consolidation
under the rules of the Financial Conduct Authority in the UK in 2014; and guidance from a set of
interim rules in China starting in 2016 (Huang, 2018). Over 61.9% of publications after 2016
examined lending platforms in China, explaining interest in this phenomenon following
regulatory changes in 2016. Approximately 62% of the publications in 2019 and 2020 examine
the financial, demographic and social determinants of online P2P. While over a third of these
studies propose new techniques for examining the determinants, only three (Australia, Germany
and Indonesia) unexplored contexts have been analyzed. We restate the lack of online P2P
research in other contexts, an avenue researcher with interest in the topic may consider.
4.4.
Figure 7- Article classfication based on Methdology
Methodological
Classification
A large proportion of the literature ( and
Figure 7) has examined the effects of
the financial, demographic, and social
characteristics of loan listings and
applicants; such variables can affect
Figure7 provides number of articles next to methodology. The number
of articles do not add-up to 198, as some publications apply more than
one methodology. (3A)-Theoretical models, models based on Artificial
intelligence , machine learning, (3B) Ordinal dependent variable
models 3C Policy and Regulatory discussions, (3D) - Two-stage
models such as Heckman selection, Cox proportional, (3E) Experimental studies, (3F) – Multiple regressions and other methods.
borrowers’ funding success, interest
rates, and default probabilities. Many
studies have adopted multiple regression
methodologies, largely binary dependent models such as probit, logit, or tobit (e.g., Barasinka &
Schafer, 2014; Michels, 2012; Prystav, 2016). Applications of novel empirical analysis methods
and decision-making tools have appeared in several studies (Foo et al., 2017; Guo et al., 2016;
Kumar et al., 2016; Mild et al., 2015; Zhao et al., 2017). Canonical correlation analysis has been
used to screen factors affecting online P2P variables. Data Envelopment Analysis (DEA) has
also been adopted, and a few studies have applied the estimation likelihood method and
multinomial logistic regressions (e.g., Wang, Han, Liu, & Luo, 2019). In addition, Yao et al.
(2019) applied the Latent Dirichlet Allocation (LDA) as text mining approach to classify loan
titles and investigate the impact of loan purposes on funding success. Topic models examine the
underlying themes comprising a set of narrative data within brief text. LDA is an effective
analytical tool to examine the underlying themes of textual content; it treats all documents as
probability distributions comprised of a number of topics, resulting in a probability distribution
of a number of words. LDA simulates the document generation process and uses parameter
estimation to identify topics intrinsic to the data. These methods are applied in over 40% of the
publications reviewed. Binary dependent variable models for funding success and default
prediction are the next widely applied techniques with over 33% publications using the
techniques (for e.g., Caldieraro et al., 2018; De et al., 2015; Michels, J. , 2012; Tang, 2018).
Fewer studies, 9% of the publications, have applied Cox proportional hazard models
(Chen et al., 2017; Emekter et al., 2015;
Figure 8-Methodology by Context
Lin, 2015; Lin et al., 2013) and variations
on hidden Markov models (Zhao et al.,
Table 5-Methodology by Context
Methodology/
Country
Africa
Australia
China
Estonia
Europe
Finland
Germany
Global
India
Indonesia
Iran
Israel
Italy
Korea
Portugal
Russia
Slovakia
S pain
Taiwan
UK
US A
3A
3B
3C
3D
3E
3F
1
1
45
0
1
0
1
3
1
3
1
1
0
0
0
0
0
0
0
3
25
0
0
35
1
2
0
1
0
0
1
0
0
0
1
0
0
0
0
0
0
26
0
0
4
0
0
0
0
6
0
2
0
0
0
0
0
1
0
0
0
1
12
1
0
7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
9
0
0
12
0
1
0
1
1
0
0
0
0
0
0
0
0
0
0
0
1
11
0
0
25
0
1
0
0
1
0
2
0
0
0
1
1
0
0
0
1
1
16
2017) to empirically examine interest-rate
determination and relationships between markets and borrower behavior. Semiparametric models
such as Heckman’s model have been applied by Michels (2012), Crowe and Ramachandran
(2014), Chen et al. (2016), and Ding et al. (2019). Online P2P being an emerging area,
approximately 13% of literature reviewed has offered theoretical frameworks and discussions
(for eg., Atz & Bholat, 2016; Bachmann et al., 2011; Herzenstein et al., 2009; Han et al., 2018;
Iyer et al., 2016; Lin et al., 2013; Morse, 2015; Milne & Parboteeah, 2016; Pokarna & Miroslav,
2016; Sundarajan, 2014).
