Online P2P Lending Platforms Research Articles

Analysis of the Ponzi Scheme in P2P Platform: Taking Ezubo as an Example

Driven by economic development, P2P online lending platforms have become a new area for the development of the financial industry. The rise of P2P platforms reflected the improvement in people's quality of life and the increasing demand for investment. This is good for economic development, but it also increases the difficulty of market supervision, and investment fraud from the perspective of Ponzi schemes is becoming more and more serious. This paper took Ezubo as an example to study the causes of the Ponzi scheme. According to research, it was found that the formation of a Ponzi scheme was closely related to corporate financial fraud, corporate and P2P risk problems, and social factors. The paper analyzed Ezubo's financial fraud, conducted risk analysis, and made suggestions on risk management and strengthening education. This paper helps to complement the shortcomings of current research on Ponzi schemes, and the advice on such behavior also has some practical implications to help people stay away from investment fraud.

Blockchain Technology Mitigates the Role of Information Asymmetry -- Take P2P Online Lending Platforms as an Example

With the rise of online finance, P2P online lending platforms based on new lending methods have ushered in an era of rapid development, attracting the attention and research of scholars. This paper uses platform data comes from the home of online loans, the National Bureau of Statistics of the People's Republic of China, and the Wind database, and takes P2P online loan platform as an example to study its impact on information asymmetry. Empirical analysis shows that P2P online lending can alleviate information asymmetry and improve information transparency, and the robustness test verifies the results of benchmark regression. Finally, this article also makes some specific recommendations for enhancing the P2P online loan platform based on the findings of the investigation such as the use of blockchain technology to establish a regulatory system and improve information transparency.

Politeness matters: The role of polite languages in online peer-to-peer lending

Given the limited information for assessing borrowers in online peer-to-peer (P2P) lending, the textual content of loan requests has been recognized as a crucial factor influencing lenders' lending decisions. However, whether politeness, an important but largely neglected linguistic element in loan requests, is related to lenders' decisions remains underexplored. Drawing from signaling theory, this paper seeks to answer two important questions: (1) Is the use of polite language in loan requests related to lenders' decisions? (2) If so, for which types of borrowers or loans is politeness more beneficial? We test our theoretical framework using a sample of 127,044 loan requests from Renrendai, a leading online P2P lending platform in China. Our findings reveal a significant and positive relation between the use of polite language and the likelihood of successful funding. This positive relation is stronger for borrowers with higher credit ratings, higher education levels, as well as for loan requests with lower information richness and higher certainty in language. Our findings suggest that politeness functions as a costless signal that complements costly signals, reducing information asymmetry in P2P lending. This paper bridges the gap between the literature on the role of politeness in individual decision making and information asymmetry in financial markets, providing new insights into the role of language use in P2P lending.

Proposing a new loan recommendation framework for loan allocation strategies in online P2P lending

PurposeLenders in online peer-to-peer (P2P) lending platforms are always non-experts and face severe information asymmetry. This paper aims to achieve the goals of gaining high returns with risk limitations or lowering risks with expected returns for P2P lenders.Design/methodology/approachThis paper used data from a leading online P2P lending platform in America. First, the authors constructed a logistic regression-based credit scoring model and a linear regression-based profit scoring model to predict the default probabilities and profitability of loans. Second, based on the predictions of loan risk and loan return, the authors constructed linear programming model to form the optimal loan portfolio for lenders.FindingsThe research results show that compared to a logistic regression-based credit scoring method, the proposed new framework could make more returns for lenders with risks unchanged. Furthermore, compared to a linear regression-based profit scoring method, the proposed new framework could lower risks for lenders without lowering returns. In addition, comparisons with advanced machine learning techniques further validate its superiority.Originality/valueUnlike previous studies that focus solely on predicting the default probability or profitability of loans, this study considers loan allocation in online P2P lending as an optimization research problem using a new framework based upon modern portfolio theory (MPT). This study may contribute theoretically to the extension of MPT in the specific context of online P2P lending and benefit lenders and platforms to develop more efficient investment tools.

Online Loan Default Prediction Model Based on Deep Learning Neural Network.

