Lending Platforms In China Research Articles

Credit Anomaly Detection Method based on Bayesian Networks

Due to the virtual nature of online transactions and the underdeveloped personal credit risk assessment system of Internet finance lending platforms in China, these platforms often struggle to verify the creditworthiness of applicants prone to fraud and default, leading to loan losses. As a result, risk control has become the core of Internet lending platforms. Modern risk control relies on statistical and data mining techniques to analyze and model data, uncover patterns that indicate loan defaults, and identify anomalies. This paper proposes a Bayesian network-based credit anomaly detection method, which detects anomalies by calculating and ranking the joint anomaly probabilities of sample instances. The technique operates under unsupervised learning and can effectively handle missing and imbalanced data. This method analyzes a loan credit dataset with 32,581 samples and 12 features of the customers. The result of the analysis demonstrates the feasibility and effectiveness of this method in detecting anomalies in online loans.

Do P2P borrowers improve the quality of information disclosure? An analysis with text mining on loan descriptions

AbstractMost of peer‐to‐peer (P2P) online borrowers are small business managers. The learning behavior of borrowers in the P2P market is rarely studied. The aim of this paper is to identify the existence of borrowers' learning behavior in the P2P market using a large sample from renrendai.com, which is one of the largest P2P lending platforms in China. The loan description written by the borrower is an important way to disclose the borrower's information. We analyze changes in loan descriptions in multiple borrowings with text mining techniques and investigate whether a borrower has a learning behavior in writing loan descriptions. Empirical results show that after accumulating enough experience, borrowers can optimize the loan description to make it easier to obtain loans at lower interest rates.

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.

Media News and Social Media Information in the Chinese Peer-to-Peer Lending Market

This paper uses supervised machine learning (sentiment analysis) to analyze the sentiments of social media information in the P2P lending market. After segmentation, filtering, feature word extraction, and model training of the text information captured by Python, the sentiments of media and social media information were calculated to examine the effect of media and social media sentiments on default probability and cost of capital of peer-to-peer (P2P) lending platforms in China (2015–2019). We find that only positive changes in media and social media sentiment have significantly negative effects on the platform’s default probability and cost of capital, while negative changes in sentiment do not have any effects. We conclude the existence of an asymmetric effect of media and social media sentiments in the Chinese peer-to-peer lending market.

Big data pricing in marketplace lending and price discrimination against repeat borrowers: Evidence from China

In this work, we systematically investigate the pricing mechanism change from auction to big data pricing on one of the major marketplace lending platforms in China. We find that big data pricing reduces the average interest rate while the borrowers with delinquency or default histories are assigned higher interest rates. However, repeat borrowers are also faced with growing interest rates, even though they have been paying their debts on time. Further analysis shows that repeat borrowers have lower income and education levels. Moreover, investor returns become less dispersed after pricing with big data, which can be a result of homogeneous loans on the market. The implications of the above findings are discussed.

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.

The crowding-out effect of formal finance on the P2P lending market: An explanation for the failure of China's P2P lending industry

Using the credit records of the borrowers from a peer-to-peer (P2P) lending platform in China, this paper finds that P2P borrowers from cities with broader coverage of formal finance are more likely to default. Moreover, as formal finance develops, high-quality borrowers become less likely to turn to P2P lending, which suggests that formal finance exerts a crowding-out effect on the P2P lending market. This paper provides an explanation for the failure of China's P2P lending industry, implying that Fintech intended to promote financial coverage, if not reducing information asymmetry, does not necessarily lead to desirable economic and social outcomes.

Borrow low, lend high: Credit arbitrage by sophisticated investors

We document that some investors arbitrage by borrowing and lending at the same time on Renrendai, one of the largest peer-to-peer lending platforms in China. They borrow at an annual interest rate of 7.31% and lend at 12.53% to earn the spread. Compared with non-arbitrageurs, arbitrageurs have larger portfolios and they diversify default risk by lending smaller amounts to many borrowers. Moreover, arbitrageurs are able to identify quality loans with lower risk and higher return, and thereby earn 1.19% higher risk-adjusted return than non-arbitrageurs do. As a result, 706 arbitrageurs earn 1.5 million yuan in 3 years. However, unsuccessful investments Granger cause some arbitrageurs to default, suggesting that arbitrageurs may increase platform risk. After the platform employ big data and reform the loan pricing mechanism from borrower-determined to platform-determined, loan interest rate decreases from 12.49% to 10.15%, and arbitrage phenomenon no longer exists. These findings suggest that arbitrageurs are highly sophisticated investors who make profits by utilizing financial leverage, diversifying credit risk, and identifying quality loans. Meanwhile, regulators should closely monitor arbitrage behaviors and constantly evaluate their consequences.