Figure 8 and Table summarize statistics for studies on major economies, mainly China
and USA. The studies propose new theoretical models for default prediction, funding success
include machine learning (for e.g., Wang, Han, Liu, & Luo, 2019; Pan, Y., et al., 2020; Zanin,
2020), multi-criteria decision aid models (Wei & Lin, 2017; Ji et al., 2020), kernel function for
risk assessment (Pan, S., et al., 2020). 45 studies, over half of the studies examining Chinese
platforms , 25 publications , over 37% of the publications examining platforms in the United
States propose new theoretical frameworks or employ methodology classified as novel. Future
research can address by extending these methodologies to platforms in the rest of the world
contexts to validate the applicability and provide practical implications to platforms, lender,
borrowers and regulators. Psychological aspects of lenders and borrowers are more prominent in
P2P lending as compared to traditional lending channels, possibly due to the interpersonal
relationships between parties on online platforms that occasionally extend to social media and
other platforms inherent in peer group formation. Experimental studies, 14% of studies reviewed,
have considered the effects of these attributes on decision making in P2P lending (Gonzalez &
Kamorova, 2014).
Experimental studies examine credit scoring methods (for e.g. Guo et al., 2016; Zhang,
Z., et al., 2020), role of trust and appearance in online P2P (for e.g. Duarte et al., 2015), levels of
personal information provided on platforms (Prystav, 2016), herding (for e.g. Dan et al., 2018)
on online P2P platforms etc. Publications with a SNIP score greater than three have applied quasi
experiment (Lin and Viswanathan, 2016), experiment (Duarte et al., 2015), conditional logit
model (De et al., 2015), network analysis (Redmond and Cunningham, 2013). The prediction of
loan default probability is less common in the P2P lending literature due to online platforms’ use
of unstructured data; such text is difficult to quantify and analyze.
Recent studies such as, Jiang, C., et al. (2018a) constructed and examined default
prediction using hard and soft information. They also proposed topic models to extract valuable
features from stored loan texts on platforms in China and determined the effectiveness of these
models in predicting loan default behavior.
Figure 9-Methodology -SJR
Table 6-Methodology-SCHimago Journal Rank
SJR / 0-.999 1-1.999 2-2.999 3-3.999 4-4.999 5-5.999 8-8.999 12-12.999
Methodology
3A
43
15
6
2
0
2
1
0
3B
38
10
4
2
2
3
1
1
3C
11
3
0
1
0
3
1
0
3D
9
2
1
1
1
1
0
0
3E
15
5
2
0
0
1
0
1
3F
28
8
4
0
0
1
0
0
Empirical studies have used logit and probit
models or Heckman’s selection models to determine default probabilities and linear regressions
to analyze associations between the determinants of funding success and dependent variables
(e.g., interest rate and loan amount). Hoetker (2007) identifies pertinent concerns such as the
misinterpretation of coefficients and identification of model fit and recommended a cautious
approach to verifying the homogeneity of unobserved variation before comparing coefficients.
Studies applying ordinal dependent variable models tend to apply the
models incorrectly.
Moving forward, researchers can consider the limitations of interpreting the results of logit and
probit models using only the outcomes of regressions and should instead analyze findings with
marginal effects. Furthermore, to overcome data availability limitations, artificial intelligence
methods such as machine learning may be incorporated into empirical analysis.