With the rapid development of Internet loans and the demand for Internet loans, Internet-based loan default prediction is particularly important. P2P online lending is based on Internet technology. With the popularization of personal PCs and mobile terminals, the borrower's financing cost has been reduced to a large extent, and the efficiency of the borrower's capital utilization has also been improved to a considerable level. Making full use of the existing data of the online lending platform, integrating third-party data, and predicting the default behavior of users are the major directions of future development. This paper mainly studies the network loan default prediction model based on DPNN. This paper first analyzes the problems and risks of the P2P online lending platform, then introduces the principle and characteristics of BPNN in detail, and determines the credit risk rating process for online lending based on BPNN. With the help of data analysis and processing software, after cleaning and variable selection of credit customer data provided by lending clubs, a set of corresponding online lending default risk assessment models are established through BPNN. This paper simulates the network loan default assessment model of the BPNN model and compares it with the support vector machine and regression model. The experimental results show that the highest accuracy rate of the BPNN model is 98.01% and the highest recall rate is 99.82%, which is better than the other two models; the AUC value of BPNN is 0.79, which is significantly higher than that of support vector machine and regression model. The above results show that the online loan default prediction model based on DPNN has high application value in practice. Predicting the probability of customer default risk in advance will help reduce the risk of P2P companies and lenders, improve the competitiveness of P2P lending institutions, and promote the development of domestic P2P platforms to be more stable.

Online peer-to-peer lending platform and supply chain finance decisions and strategies

Online peer-to-peer (P2P) lending platform is an emerging FinTech business model that establishes a link between investors and recipients of capital in supply chains (SCs). Businesses face capital constraints impacting directly on their final product price and demand. This article studies optimal decisions and operational strategies in a logistics network considering two capital-constrained manufacturers who produce products of different qualities and sell them to a retailer having deterministic demand over a specific period. The high quality product manufacturer borrows capital through an online P2P lending platform with a service fee, while the low quality product manufacturer pre-sells products for competing with the high quality product manufacturer. In this study, we find optimal prices of the SC participants, service rate of the online P2P platform and percentage of the pre-ordering quantity of the retailer. We analyse optimal Stackelberg and Nash equilibrium of the SC participants. We find that an increase in the amount of opportunity cost will cause a decrease in the pre-ordering quantity of the retailer affecting the SC profit in numerous ways. The online P2P lending platform should consider the amount of the retailer’s target profit in determining the platform’s service rate. We posit some practical insights based on our numerical study and observations for SC managers enabling them to take appropriate measures about their optimal strategies according to the networks’ existing economic conditions.

Trust in peer-to-peer (P2P) lending platforms in Malaysia: understanding the determinants from retail investors' perspectives

PurposeDespite a large stake of investment by retail investors and a growing number of peer-to-peer (P2P) lending platforms coupled with the initiation of secondary market and strong regulatory framework, less is known what leads investors to trust in P2P (TP2P) lending platforms in a multi-ethnic country, Malaysia. This study aims to investigate the effects of individual characteristics (gender, age, ethnicity, education and income), social influence of P2P (SIP2P) lending and privacy of P2P (PP2P) lending on the trust in emerging P2P platforms.Design/methodology/approachA cross-sectional survey was conducted to collect the data from retail investors in Malaysia. A variance-based partial least squares-structural equation modeling (PLS-SEM) model was applied to examine the significant predictors of TP2P lending platforms.FindingsThe results show that while investors' income is positively related to TP2P lending platforms, younger investors are less likely to have trust on P2P lending platforms. PP2P lending platforms increases retail investors' trust toward P2P platforms in Malaysia.Practical implicationsP2P service providers are suggested to give especial attention to investors' specific characteristics to develop trust and attract investors to the platforms. Service providers need to ensure the privacy of potential investors' personal and confidential data to build investors' trust.Originality/valueThis is the first study to assess retail investors' trust toward online P2P lending platforms in Malaysia, where this alternative financing platform gradually gaining popularity.

Posts and reviews in P2P online lending platforms: a sentiment analysis and cross-culture comparison

ABSTRACT P2P online lending platforms have been developing rapidly worldwide in recent years. This study seeks to explain platform users’ behaviour in different cultures. Specifically, this research aims to investigate the sentiment polarity of the posts in P2P online lending platforms and how it influences the quantity and quality of user reviews. Over 3000 online posts were collected from large online platforms in China and U.S. The findings indicate that in U.S. P2P online lending platforms, there are more reviews for negative-sentiment posts than those for positive-sentiment posts. In a collective culture such as China, there are more reviews in P2P online lending platforms for positive-sentiment posts, and platform participants tend to post strong sentiment information.

