The Role of Social Capital in People-to-People Lending Marketplaces

The objective of this paper is to investigate the role of social capital in for-profit People-to-People (P2P) lending marketplaces such as Prosper, the largest P2P lending marketplace in the US. We examine whether marketplace members (lenders, borrowers) are able to capitalize on borrowers\u27 accumulated social capital. From a borrower\u27s perspective, we investigate the influence of social capital on borrowers\u27 chances to obtain funding and better interest rates in general as well as by borrower groups and over time. From a lender\u27s perspective, we investigate the influence of borrowers\u27 social capital on loan payment. We use data over a time span of two and a half years from Prosper, and analyze more than 200,000 loan requests and 27,500 loans. Our results suggest that social capital does not provide equal benefits to all members of Prosper and that mechanisms to promote social capital should be carefully designed

People-to-People Lending: The Emerging e-Commerce Transformation of a Financial Market

This paper provides an overview of the concept of people-to-people (P2P) lending, a relatively new e-commerce phenomenon that has the potential to radically change the structure of the loan segment of the financial industry. P2P lending creates a marketplace of individuals and a social fabric through which these individuals interact. It provides efficient information transfer, thus perhaps creating more perfect markets. P2P lending requires information systems support to make it function, and to provide a social network mechanism that may be crucial for its success. We discuss different P2P lending marketplace models, and how information systems support the creation and management of these new marketplaces, and how they support the individuals involved. We conclude by providing some important research questions and directions, and issues for which further investigation is called

Credit Risk Management of P2P Network Lending

This article first studies the literature of P2P online loans, including online loans, credit risk factors and models, and summarizes the current status of P2P and credit risk assessment management in China. Based on the loan data of domestic P2P lending platforms, this paper conducts an empirical study on credit risk assessment. This study uses random forest importance assessment and logistic regression classification for credit risk assessment to identify loan targets with higher probability of default and improve overall loan quality. This research used 10,930 loan data, based on 26 fields, and finally selected 20 model variables to participate in credit risk quantification through feature structure and feature analysis. The final modelling test results show that the model screening accuracy rate is 73.3%, indicating that this model has a good performance in the credit risk quantification of borrowers

Analysis on the Risk and Supervision of P2P Online Financing Platforms in China

Microcredit is a vital breakthrough to solve the financial problems of low-income groups and small and medium-sized enterprises, while traditional microfinance providers can only meet a small proportion of their capital needs. By using internet technology, P2P online financing extends the innovative development of microcredit with the aim of solving traditional micro-credit problems. This paper mainly explores the existing online financing operation model of P2P in China, and summarizes the relevant problems, such as low entry barriers for P2P online financing enterprises and lack of supervision, Lack of verification on the qualification of borrowers and poor management of the platform, imperfect information revealed or providing false information by platform, etc. Finally, the article put forward some suggestions concerning the healthy development for the P2P online financing platform, including the establishing entry audit system and strengthening the supervision of the P2P platform, strengthening the management of borrowers and improving the credit collection system, and strengthening the disclosure of information by platform

Credit risk prediction in an imbalanced social lending environment

© 2018, the Authors. Credit risk prediction is an effective way of evaluating whether a potential borrower will repay a loan, particularly in peer-to-peer lending where class imbalance problems are prevalent. However, few credit risk prediction models for social lending consider imbalanced data and, further, the best resampling technique to use with imbalanced data is still controversial. In an attempt to address these problems, this paper presents an empirical comparison of various combinations of classifiers and resampling techniques within a novel risk assessment methodology that incorporates imbalanced data. The credit predictions from each combination are evaluated with a G-mean measure to avoid bias towards the majority class, which has not been considered in similar studies. The results reveal that combining random forest and random under-sampling may be an effective strategy for calculating the credit risk associated with loan applicants in social lending markets

LENDING ROBOTS AND HUMAN CROWDS: INTEREST RATE DETERMINATION ON A REVERSE AUCTION PLATFORM

We analyze the determinants of the level of interest rates related to business loans traded on digital crowdlending platforms. We consider one of the leading platforms in France and collect-ed original data on all the projects financed via this platform. On that platform, interest rates are set by the crowd of investors through a reverse auction process. We show that the loan characteristics and the scoring provided by the platform significantly influences the interest rate. However, though financial ratios are used traditionally to estimate credit risk, those ratios do not exhibit significant influence. Besides, we analyze the impact of the recent implementation of an automated auction mechanism. This implementation seems to have a large impact on both auction duration and on the determinants of interest rate. This suggests that use of a robot im-pacts on price and saving allocation on this platform-based credit market