Figure 10-SNIP by Methodology
Table 7-Methodology -SNIP
SNIP /
0-.999 1-1.999
Methodology
3A
23
25
3B
14
32
3C
7
5
3D
5
5
3E
6
9
3F
16
19
4.5. Journal Classification
2-2.999
3-3.999
4-4.999
5-5.999
14
6
3
2
6
5
5
6
3
3
2
1
0
1
0
0
1
0
2
2
1
0
0
0
P2P lending studies have been published in a variety of outlets as shown by the SJR and
SNIP metrics. We classify the documents based on Scopus Journal Rank (Error! Reference
Table 8-Top 20 cited sources
No. S ource Journal
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 total
%
Total
1
M anagement Science
0
0
0
5
10
33
40
88
169
185
253
783
24%
2
Electronic Commerce Research and Applications
0
0
4
3
7
32
29
39
56
51
89
310
10%
3
Applied Economics
0
0
0
0
0
2
8
14
49
65
72
210
7%
4
Journal of Interactive M arketing
1
1
5
4
8
20
17
20
37
35
29
177
5%
5
European Journal of Operational Research
0
0
0
0
0
0
0
0
2
34
107
143
4%
6
Business Research
5
5
4
4
4
15
14
16
19
20
20
129
4%
7
Journal of Internet Banking and Commerce
0
0
3
2
6
20
17
20
21
20
20
129
4%
8
M IS Quarterly: M anagement Information Systems
0
0
0
0
0
2
11
19
27
29
34
122
4%
9
Journal of Applied Communication Research
0
0
2
0
3
7
11
6
23
20
34
106
3%
10
Decision Support Systems
0
5
4
2
3
5
6
11
18
19
30
103
3%
11
Journal of M anagement Information Systems
0
0
0
0
0
0
0
1
14
13
65
93
3%
12
Journal of Behavioral and Experimental Finance
0
0
0
0
0
6
4
7
14
14
26
71
2%
13
Information Technology and M anagement
0
0
0
0
0
3
5
6
15
16
16
61
2%
14
International Journal of Industrial Organization
0
0
0
0
0
0
0
0
5
15
31
51
2%
15
Financial Innovation
0
0
0
0
0
0
3
5
5
17
14
44
1%
15
Information Systems and e-Business M anagement
0
0
0
1
0
4
2
5
12
7
13
44
1%
16
Annals of Operations Research
0
0
0
0
0
0
0
0
2
12
19
33
1%
17
Review of Financial Studies
0
0
0
0
0
0
0
0
0
8
24
32
1%
18
Banking and Finance Review
0
0
0
0
0
1
0
2
6
16
6
31
1%
19
IEEE ACCESS
0
0
0
0
0
0
0
0
0
11
19
30
1%
20
Electronic Commerce Research
0
0
0
0
0
0
0
0
0
10
19
29
1%
Table 8 provided the top twenty cited sources for the selected publications. Citation scores were obtained from Scopus and
when not available on Scopus from Web of Science database.
source not found.9 and Table 6) and Source Normalized Impact factor (Error! Reference
source not found.7 and Error! Reference source not found.) as the publications examining
online P2P lending platforms appear in journals of different disciplines. One study, Caldieraro et
al. (2018) employing a counter signaling model and logistic regressions has been published in a
source (Journal of Marketing) , with a SNIP score of above 5 and SJR of 8.626. Two studies
(Tang et al., 2018; Duarte et al., 2015) have been published in a source with SNIP of 4.903 and
SJR 12.837 namely, Review of Financial studies. Over half of the publications (102) have been
indexed in Academic Journal Ranking (AJG) list, with ten publications in a 4* journal, ten in a
journal rated 4, twenty in journal rated 3. Overall, 39.21% of the AJG indexed articles are
published in sources rated 3 and above. It is noteworthy, to mention that the articles with SNIP
score of over 4 and SJR over 8 are published in the AJG sources.