Can Social Notifications Help to Mitigate Payment Delinquency in Online Peer‐to‐Peer Lending?

Payment management is an operations management challenge in online peer‐to‐peer (P2P) lending. It is critical to control the cost of debt collection incurred in late repayments and defaults. We study whether and how a platform can leverage on borrowers’ social connections and use automatic social notifications in regulating repayment behavior. In collaboration with a large online P2P lending platform, we conduct a randomized field experiment to investigate the effect of social notifications targeted at different contact groups (core‐circle and peripheral‐circle groups). Our results indicate that notifying social contacts of a delinquent regarding the overdue payment significantly improves the repayment rate. Compared with the control group in which no notification messages are sent to social contacts, notification‐triggered social sanctions and social support reduce the default rate by more than 50% in both core‐circle and peripheral‐circle groups. Furthermore, we find that social notifications targeted at peripheral‐circle social contacts are only effective in the short term, and its effectiveness decreases with repeated use. By contrast, social notifications targeted at core‐circle social contacts have a lasting effect.

Low rank representation and discriminant analysis-based models for peer-to-peer default risk assessment

PurposeThis study aims to assess the default risk of borrowers in peer-to-peer (P2P) online lending platforms. The authors propose a novel default risk classification model based on data cleaning and feature extraction, which increases risk assessment accuracy.Design/methodology/approachThe authors use borrower data from the Lending Club and propose the risk assessment model based on low-rank representation (LRR) and discriminant analysis. Firstly, the authors use three LRR models to clean the high-dimensional borrower data by removing outliers and noise, and then the authors adopt a discriminant analysis algorithm to reduce the dimension of the cleaned data. In the dimension-reduced feature space, machine learning classifiers including the k-nearest neighbour, support vector machine and artificial neural network are used to assess and classify default risks.FindingsThe results reveal significant noise and redundancy in the borrower data. LRR models can effectively clean such data, particularly the two LRR models with local manifold regularisation. In addition, the supervised discriminant analysis model, termed the local Fisher discriminant analysis model, can extract low-dimensional and discriminative features, which further increases the accuracy of the final risk assessment models.Originality/valueThe originality of this study is that it proposes a novel default risk assessment model, based on data cleaning and feature extraction, for P2P online lending platforms. The proposed approach is innovative and efficient in the P2P online lending field.

Determinants of defaults on P2P lending platforms in China

Chinese P2P lending platforms have an astonishing default rate of 87.2% based on data available in 2019, which indicates the seriousness of the problem this industry faces. Insufficient regulation has resulted in generation of risky services, such as margin finance in 2015 for stock markets and zero down-payment mortgages in 2016 for real estate buyers. Such services are prone to resulting in dramatic losses to investors with the following potential causes: adverse selection caused by information asymmetry of the P2P platform operators, lack of financial knowledge or expertise of the investors, insufficient regulation on P2P platforms, and changes in policies related to stock and real estate markets.Does “Too Big to Fail” (TBTF) apply to Peer-to-peer (P2P) lending platforms in China? Are platforms with large Transaction Volumes or large Total Loan Size safer? This paper aims at answering such questions and identifies other factors that have led to defaults of such P2P lending platforms. In order to generate a more robust model that covers a wider range of factors, we adopt two approaches: Failure Prediction Model and Decomposition Methods. The reason why we have adopted this approach was that the factors that caused failure of such P2P platforms were very complicated and varied in nature. Failure Prediction Model is mainly based on the disclosed transaction or financial data. However, Decomposition Method aims at identification of sources of problem from different groups of factors, such as internal operations and external business environment. Also, another contribution of our research is that the data sets that we have used are from three different sources, which provide us a more comprehensive picture about the risks faced by such P2P lending firms.This study provides evidence that the bond yield, which reflects the liquidity level of the market, is the major reason for P2P platforms’ failure during the research period. Our results also indicate that apart from the size-related factors one also needs to answer the question - “Were platforms with larger Transaction Volumes and Total Loan Sizes safer?” The style factors like the interest rate and ownership, the timing factors like loan maturity and bond yield, and the selectivity factors like popularity are found to be even more significant. Hopefully, our findings could contribute academically to the existing literature in the credit risk estimation of online P2P lending platforms and provide practical support for development of a more effective risk monitoring system for online P2P lending platforms.