A Research on the Model of Network Lending Platform in China

In 1974, Professor Yunus has built microfinance institutions to alleviate poverty in Bangladesh, soon the microcredit model has gradually expanded around the world. In recent years, with the continuous innovation and development of the Internet and Mobile Internet, a variety of online platforms are growing rapidly and widely, As the economic and the continuous progresses on network technology have developed rapidly, Internet technology has quickly melt into the financial industry, Internet network finance has raised as a combination of traditional financial and Internet.P2P lending is one of the most important models of Internet banking, and it is getting more and more people\u27s attention and recognition. "P2P" originated in the IT technology, referred to the Internet transmission protocol. In this Internet transmission agreement, both data download sides and data provider sides are individuals, the more number of downloads and more download points available, the download speed is also quicker. In the field of microfinance, "P2P" is a short for "peer-to-peer" lending. In the ideal Internet financial environment, The main information of transaction is transparent, P2P lending platforms can use Internet to spread information faster. In addition, P2P lending also has the features of flexibly, short-term of loaning, small amount of lending and borrowing. In a word, P2P lending has improved the utilization of idle funds. Compared with traditional bank loans, it can reduce the financing costs and facilitate personal financing.In 2005, the world\u27s first P2P lending platform Zopa was born in the UK, then Lending Club, Kiva and other P2P platforms establish quickly. China\u27s first P2P online lending platform Pat Pat loan (拍拍贷 built) in Shanghai on 2007. P2P online lending platforms in different countries face different problems on development and growth. It cannot be denied that the development of P2P platforms has produced a larger society benefit, but also brought some problems. To prevent P2P lending risk, the government should clear who to regulate this industry and to build up the regulatory system. P2P lending platforms themselves should not only improve the network technology to reduce technology risk, but also clear financial responsibilities to strengthen control of the operational process.The goal of this research are: (1) to summarize the typical P2P platforms and their operating models, and (2) to solve the financing difficulties of SMEs and giving practical suggestions to financial Innovation of P2P platforms in China

Can Loan Descriptions Predict the Funding Probability in a Peer to Peer Lending Platform?

The purpose of this empirical study is to determine if the orthographic quality of the description of a loan\u27s purpose predict the total funding probability in a peer to peer lending platform. I hypothesize that the data I collect will show a negative correlation between spelling errors in loan descriptions and probability that the entire loan will be funded

Decisions under Uncertainty in Decentralized Online Markets: Empirical Studies of Peer-to-Peer Lending and Outsourcing

Recent developments in information technologies, especially Web 2.0 technologies, have radically transformed many markets through disintermediation and decentralization. Lower barriers of entry in these markets enable small firms and individuals to engage in transactions that were otherwise impossible. Yet, the issues of informational asymmetry that plague traditional markets still arise, only to be exacerbated by the "virtual" nature of these marketplaces. The three essays of my dissertation empirically examine how participants, many of whom are entrepreneurs, tackle the issue of asymmetric information to derive benefits from trade in two different contexts. In Essay 1, I investigate the role of online social networks in mitigating information asymmetry in an online peer-to-peer lending market, and find that the relational dimensions of these networks are especially effective for this purpose. In Essay 2, I exploit a natural experiment in the same marketplace to study the effect of shared geographical ties on investor decisions, and find that "home bias" is not only robust but also has an interesting interaction pattern with rational decision criteria. In Essay 3, I study how the emergence of new contract forms, enabled by new monitoring technologies, changes the effectiveness of traditional signals that affect a buyers' choice of sellers in online outsourcing. Using a matched-sample approach, I show that the effectiveness of online ratings and certifications differs under pay-for-time contracts versus pay-for-deliverable contracts. In all, the three essays of my dissertation present new empirical evidence of how agents leverage various network ties, signals and incentives to facilitate transactions in decentralized online markets, form transactional ties, and reap the benefits enabled by the transformative power of information technologies

Default Prediction of Internet Finance Users Based on Imbalance-XGBoost

Fast and accurate identification of financial fraud is a challenge in Internet finance. Based on the characteristics of imbalanced distribution of Internet financial data, this paper integrates machine learning methods and Internet financial data to propose a prediction model for loan defaults, and proves its effectiveness and generalizability through empirical research. In this paper, we introduce a processing method (link processing method) for imbalance data based on the traditional early warning model. In this paper, we conduct experiments using the financial dataset of Lending Club platform and prove that our model is superior to XGBoost, NGBoost, Ada Boost, and GBDT in the prediction of default risk