We consider another metric, the percent
Figure 11-Sources by Percent cited
cited metric to analyze the quality of publications
across disciplines (Figure 1). The metric is available
for 173 of the total 198 publications. 43% of the
publications are in sources that have a percent cited
score of above 70, three of the studies (Chen, Q., et
al., 2020; Han et al., 2018; Tao et al., 2017) are from sources with a percent cited score of above
90. Error! Reference source not found. provides twenty most cited sources for publications in
online P2P research from Scopus and WOS databases. Management Science is the top cited
source with 24% of the citations, followed by Electronic Commerce Research and Applications
with 10% of the citations. Table provides the most cited publications. “Judging borrowers by the
company they keep: Friendship networks and information asymmetry in online peer-to-peer
lending” (Lin et al., 2013), that examines the impact of relational networks in funding success,
interest rate and probability of default on Prosper is the most cited publication. 40 publications
selected for the review are not from peer-reviewed sources, such as working papers published by
universities and other international organizations or international financial organization reports;
however, these publications are relevant to the discussion of P2P lending.
Furthermore, the topic has appeared in cross-domain studies such as those involving
information technology, management, and small business. However, we did not find articles
examining P2P lending in conjunction with microfinance organizations. Classification of the
publications based on disciplines could not be discussed in this review owing to availability of
information and time limitations, an aspect future review on online P2P can consider. Avenues
for future research are thus apparent in this domain due to the topics’ shared characteristics.
Table 9-Top 20 cited publications
No. Document Title
Judging borrowers by the company they keep:
1 Friendship networks and information asymmetry in
online peer-to-peer lending
Strategic Herding Behavior in Peer-to-Peer Loan
Auctions
2
Year Journal Title
Author(s)
2013 M anagement Science
Lin M ., Prabhala N.R.,
Viswanathan S.
0
0
0
5
10
33
35
55
89
87
115
429
Journal of Interactive
M arketing
Herzenstein M ., Dholakia
U.M ., Andrews R.L.
1
1
5
4
8
20
17
20
37
35
29
177
Applied Economics
Emekter R., Tu Y.,
Jirasakuldech B., Lu M .
0
0
0
0
0
2
8
14
43
48
59
174
M anagement Science
Lin M ., Viswanathan S.
0
0
0
0
0
0
3
18
48
51
52
172
Electronic Commerce
Research and Applications
Lee E., Lee B.
0
0
4
2
4
16
21
21
27
26
44
165
Business Research
Berger S.C., Gleisner F.
5
5
4
4
4
15
14
16
19
20
20
129
0
0
0
0
0
2
11
19
27
29
34
122
0
0
0
0
0
0
2
14
21
32
50
119
0
0
0
0
0
0
0
0
0
27
84
111
0
0
2
0
3
7
11
6
23
20
34
106
2011
Evaluating credit risk and loan performance in online Peer2015
to-Peer (P2P) lending
Home bias in online investments: An empirical study of
2016
an online crowdfunding market
Herding behavior in online P2P lending: An empirical
2012
investigation
Emergence of Financial Intermediaries in Electronic
2009
M arkets: The Case of Online P2P Lending
Friendships in online peer-to-peer lending: Pipes, prisms,
2015
and relational herding
3
4
5
6
7
Screening peers softly: Inferring the quality of small
borrowers
Soft consensus cost models for group decision making
9
and economic interpretations
8
Peer to Peer lending: The relationship between language
10
features, trustworthiness, and persuasion success
From the wisdom of crowds to my own judgment in
11 microfinance through online peer-to-peer lending
platforms
M IS Quarterly: M anagement De L., Brass D.J., Lu Y.,
Information Systems
Chen D.
Iyer R., Khwaja A.I.,
2016 M anagement Science
Luttmer E.F.P., Shue K.
European Journal of
Zhang H., Kou G., Peng
2019
Operational Research
Y.
Larrimore L., Jiang L.,
Journal of Applied
2011
Larrimore J., M arkowitz
Communication Research
D., Gorski S.
2012
Electronic Commerce
Research and Applications
Yum H., Lee B., Chae M .
0
0
0
1
3
16
8
11
18
18
20
95
Journal of Internet Banking
and Commerce
Bachmann A., Becker A.,
Buerckner D., Hilker M .,
Kock F., Lehmann M .,
Tiburtius P., Funk B.
0
0
3
2
3
12
13
11
13
17
15
89
Puro L., Teich J.E.,
Wallenius H., Wallenius J.