How does an individual\u2019s default behavior on an online peer-to-peer lending platform influence an observer\u2019s default intention?

Previous studies indicate that individuals’ default behaviors on online peer-to-peer (P2P) lending platforms greatly influence other borrowers’ default intentions. However, the mechanism of this impact is not clear. Moreover, there is scarce research in regard to which factors influence the relationship between an individual’s default behavior and an observer’s default intention. These important questions are yet to be resolved; hence, we conducted two experiments using the scenario-based research method, focusing on Chinese online P2P lending platforms. Our results indicate that an individual’s default behavior can trigger an observer’s default intention as a result of the imperfect punitive measures as they currently exist on Chinese online P2P lending platforms. Both the observer’s moral disengagement level and pragmatic self-activation level serve as mediating variables. In situations where an observer knows an individual’s default behavior, the level of intimacy between the defaulter and observer positively affects the relationship between their default behavior and intention. The intimacy level also positively influences the relationship between the individual’s default behavior and the two mediator variables. Based on the findings, we provide management suggestions in the context of online P2P lending. Our study sets a foundation for future research to utilize other methods to extend the present research findings to other regions and domains.

Study on Evaluation Model of Chinese P2P Online Lending Platform Based on Hybrid Kernel Support Vector Machine

Accurate evaluation of the risk level and operation performances of P2P online lending platforms is not only conducive to better functioning of information intermediaries but also effective protection of investors’ interests. This paper proposes a genetic algorithm (GA) improved hybrid kernel support vector machine (SVM) with an index system to construct such an evaluation model. A hybrid kernel consisting of polynomial function and radial basis function is improved, specifically kernel parameters and the weight of two kernels, by GA method with excellent global optimization and rapid convergence. Empirical testing based on cross-sectional data from Chinese P2P lending market demonstrates the superiority of the improved hybrid kernel SVM model. The classification accuracy of credit risk level and operation quality is higher than the single kernel SVM model as well as the hybrid kernel model with empirical parameter values.

Developing a P2P lending platform: stages, strategies and platform configurations

PurposeOnline Peer-to-Peer (P2P) lending platforms are becoming increasingly popular globally in recent years. Our knowledge of how to develop and manage the digital platforms that make P2P lending possible, however, is limited. Through an in-depth examination of the strategies deployed and actions taken across the various stages of development of Tuodao, one of the most successful online P2P lending platforms in China, the purpose of this study is to develop a process model of P2P Lending Platform Development to address this knowledge gap.Design/methodology/approachThe case research method was adopted for this research, and a total of 16 informants were interviewed. The informants were composed of representatives of Tuodao’stop management, organizational IT functions as well as its various business units.FindingsOur study reveals that the development of a P2P lending platform can unfold in a specific sequence across three stages, and the development of a particular side of the platform should be emphasized in each stage (i.e. Partners, followed by Lenders, and then Borrowers). Each stage is also distinctive in terms of their strategies and platform configuration outcomes, which are elaborated on in our paper.Originality/valueOur process model contributes an in-depth view of how P2P lending platforms should be established and nurtured to complement the existing studies in this rapidly growing research area. In addition, our study also hints at the strategies that can facilitate the various stages. Our model can potentially serve as the foundation for formulating guidelines for the managers of P2P lending platforms, so that they are able to optimize the development of their platforms and extend the benefits of P2P lending to a broader base of customers.

The impact of social capital on economic attitudes and outcomes

This article traces the extant literature on the impact of social capital on economic attitudes and outcomes. Special attention is paid to clarify conceptual ambiguities, measurement techniques, channels of influence, and identification strategies. Insights derived from the literature are then used to analyze the marketplace lending industry in China, where the size of the peer-to-peer (P2P) lending market is larger than that of the rest of the world combined. Ironically, approximately two-thirds of these online P2P lending platforms have failed. Empirical evidence from the monthly operating data of 735 lending platforms and transaction level data from one prominent platform (Renrendai) shows that platforms in provinces with high social capital have low risk of failure, and borrowers in provinces with high social capital can borrow at low interest rate and are less likely to default. We also provide observations to guide future economic research on social capital.