Government Affiliation and Peer-To-Peer Lending Platforms in China

With thousands of co-existing and competing platforms, the Chinese peer-to-peer (P2P) lending market experienced both high growth and high failure rate. We hand collect unique data for these P2P platforms and investigate the differences in performances and survivals for platforms with and without affiliations with state-owned enterprises (SOEs). P2P platforms with SOE affiliations have higher trading volumes, attract more investors, and offer lower interest rates. These platforms also survive significantly better than those without the SOE affiliations, especially during market downturns. Using P2P platforms with fake SOE affiliations as identification, we show that the SOE affiliation itself (not related to the fundamentals) is an important signal for P2P market participants. These results can be helpful to investors and regulators, especially those from other emerging markets.

Efficiency and Effect of Regulatory Policies on the Online Peer-to-peer (P2P) Lending Industry

ABSTRACT We investigated the influential power of regulatory policies on 463 peer-to-peer lending platforms in China to understand the effectiveness of their regulatory policies. Our results showed that the influence of regulatory policies varies across platforms. Notably, 1) the platforms controlled by the listed firms face more difficulties than others with strict supervision; 2) the platforms with little registered capital and a shorter period of establishment are more susceptible to regulatory policies; and 3) the platforms with a short duration of debt transfer have a greater risk of bank runs and serious fluctuations in trading volume due to the policy release.

Business Friendliness: A Double-Edged Sword

In this paper, we test the hypothesis that business-friendly local-government policies combined with weak legal institutions lead to lower economic welfare in the form of greater fraud activity. Using data of almost 3000 failed peer-to-peer (P2P) lending platforms in China, labeled as “runaways”, we find that they are more prevalent in provinces with business-friendly policies with weak law-enforcement regimes.

The geography of the emergence of online peer-to-peer lending platforms in China: an evolutionary economic geography perspective

ABSTRACT This article discusses the geography of the emergence of online peer-to-peer (P2P) lending platforms in China. Moving beyond conventional approaches in financial geography, we propose a new analytical framework drawing on evolutionary economic geography. We find that China’s P2P industry is highly concentrated in Beijing, Shenzhen, Shanghai, Hangzhou, and Guangzhou. These cities have possessed competent financial and Internet sectors before the emergence of P2P industry, and thus provide capabilities that P2P industry could build on. Moreover, experienced entrants, namely new firms founded by pre-existing firms and their employees from technologically related industries, play a crucial role in transferring capabilities from pre-existing financial and Internet capabilities into the P2P industry. In addition, the emergence of the P2P industry experienced three stages characterized by constraining environments with uncertainty coming from customers (2007–2012), enabling environments with inclusive regulations (2013–2015), and constraining environments with tight regulations (2016–2019).

Ensemble methods for credit scoring of Chinese peer-to-peer loans

Risk control is a central issue for Chinese peer-to-peer (P2P) lending services. Although credit scoring has drawn much research interest and the superiority of ensemble models over single machine learning models has been proven, the question of which ensemble model is the best discrimination method for Chinese P2P lending services has received little attention. This study aims to conduct credit scoring by focusing on a Chinese P2P lending platform and selecting the optimal subset of features in order to find the best overall ensemble model. We propose a hybrid system to achieve these goals. Three feature selection algorithms are employed and combined to obtain the top 10 features. Six ensemble models with five base classifiers are then used to conduct comparisons after synthetic minority oversampling technique (SMOTE) treatment of the imbalanced data set. A real-world data set of 33 966 loans from the largest lending platform in China (ie, the Renren lending platform) is used to evaluate performance. The results show that the top 10 selected features can greatly improve performance compared with all features, particularly in terms of discriminating bad loans from good loans. Moreover, comparing the standard

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.

Crazy gamblers or cautious investors? Evidence from a peer‐to‐peer market in China

AbstractBy data of 10,357 individual investors from Renrendai.com, an established peer‐to‐peer (P2P) lending platform in China over the period of 2011–2014, we examine whether investors’ prior investment outcomes influence their subsequent risk‐taking behavior in the credit market. We find strong evidence that a prior trading loss induces greater subsequent risk‐taking. Specifically, investors decrease their number of bids and the bidding amount; and choose listings with a lower interest rate and higher credit grade. The investors who obtain more prior gains become more cautious and take on less posterior risk. Overall, this study complements the lack of relevant research in the credit market.

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.

Seeking excess returns under a posted price mechanism: Evidence from a peer‐to‐peer lending market

AbstractThis study examines the performance of a new online peer‐to‐peer (P2P) lending platform in China that relies on non‐expert individuals to screen for loans. Using the bank deposit rate as a benchmark, positive excess returns exist under the posted price mechanism, which indicates that P2P markets provide lenders with adequate profit opportunities to compensate for investment risks. Moreover, we find that loans with higher excess returns are more likely to be funded and are bid on more quickly than other loans. Finally, voluntarily disclosed soft information in the listing's description plays a significant moderating role in the lenders’ decision‐making process. Borrowers who promise to repay on time are more likely to be funded and to be funded faster, but those who claim economic hardship have a lower probability of being funded. Our results provide evidence that lenders have the ability to seek excess returns in P2P lending markets and highlight that aggregating the views of peers can improve the market efficiency.

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.