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Screening in New Credit Markets: Can Individual Lenders Infer Borrower Creditworthiness in Peer-to-Peer Lending?

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ABSTRACT : This paper looks at how well Finland performs in high growth entrepreneurship and uses data from the Global Entrepreneurship monitor to benchmark Finland against other European countries. It is found that Finland’s prevalence rate of high growth entrepreneurial activity lags significantly behind most of its European and all of its Scandinavian peers. That this weak performance in high-growth entrepreneurship goes hand in hand with Finland being a world leader in per capita investment in R&D may be described as a paradox. The reasons underlying the underperformance of Finland remain however unclear. At this point, explanations should be sought in culture, industrial traditions and systemic experience in high growth entrepreneurship.

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... Fortunately, with the accumulation of large-scale user behavior data in P2P lending platforms, many data-driven studies focusing on risk evaluation [3] [4] [5] [6] [7], fundraising analysis [8], and lending or bidding behavior [9] [10] have been conducted. For example, a regression model was adopted by Iyer et al. [11] in order to evaluate whether lenders in the P2P lending market can use the borrower information to determine the creditworthiness on prosper data. Especially, Zhao et al. [12] not only used a regression model to evaluate the risk of loans but also integrated the loan risk into the recommendation system that inspired our research. ...
... We can obtain p u by u's id and q i by i's id (i ∈ S t ) and input the detailed information of i into Equation (15) in order to obtain r i . Further, the three values obtained above can be fed into the already trained model (11), and the probability of the investment behavior of the investor u for the loan i can be obtained. We perform the above operations on each element in S t to obtain their values and sort the values to obtain the top-K recommendation list L(k). ...
... The SeIMF model is used to update p u and q i on time to prepare the next recommendation task, once the investor has completed the investment. Due to the addition of new investment records, we again need to train the model (11), when the data expand to a certain extent and finally slide forward with the time window until the test is completed. ...
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With the development of the mobile Internet, a peer-to-peer(P2P) online lending platform has become increasingly popular in the financial market, and it attracts a massive number of users. The task that helps investors find potential loans for improving the funding success rate has become a major challenge for lending platforms. However, in contrast to the traditional recommendation problem, the challenges, such as the timeliness of loans (i.e., when a loan funding is completed or expired, it will no longer recruit investment), the common cold start problem (continuously releasing new loans is a common phenomenon), and the loans’ potential default risk, indicate that the traditional recommendation schemes are no longer suitable. Considering the above characteristics, we propose a deep learning model based on a sequence of the incremental matrix factorization technology (DeepSeIMF). First, the cold start problem of loans can be effectively solved by designing an incremental matrix factorization model based on the time series. Then, a neural network is used to provide investors with personalized investment recommendation services based on risk assessment. Finally, the model performance is systematically evaluated based on a large-scale real-world dataset. The experimental results demonstrate the effectiveness of our solution.
... According to Iyer et al. (2009), the credit grade of a borrower assigned by the platforms is the most crucial factor that explain funding success. It is one of the first things that lenders use to evaluate the default risk of a loan. ...
... It is one of the first things that lenders use to evaluate the default risk of a loan. Higher credit grade (lower risk) loans are usually associated with higher funding success probability, even though the interest rates maybe lower (Iyer et al. 2009). De Roure et al. (2016) believe that P2P lending 3 RESEARCH METHODOLOGY 3.1 Hypothesis Development: ...
... Similar to any credit market, P2P lending also suffers from asymmetric information. It is a key theme in the literature of P2P lending and is most relevant through ex ante adverse selection (Iyer et al. 2009) and ex post moral hazard (Lin 2009). ...
Thesis
This paper analyses the factors that determine loan performance in online peer-to-peer lending. The basis of online peer-to-peer lending is to provide loans to individuals or businesses through online lending platforms that match lenders or investors with the borrowers. Unlike in traditional financial institutions, individual investors are the ones who assume the loan risk in peer-to-peer lending. These individual lenders suffer from a serious problem of information asymmetry. As a result, peer-to-peer lending platforms provide the lenders with various loan quality information along with assigned credit grades with a view to reducing the information asymmetry. By analysing 306,439 matured loans funded on the online peer-to-peer lending platform 'Lending Club' with binary logistics regressions, this study suggests that the platform-assigned credit grade is the most significant determinant of loan performance. In addition, loan amount, debt-to-income ratio, annual income, open credit lines, total credit lines and inquiries in the last 6 months were also found to be major determinants of loan performance.
... First, the financial information of borrowers has a great impact on the probability of funding success such as the borrowers' economic status, debt-to-income ratio, credit rating, and education level. (Chen et al., 2020;Greiner & Wang, 2010;Iyer et al., 2009). The borrower's credit rating is a decisive factor affecting funding success (Klafft, 2008). ...