0
5
4
2
3
5
6
11
18
12
14
80
Gonzalez L., Loureiro
Y.K.
0
0
0
0
0
6
3
7
13
11
22
62
12 Online peer-to-peer lending - A literature review
2011
13 Borrower Decision Aid for people-to-people lending
2010 Decision Support Systems
14
15
16
17
18
19
20
When can a photo increase credit? The impact of lender
and borrower profiles on online peer-to-peer loans
A trust model for online peer-to-peer lending: a lender’s
perspective
M arket mechanisms in online peer-to-peer lending
The information value of online social networks: Lessons
from peer-to-peer lending
Predicting and Deterring Default with Social M edia
Information in Peer-to-Peer Lending
A decision tree model for herd behavior and empirical
evidence from the online P2P lending market
A comparative study of online P2P lending in the USA
and China
2014
2014
2017
2017
2017
2013
2012
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Total
Journal of Behavioral and
Experimental Finance
Information Technology and
M anagement
M anagement Science
International Journal of
Industrial Organization
Journal of M anagement
Information Systems
Information Systems and eBusiness M anagement
Journal of Internet Banking
and Commerce
Chen D., Lai F., Lin Z.
0
0
0
0
0
3
5
6
15
16
16
61
Wei Z., Lin M .
0
0
0
0
0
0
0
1
10
11
30
52
Freedman S., Jin G.Z.
0
0
0
0
0
0
0
0
5
15
31
51
Ge R., Feng J., Gu B.,
Zhang P.
0
0
0
0
0
0
0
1
9
8
29
47
Luo B., Lin Z.
0
0
0
1
0
4
2
5
12
7
13
44
Chen D., Han C.
0
0
0
0
3
8
4
9
8
3
5
40
Table 9 presents number of citations for the top twenty cited publications based on citation scored on Scopus for citation metric
available on Scopus. For other publications, the number of citations was obtained from Web of Science (WOS).
4.6. Geographical Classification
Figure 12 shows that over 48% of publications examined online P2P lending in China
while over 34% focused on
Figure 12-Number of publications by context
platforms in the United
States. However, whereas platforms in the United States were most examined early in the online
P2P lending literature, platforms in China have stimulated more recent interest. Overall,
platforms in the rest of the world, specifically Europe, have not received sufficient attention,
considering UK having third most volume of online P2P (Ziegler et al., 2018). Regulation in this
region falls under consumer protection and the self-regulation mechanism of crowdfunding.
Furthermore, studies on lending platforms in Africa, South America, Australia, and the rest of
Asia may provide insights into this relatively new and disruptive debt-financing mechanism. A
few articles considered platforms in Taiwan, South Korea, the UK, other European countries
such as Germany and Estonia, and a unique platform in Denmark that caters to borrowers in
African countries.
Data availability seems to explain
the geographical concentration. Empirical
studies involving hard information (e.g.,
financial or demographic data) and soft
determinants
such
as
social
groups,
appearance, and loan details and purpose
on funding success, interest rates, and
default rates tended to guide research methodologies. Multiple regressions; dichotomous
dependent models such as logit, probit, and tobit regressions; and hazard models dominated
empirical studies in nearly all geographical locations. Regulatory discussions mostly originated
from the U.S. and Europe. Figure 13 and Table 10 show the SNIP by context. The four
publications with a SNIP of above 4 are all from the United States. The only other country with
publications in sources with SNIP of above 3 is China. Publications from all other contexts
examined for which the metric is available are in sources with SNIP of below 3 . This can be
considered by future research by applying robust methodologies and novel research questions
that are required by high quality sources.