How do reputation, structure design and FinTech ecosystem affect the net cash inflow of P2P lending platforms? Evidence from China

Based on daily data of 749 active online P2P lending platforms in Chinese market, this study explores the key factors affecting the net cash inflow rate of the platform which is vital for its operation and survival from the perspective of reputation, structure design and FinTech ecosystem. Internal governance issues of P2P lending platforms are further discussed according to the model results. A positive U-shaped relationship has been found between the platform duration and its net cash inflow rate which proves the role of reputation in the long-term development of P2P lending platforms. In addition, we demonstrate that both capital and operational structure design of the platform (e.g. shareholders background, credit assignment, trusteeship and guarantee) have a significant impact on the platform’s net cash inflow rate. The cash flow level of the platform has also been affected by the regional FinTech ecosystem. Platforms in a medium-developing ecosystem may have the highest net cash inflow rate, while a backward ecosystem will lower the cash flows of the platforms located in this area on average. Some suggestions on cash flow management and internal governance of P2P lending platforms for both platform founders and governments are put forward in the end of the study.

Can Social Notifications Help to Mitigate Payment Delinquency in Online Peer-to-Peer Lending?

Payment management is an operations management challenge in online peer-to-peer lending. It is critical to control the cost of debt collection incurred in late repayments and defaults. We study whether and how a platform can leverage on borrowers’ social connections and use automatic social notifications in regulating repayment behavior. In collaboration with a large online P2P lending platform, we conduct a randomized field experiment to investigate the effect of social notifications targeted at different contact groups (core-circle and peripheral-circle groups). Our results indicate that notifying social contacts of a delinquent regarding the overdue payment significantly improves the repayment rate. Compared with the control group in which no notification messages are sent to social contacts, notification-triggered social sanctions and social support reduce the default rate by more than 50% in both core-circle and peripheral-circle groups. Furthermore, we find that social notifications targeted at peripheral-circle social contacts are only effective in the short term, and its effectiveness decreases with repeated use. By contrast, social notifications targeted at core-circle social contacts have a lasting effect.

Understanding Lenders' Investment Behavior in Online Peer-to-Peer Lending: A Construal Level Theory Perspective

Online peer-to-peer lending (i.e., P2P lending) has grown rapidly in recent years and is a new source of fixed income for investors. However, we have a rather limited understanding of factors affecting lenders’ decision making in this context, which is characterized as high risk and prosocial in nature. In our research, drawing upon incentive theory of motivation and construal level theory (CLT), we theorize how interest rate and psychological distance caused by the borrower’s demographic attributes (i.e., geographic location and educational level) relative to those of the lender jointly affect the bidding value submitted by the lender. Using a rich data set from a popular online P2P lending platform in China, we apply multiple identification strategies and estimation methods to conduct the analysis. We find that interest rate is the driving factor for the lender’s bidding value on a loan listing and that such positive effects are strengthened by the geographic and educational distance between the lender and the borrower. In addition, geographic distance decreases the lender’s bidding value on a loan listing (i.e., home bias effect), whereas educational distance increases the bidding value (i.e., educational distance effect). Theoretical contributions and practical implications are discussed.

Research on Tax Collection and Management of Internet Finance --Illustrated by the Example of P2P Online Lending Platform

Peer-to-Peer (P2P) online lending platform is an important field for Internet Finance tax administration. In this paper, a series of related problems are brought out, including laws and regulations lagging behind economic development, weak consciousness of individual self-declaration, opaque data on P2P platforms, difficulty in defining regulatory bodies, and irregular accounting procedure. To this end, the researchers put forward following suggestions: establishing a sound real-name transaction system and registration system; establishing an information-sharing mechanism with financial regulatory agencies and credit rating agencies; defining P2P online lending platforms as individual income tax withholding agents; standardizing the accounting procedure of the platforms’ current funds; applying the blockchain technology to electronic invoice management.

Impacts of Venture Capital on Online P2P Lending Platforms: Empirical Evidence from China

ABSTRACT We examined the impacts of venture capital investment (VCI) on the performance of online Peer-to-Peer (P2P) lending platforms. The research results show that: (1) Gaining the first round of VCI can increase the transaction scale and improve the compliance with regulatory requirement of the online P2P lending platforms; and (2) the platforms acquiring more rounds of VCI have greater turnover. The above research shows that under the background of information asymmetry, the certification function and monitoring mechanism of VCs can work in online P2P lending markets.