... Information that is difficult to completely summarize in a numeric score is soft information (Liberti & Petersen, 2018). Iyer et al. (2009) divide borrowers' information into hard, verified information and soft, subjective (nonverified) information. Klafft (2008) analyzes the relationship between borrowers' appearance and the probability of funding success. ...
Article
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Most 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.
... Participants in crowdfunding projects are likely to have diverse motives. Iyer et al. (2009) demonstrate that participants consider hard financial facts to evaluate a project, and their actions are comparable to common banks or venture capital firms. Attractive rewards or returns (Gerber et al. 2012, Lambert and and the social and intrinsic motives of participants are also considered in their decision-making processes. ...
... The motivation to participate is driven more by economic value than the desire to support the club. These findings are comparable with those of the study by Iyer et al. (2009), which underlines the important role of economic facets in the decision making of crowdfunders. The members of the second segment can be called "price-sensitive crowdfunders". ...
Article
Due to the official regulatory credit screening procedures of Basel II and Basel III in Europe, credit is now more difficult to obtain. As a consequence, alternative financial mechanisms, such as crowdfunding, that focus on sports clubs’ supporters have become more important. The aim of the present study is to evaluate crowdfunding related to sports clubs using a choice-based conjoint analysis (CBCA) to detect project- and participant-related success factors in successful financing. Therefore, two fictitious crowdfunding projects with the offered return and the price are chosen as features and two German sports clubs – one ice hockey club and one football club – are selected for the analysis. Using segmentation techniques, the study also examines the types of crowdfunders and their preferences. The results show that the offered return and the price are the two most important features for potential crowdfunders. They prefer either a club-related return containing a certain economic value or the donation as representative of a more altruistic return. The findings also indicate that crowdfunding can be a financial instrument for both semi-professional and professional clubs. Keywords: crowdfunding, financial instrument, sports clubs, semi-professional clubs, professional clubs
... Hard credit information refers to this type of information that is provided and displayed by the P2P platforms including credit score, demographic information, loan purpose, and others. Credit score was identified as a major factor for loans to be funded (Iyer et al., 2009;Lin et al., 2012). Borrowers' demographic information (e.g., gender, age and race) also affected lending outcomes (Berger and Gleisner, 2007;Kumar, 2007). ...
... Soft information refers to the information that is fuzzy, hard-to-quantify about borrowers beyond hard credit information (Lin et al., 2012). Such information could be generated from social networks between borrowers and lenders (Collier and Hampshire, 2010;Iyer et al., 2009). Jin (2008, 2011) examined the impacts of social networks on mitigating information asymmetry. ...
... Hard credit information refers to this type of information that is provided and displayed by the P2P platforms including credit score, demographic information, loan purpose, and others. Credit score was identified as a major factor for loans to be funded (Iyer et al., 2009;Lin et al., 2012). Borrowers' demographic information (e.g., gender, age and race) also affected lending outcomes (Berger and Gleisner, 2007;Kumar, 2007). ...
... Soft information refers to the information that is fuzzy, hard-to-quantify about borrowers beyond hard credit information (Lin et al., 2012). Such information could be generated from social networks between borrowers and lenders (Collier and Hampshire, 2010;Iyer et al., 2009). Jin (2008, 2011) examined the impacts of social networks on mitigating information asymmetry. ...
... Of all these types of information, the credit rating is the most simple and intuitive one for lenders to use, because it explicitly reveals a borrower's default risk. Iyer et al. (2011) note that the credit score is the best available measure of the ex-ante default probability, but direct research on the impact of credit ratings on default in the P2P lending market is scarce. In evaluating default risk, Emekter et al. (2015) state that FICO credit scores and credit grades can effectively predict default by borrowers on the Lending Club. ...
... For example, in their study on Prosper.com, Iyer et al. (2011) evaluate the screening ability of lenders, and Lin et al. (2013) examine the effects of online friendships among borrowers on funding, interest rates, and the rate of default. Chen et al. (2018a) investigate the relationship between the education level and the default risk of borrowers on a Chinese P2P platform, Paipaidai. ...
Article
By investigating a Chinese peer-to-peer online lending platform, Renrendai, we find that the credit ratings of new borrowers do not accurately predict their default. Moreover, we find that on this platform the probability of default by new borrowers is 56%. These findings indicate that in China, in the absence of authoritative credit agencies, platforms’ assigning credit ratings themselves, not only induces high investment risk for lenders, but also high systemic risk for platforms since most of these platforms guarantee the loan principal. Our results might explain why over 86% of Chinese lending platforms experience operational difficulties.
