P2P Lending Platforms In China Research Articles

Peer-to-Peer Loan Fraud Detection: Constructing Features from Transaction Data

Although financial fraud detection research has made impressive progress because of advanced machine learning algorithms, constructing features (or attributes) that can effectively signal fraudulent behaviors remains a challenge. In recent years, a new type of fraud has emerged on peer-to-peer (P2P) lending platforms, where individuals can borrow money from others without a financial intermediary. In these markets, the information asymmetry problem is seriously elevated. Inspired by the fraud triangle theory and its extensions, and using the design science research methodology, we construct five categories of behavioral features directly from P2P lending transaction data, in addition to the baseline features regarding borrowers and loan requests. These behavioral features are intended to capture the fraud capability, integrity, and opportunity of fraudsters based on their loan requests and payment histories, connected peers, bidding process characteristics, and activity sequences. Using datasets from real users on two large P2P lending platforms in China, our evaluation results show that combining these additional features with the baseline features significantly enhances detection performance. This design science research contributes novel knowledge to the financial fraud detection literature and practice.

Naïve or sophisticated? Information disclosure and investment decisions in peer to peer lending

Despite the explosive growth of peer-to-peer lending in China, information asymmetry remains a critical issue and is likely to be amplified in such an evolving credit market compared to a traditional credit market. This paper studies how investors screen the nonstandard and often unverifiable information disclosed voluntarily by the borrowers to make their investment decisions. Using data from Renrendai, one of the leading P2P lending platforms in China, we find that the amount of information disclosed voluntarily by the borrowers can significantly improve the funding probability. The impact is even more remarkable for the borrowers with lower credit rating. However, the loan default probability increases with the amount of disclosure, indicating the possibility of information manipulation by the borrowers. Further investigation shows the puzzle that lenders remain attracted by such loan listings can be explained by the higher profitability offered by the borrowers. These findings imply the necessity of regulation on the information disclosure in the P2P lending

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.

VC Participation and failure of startups: Evidence from P2P lending platforms in China

We investigate how VC participation affects the failure of startups. Using a unique dataset of the survival of peer-to-peer (P2P) platforms in China, we identify two types of failures, bankruptcy, and run off with investors’ money. The Competing Risk Model results show that while VC participation reduces bankruptcy hazard, it has little impact on the runoff failures. The findings are robust to the use of matched subsamples that disentangle the influence of pre-investment screening by VC. Further analysis of exit routes reveals that conditional on failure, VC participation is associated with a higher chance of running for the exit.

Identifying the Influencing Factors on Investors’ Investment Behavior: An Empirical Study Focusing on the Chinese P2P Lending Market

Our study explores the factors influencing investors’ behavior in the peer-to-peer (P2P) lending market in China, and the relationships among them from the perspective of investors. The primary component analysis method was used to divide the P2P lending platforms into five categories. Then, a structural equation model was applied to analyze the interrelationship. Our results show that more exceptional operating ability, profitability, and security of the platform help to improve investor’s investment behavior. Operation ability is the most significant influencing factor, which also influences other factors to different degrees. After the analysis of the results, we found that the security degree of P2P lending platforms in China differs, and the risk due to the lack of bank depository for platforms is the most serious. In terms of the background, investors are less interested in the state-owned platforms compared to the bank- or listed-company-owned platforms, although the background strength of the state-owned platforms is more powerful.

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.

Will CEOs with banking experience lower default risks? Evidence from P2P lending platforms in China

Using a novel dataset of 121 Chinese P2P lending platforms, we investigate the impact of CEOs’ banking experience on default risk. The empirical results indicate that CEOs with prior banking experience manage default risk better. Moreover, CEOs’ banking experience has a stronger influence on small platforms and in situations where the platforms’ depository banks are city commercial banks. Our results indicate that although fintech provides technology to reduce risks, we cannot ignore the constructive role of professional experience in risk management. Thus, investors and regulators in emerging markets should pay attention to managers’ financial qualifications, and especially to CEOs’ banking experience.

Venture capital certification and customer response: Evidence from P2P lending platforms

This paper examines whether the certification effect of VCs extends to firm's potential customers, and whether, by certifying firms' values to potential customers, VCs provide value to firms. Using weekly trading data from P2P lending platforms in China, we find that the amount of loans facilitated and the number of lenders increase significantly by 25.7% and 49.3%, respectively, immediately after announcements that P2P lending platforms obtain VC investment. We find that this certification effect increases with measures of VC reputation and measures of information asymmetry between P2P lending platforms and potential customers. A difference-in-differences analysis provides consistent results, which are robust after controlling for the effects of news, advertising, and funding. We also document that VC-backed platforms are less likely to default than non-VC-backed platforms. This result provides indirect evidence that VC backing has long-term benefits beyond the news and advertising effects.