P2P lending holds the largest market share in alternative finance at 59% in Europe, 71%
in Africa, 32% in Latin America and Carribean, 19% in Middle East, 48% in USA and 11% in
Canada, 47% in Asia Pacific excluding
Table 4-SNIP by Context
S NIP/Country
Africa
Australia
China
Estonia
Europe
Finland
Germany
Global
India
Indonesia
Iran
Israel
Italy
Korea
Portugal
Russia
S lovakia
S pain
Taiwan
UK
US A
0-.999
0
0
33
0
0
0
0
0
0
6
0
1
0
0
1
1
0
0
1
3
12
1-1.999
1
0
42
1
1
0
2
2
0
1
1
0
0
2
1
0
0
0
0
1
21
2-2.999
1
0
15
0
1
0
0
4
0
0
0
0
0
0
0
0
0
0
0
0
8
3-3.999
0
0
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
8
4-4.999
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
5-5.999
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
China and 96% in China (Ziegler et al.,
2021). Less than 12% of the studies discuss
and/or empirically examine the platforms
in Europe, 1% examine Middle East, 5.5%
examine Asia Pacific region excluding
China.
Future
research
can
consider
examining contexts not examined so far.
Future work can also explore other
developing countries and areas with low
financial inclusion to demarcate the impact of Fintech in general and P2P lending in particular on
access to finance. In addition, future contextual research can examine the diversification
opportunities provided by global online P2P lending platforms for lenders and investors such as
venture capitalists.
5. Discussion and Conclusion
Online P2P lending platforms represent an emergent but fast spreading phenomenon in the
context of digital finance; however, studies that examined P2P lending phenomenon are still
limited and challenged by data availability. This study comes to fill the gap to examine, collate,
critique, and summarize the available research on online P2P lending. As far as we are aware,
this is the only recent study that comprehensively reviews online P2P lending literature (198
published papers) and examines the major relevant literature which discusses the determinants of
P2P, discussions related to its regulations and related theories. In this section, we are going to
highlight the answers to our research questions by explaining the evolving of P2P literature and
suggesting further research agenda.
Using qualitative and quantitative systematic review, this study finds that previous studies
theoretically concentrated on information
asymmetry, signaling hypothesis, social capital
theory. Available studies on P2P lending are also found geographically skewed to empirically
examine data from U.S. and China; many other regions with high concentration of P2P
businesses have not been empirically examined. However, methodologically, regression models
are the mainly used models to examine the determinants of P2P lending.
Moving to the publishing journals, 102 publications (over 50%) appear in Academic Journal
Guide (AJG) indicating the importance of the topic in business fields. However, only 3
(approximately 2%) publications are from sources with a percent cited metric of over 90%, 2
(1%) with a SCHimago Journal Rank of over 5 and 2 (1%) with a Source Normalized Impact
factor (SNIP) of over 10, indicating the limited discussion of the online P2P topic in high-quality
sources, an avenue for future research to consider.
Financial determinants such as borrowers’ credit grades, interest rates, and loan duration are
significant predictors of funding success. Social factors such as group membership,
communication outside lending platforms, and borrower leadership play similarly important
roles. However, demographic factors such as borrower gender, age, and race seem to vary
globally. Over 50% of the articles examined financial, demographic, and social determinants of
funding success. Europe is more neutral to demographic characteristics. Although these
characteristics seem to have less effect on funding success, interest rates, and default
probabilities on lending platforms in European countries, they are significant for platforms in
China and the United States. A possible explanation could involve variations in regulations and
other debt mechanisms between the countries. The results may have also been skewed because
more than 83% of studies focused on platforms in either the U.S. or China. Data availability
related to online P2P lending in the U.S., UK, and China is noteworthy.
Future research is recommended to consider other contexts; global studies may have been
limited in our sample due to data availability. Atz and Bholat (2016) contend that online P2P
data is less opaque compared to financial institutions and is accessible with the application of big
data methodologies and right statistical tools. Concentrated efforts to obtain global data and
empirical examination can decode the differences in determinants and online P2P behavior. In
addition, future research agendas in Fintech could focus on behavioral aspects such as cultural
uniqueness, language, information technology literacy, innovation quotient, and law and order
effects on the determinants of P2P lending.