... As for the risk of trading, the researchers focus on the factors that affect credit risk in the P2PL market and P2PL risk regulations. Lenders can evaluate one-third of the credit risk using both soft and hard information about borrowers (Iyer et al., 2009). In the study of the behavior of traders, the researchers mainly discuss herding behaviors. ...
... In terms of the risk of trading, Guo et al. (2016) focus on the factors that affect credit risk in the P2PL market and risk regulation. Lenders can evaluate one-third of the credit risk using both soft and hard information about borrowers (Iyer et al., 2009). A summary of the literature review is presented in Table 1. ...
Article
This study applied a multivariate panel Granger causality test to examine the causal relationship between peer-to-peer lending (P2PL) and bank lending (BL) in China’s eight major regions for the period from 2014M01 to 2019M12. The empirical results of this paper support evidence for the P2PL leading hypothesis in regions such as Jiangsu and Hubei while the BL leading hypothesis relationship supports the evidence for regions such as Zhejiang and Shanghai. In addition, there is an interactive causal relationship between P2PL and BL in a region such as Shandong. However, the result of a neutrality hypothesis supports three of these eight major regions (Guangdong, Beijing and Sichuan). The findings of this paper provide important policy implications for China’s eight major regions as well as business sectors in the banking industry for understanding and predicting market conditions.
... En este ambiente, es importante comprender cómo es que los prestamistas aprovechan la información disponible para tomar decisiones de financiamiento sólidas, con el afán de detectar aquellos prestatarios con posibilidades de incumplimiento. En este sentido, Iyer et al. (2009) e Iyer et al. (2016 evalúan si los prestamistas en estos mercados tienen la capacidad de utilizar la información del prestatario para inferir su solvencia crediticia. Confirman que los prestamistas en estos mercados tienen la capacidad de inferir un tercio de la solvencia crediticia de los prestatarios a partir de la información financiera, como las calificaciones de riesgo presentes en la plataforma. ...
Article
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El préstamo entre particulares o mejor conocido como préstamos de persona a persona (P2P) es un modelo de negocio esencial en las finanzas tecnológicas y una alternativa orientada al fomento de obtención de fondos para prestatarios de forma novedosa. Sin embargo, la posibilidad de que solicitantes que acceden a préstamos mediante este modelo de negocio no cumplan con sus obligaciones de pago es una preocupación importante para prestamistas e inversores. De este modo, el presente estudio tiene como objetivo examinar los enfoques bajo los que se ha analizado este modelo de negocio e identificar patrones y tendencias de esta temática a través de un análisis bibliométrico, con el apoyo de la herramienta Bibliometrix de R y VOSviewer. Se identificaron 7 enfoques de estudio y una parte esencial de los factores que pueden aumentar la probabilidad de incumplimiento. Los hallazgos de la bibliometría sugieren que las publicaciones principales provienen de Estados Unidos y China, de la University of Maryland y National Kaohsiung University of Science and Technology, como revista destacada se tiene a SSRN Electronic Journal, las plataformas mayormente analizadas son Lendingclub.com y Prosper.com, las técnicas estadísticas utilizadas principalmente son probit y regresión logística. Bin Gu se destaca por su producción y colaboración con otros autores. La coocurrencia de palabras clave muestra que peer to peer lending, default risk y fintech aparecen con mayor frecuencia. Finalmente, el mapeo temático indica que los temas screening, market inference, P2P lending, misreporting, China e information asimetry son tópicos principales.
... Identifying borrowers' information has dominated the theoretical study of this technique. Scholars have used empirical data from different P2P platforms to discover that basic borrower characteristics, such as gender, age, marital status, education, and race [12][13][14], financial characteristics [15,16], and linguistic characteristics [17,18] significantly affect borrowing. It is true that many of these factors affect P2P lending success, but P2P online lending companies differ significantly from traditional financial institutions in their business models, meaning lending decisions still mainly rely on explicit hard data such as asset and liability data. ...
Article
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BigTech credit has enhanced financial inclusion, but it also poses concerns with its boundaries. This article uses theoretical frameworks and numerical simulations to examine the risks and inclusiveness of technology-empowered credit services for “long-tail” clients. This research discovered that the discrepancy between the commercial boundaries of BigTech credit and the technical limitations of risk management poses a risk in BigTech credit. The expanding boundaries of BigTech’s credit business may mitigate the representativeness of the data, resulting in a systematic deviation of unclear characteristics from the training sample data, which reduces the risk-control model’s ability to identify long-tail customers and raises the risk of credit defaults. Further computer simulations validate these results and demonstrate that competition among various companies would expedite the market’s transition over the boundary in case of a capital shortage. Finally, this article proposes setting up a joint-stock social unified credit technology company with data assets as an investment to facilitate the healthy and orderly development of financial technology institutions.