The Bilateral Effects of Platform-Sponsored Collateral in Peer-To-Peer (P2P) Lending: Evidence from China

ABSTRACTUsing a unique large dataset collected from one of the oldest and largest P2P lending platforms in China, namely PPDAI, this article tests the bilateral effects of a special type of platform-sponsored collateral on lenders and borrowers. Both theoretically and empirically, we find that smart lenders disaggregate the surety and riskiness-signaling effects of such collateral. Our evidence indicates that lenders in China’s P2P market are smart: they anticipate that platform-collateralized loans are riskier than the non-collateralized ones in the second or higher rounds of fundraising. For the borrowers, platform-sponsored collateral increases the overall borrowing capacity owing to its surety effect. Therefore, combining the two aspects, platform-sponsored collateral increases the overall efficiency of the P2P lending market given the safety of the platform per se.

Venture Capital Certification in Product Market: Evidence from P2p Lending Platforms

This paper examines whether the certification effect of VCs extends to firm’s potential customers, and whether, by certifying firms’ values to potential customers, VCs provide value to firms. Using weekly trading data from P2P lending platforms in China, we find that the amount of loans facilitated and the number of lenders increase significantly by 25.7% and 49.3%, respectively, immediately after announcements that P2P lending platforms obtain VC investment. We find that this certification effect increases with measures of VC reputation and measures of information asymmetry between P2P lending platforms and potential customers. A difference-in-differences analysis provides consistent results, which are robust after controlling for the effects of news, advertising, and funding. We also document that VC-backed platforms are less likely to default than non-VC-backed platforms. This result provides indirect evidence that VC backing has long-term benefits beyond the news and advertising effects.

Does automatic bidding mechanism affect herding behavior? Evidence from online P2P lending in China

Recently, online P2P lending has become one of the typical modes of Internet finance. Its automatic bidding mechanism greatly improves transaction efficiency and saves transaction time. We study the herding behavior under the environment of automatic bidding mechanism through panel data regression on the transaction information from the PaiPaiDai, one of the largest P2P lending platforms in China. We find that herding behavior exists in online P2P lending. The automatic bidding mechanism is able to weaken herding effect and presents a rational herding behavior. Its behavior is very similar to those real variables, such as loan amount and loan period.

How Incentive Structure Affects the Effectiveness of Referral Strategy: Evidence from a Randomized Field Experiment on a Lending Platform

Large and high quality user base is an important determinant of firm performance. Firms spend a great amount of resources in building referral programs to grow user base. Yet, substantial variations exist in the effectiveness of these referral programs with various incentive structures. We argue that the extent to which referral programs can grow user base depends on how existing users evaluate the cost and benefit of their referral behavior. Using a large-scale randomized field experiment with around one hundred thousand participants on one of the largest P2P lending platforms in China, we find that referral programs structured as self-benefit (egoistic) appeal or other-benefit (altruistic) appeal significantly reduce the number of new users brought by the existing users but new users from other-benefit appeal made more investment. Our study demonstrates that under certain circumstances, these referral programs structured as egoistic or altruistic incentive structure may backfire.

The role of punctuation in P2P lending: Evidence from China

This paper investigates the role of punctuation in the peer-to-peer (P2P) lending market. Using data from Renrendai, one of the largest P2P lending platforms in China, we investigate how the amount of punctuation used in loan descriptions influences the funding probability, borrowing rate, and default. The empirical evidence shows that the amount of punctuation is negatively associated with the funding probability and borrowing rate. We propose that the usage of punctuation affects the readability of a loan description and reflects borrowers’ self-control and cognitive ability. Within a given number of words, excessive usage of punctuation makes loan description informal and reduces the readability of the text, thereby impairing investors’ trust in borrowers. Moreover, borrowers that overuse punctuation may have lower ability of self-control, and tend to underestimate the risk of borrowing and offer lower borrowing rate due to overconfidence.

Influencing Factors of Online P2P Lending Success Rate in China

The low success rate of lending is the main drawback of development of online P2P lending platforms in China. Based on the theory of social capital, this study analysed the influence factors of success rate of P2P lending platform in China, using social network method and multiple linear regression model. Soft information, such as bidding record, has been creatively employed to study the corresponding topics. Data used in this study comes from the largest online P2P lending platform in China. The results show that: compared with other influence factors, the bidding record has a more significant effect on the success rate, and the users depend more on the social capital; the bidding records reduce the asymmetry of information, and help increasing the success rate of lending and decreasing the cost of online P2P lending.

Investments Under Fast-Thinking: Evidence from Peer-to-Peer Lending

Investment opportunities in the online peer to peer (P2P) lending market often disappear in a few minutes. It provides an ideal laboratory for testing the hypothesis that people are liable to make mistakes under fast-thinking (Kahneman 2011). Using one of the leading P2P lending platforms in China, Renrendai, we show that investors tend to pay too much attention to high interest rates and too little attention to high default risks under fast-thinking. Such biases in decision making result in higher default rates and poorer returns than expected. We further show that investors learn over time to convert from System 1 to System 2 thinking.

Risks of P2P Lending Platforms in China: Modeling Failure Using a Cox Hazard Model

P2P lending platforms in China have risen since 2006 but have already experienced problems with fraud and liquidity. In this article, we describe the P2P lending platforms and their associated risks, and discuss and analyze a dataset on failed and nonfailed P2P companies. We find that an increase in the registered capital results in a decrease in the hazard ratio, while an increase in the interest rate results in an increase in the hazard ratio. We discuss policy implications for the P2P lending sector, which can help to reduce risk in the sector while allowing innovation to arise.