Methodologically, many articles have neglected the nuances in interpreting binary dependent
variable models (e.g., reporting marginal effects instead of coefficients to offer clearer
explanations of relevant impacts). Binary dependent variable models and hazard models are
common in studies of the determinants of funding success, interest rates, and default; more than
35% of studies adopted such models, specifically early empirical research. Recent work,
however, applies methods based on artificial intelligence and other big data methodologies to
examine determinants of funding, credit risk and default. Research can extend or apply these
methodologies to platforms in the rest of the world context to validate their applicability and
provide practical implications to platforms, lender, borrowers, and regulators. Dynamic models
that account for the dynamicity of various determinants may be explored in future studies.
Growth in online P2P lending in China and the rest of the world has motivated regulators to
introduce a regulatory framework to address the subsequent default and closure of online P2P
lending platforms. The industry was brought under the Financial Conduct Authority in the UK in
2014 and under the Dodd–Frank Act of 2011 in the U.S. After the global financial crisis, online
P2P lending was also regulated through the Consumer Financial Protection Bureau. Regulatory
frameworks must consider the dynamic nature of P2P lending; self-regulation and unambiguous
regulatory guidelines can improve the consumer experience and facilitate the sterilized growth of
lending channels (e.g., Huang, 2018; Philippon, 2016; Slattery, 2013). Introduction of
regulations or changes to existing regulation, and discussions during the past 5 years have also
provided avenues for researchers to assess the impacts of changes on various aspects of P2P
lending. Event study methodology may be adopted to examine the changes in functioning of
platforms and disintermediation levels post local regulations. Discussion papers offering
regulatory insights may reflect the forward-looking perspective of Fintech practitioners and
academics, possibly due to the evolution of digital finance following the global financial crisis.
Online P2P contends to fill gaps where financial institutions are not accessible. Overall, P2P
lending acts more as complement than substitute for traditional lending channels (Jagtiani &
Lemieux, 2018). The markets are less able to predict and price default; this form of lending may
expand consumption, improve productivity, shift innovation towards high-credit-grade
customers, and offer disintermediation rents to lenders Furthermore, the impact of Covid-19
event may be examined to decode the resilience of online P2P to uncertainty related to political,
economic and other forms.
Platform performance, in terms of profitability, non-performing loans, governance
mechanisms, venture capital rounds, and other online P2P firm characteristics represent more
areas for future inquiry (Yang et al., 2020; Xie 2020). P2P lending seems to offer an alternative
to traditional financial institutions as indicated in early studies. Regulation also appears to play a
key role in the growth of platforms and loan originations in developing countries. For instance,
following exponential growth earlier in the 2010s, regulation announcements in China seem to
have sterilized fraudulent platforms but have come at the cost of market confidence and financial
penalties to investors. Regarding a future Fintech agenda, Thakor (2019) discussed the roles of
traditional financial institutions and posited that online P2P lending may develop into a
mechanism adopted by the banking sector instead of replacing traditional banking. In addition,
subsequent research should investigate hybrid shadow banking elements that integrate P2P
lenders and banking services. The sustainability of this phenomenon in developing markets
deserves further exploration. Future research considering the above factors can guide investors
seeking diversification in addition to informing policy implications.
To conclude, our insights should help practitioners to understand the dynamics affecting P2P
consumers, lenders and regulators. Empirical validation of new models proposed by the reviewed
publications in different contexts and platforms can provide platforms with mechanisms for
operational and credit risk mitigation. Implications for future research in terms of methodologies,
themes and contexts are provided.
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Shabeen Afsar Basha: Conceptualization, Methodology, Software, Formal analysis, Investigation,
Writing - original draft, Data Curation, Validation
Mohammed M Elgammal: Conceptualization; Formal analysis, Methodology; Project administration;
Resources, Validation, Writing - review & editing, Supervision
Bana M. Abuzayed: Conceptualization; Formal analysis, Methodology; Project administration; Resources
Validation, Writing - review & editing, Supervision
Online Peer-To-Peer Lending a Review of the Literature
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This study reviews the online P2P lending literature from 2008 until 2020.
The literature is geographically skewed towards United States and China.
Previous research focused on the determinants of funding success, loan attributes and default.
Recently, there is a shift in the methodological approaches applied in the funding success and
default predictions.
Recommendations of regulatory discussions are divided between self-regulatory versus
institutional regulations.