... Apparently, for FinTech users, financial literacy is often a critical resource. Iyer et al. (2009) disclosed that P2P financing investors usually do not have adequate financial industry expertise and investing experience. Therefore, this confirms that P2P financing B40 retail investors assess creditworthiness using soft and challenging information. ...
Article
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Aim/Purpose: This study investigates the intention to invest in online peer-to-peer (P2P) lending platforms among the bottom 40% (B40) Malaysian households by income. Background: The B40 group citizens earn less than USD 1,096.00 (i.e., RM 4,850.00) in monthly household income, thereby possessing relatively small capital investments suitable for online P2P lending. Methodology: Drawing on the technology acceptance model (TAM), this research developed and tested the relevant hypotheses with data collected from 216 respondents. The partial least square structural equation modelling (PLS-SEM) technique was employed to analyse the collected data. Contribution: This study contributes to the body of knowledge on financial inclusion by demonstrating the relevance of modified TAM in explaining the intention to invest in online P2P lending platforms among investors with lower disposable income (i.e., the B40 group in Malaysia). Findings: The findings revealed that information quality, perceived risk, and perceived ease of use are relevant to B40 investment intention in P2P online lending platforms. However, contrary to expectations, trust and financial literacy are insignificant predictors of B40 investment intention. Recommendations for Practitioners: The P2P lending platform operators could enhance financial inclusion among the B40 group by ensuring borrowers provide sufficient, relevant, and reliable information with adequate security measures to minimise risk exposure. The financial regulators should also conduct periodic audits to ensure that the operators commit to enhancing information quality, platform security, and usability. Recommendation for Researchers: The intention to invest in online P2P lending platforms among the B40 group could be enhanced by improving information quality and user experience, addressing perceived risks, reassessing trust-building strategies and financial literacy initiatives, and adopting holistic, interdisciplinary approaches. These findings suggest targeted strategies to enhance financial inclusion and investment participation among B40 investors. Impact on Society: The study’s findings hold significant implications for financial regulators and institutions, such as the Securities Commission Malaysia, Bank Negara Malaysia, commercial and investment banks, and insurance companies. By focusing on these key determinants, policymakers can design targeted interventions to improve the accessibility and attractiveness of P2P lending platforms for B40 investors. Enhanced information quality and ease of use can be mandated through regulatory frameworks, while effective risk communication and mitigation strategies can be developed to build investor confidence. These measures can collectively promote financial growth and inclusion, supporting broader economic development goals. Future Research: Future research could expand the sample size to consider older B40 individuals across different countries and use a longitudinal survey to assess the actual investment decision of the B40 investors.
... The traditional banking model faces challenges in effectively screening and lending to small borrowers. The emergence of peer-to-peer lending platforms demonstrates alternative lending models that use technology for verification, offering insight into how traditional banks can adapt to improve their lending practices and reduce risk (Iyer et al., 2009;Diep & Canh, 2022). Morris (2011) and Kozmenko and Savchenko (2013) determine that a more focused approach to the main banking functions can help in better risk management and ensuring financial stability. ...
Article
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This paper investigates the interaction between decentralized financial services and the traditional banking system by building VAR models, conducting Granger causality tests, building impulse response functions, and performing variance analysis. To implement the model, banking indicators of the USA, India, and Great Britain were selected: the volume of commercial and industrial loans, interest rate, consumer price index, total liabilities and capital of banks, aggregate deposits, federal funds rate (for the USA), and repo rate (for India). The study examined central bank data of the specified countries from July 2018 to January 2024 with the TVL indicator, which measures the sum of all assets locked in DeFi protocols. The results of the impulse response function (IRF) for countries demonstrate different interactions between TVL and bank indicators. The US response to TVL shocks demonstrates a stimulative monetary policy, with significant Fed rate reductions and increased commercial lending to boost economic activity. In contrast, India’s monetary stimulus, marked by declining repo rates and growth in banking sector liabilities and deposits, aims to enhance economic resilience. The UK, however, adopts a conservative monetary approach, with sharp bank rate increases and mixed lending and deposit responses, prioritizing financial stability. Analysis across these nations highlights different impacts of financial indicators on TVL. In the US, the evolving relationship between TVL and bank indicators reflects the financial system’s complexity. India’s sensitivity to monetary policy, credit conditions, and inflation significantly influences TVL. In the UK, central bank decisions, particularly the bank rate, play a crucial role in financial market dynamics. AcknowledgmentThe authors appreciate the assistance in the preparation of the article provided by the University of Debrecen Program for Scientific Publication and the János Bolyai Research Scholarship of the Hungarian Academy of Sciences.
... Other researchers (Emekter et al., 2015;Serrano-Cinca et al., 2015;Wang, Xiong, & Zheng, 2021;Weiss et al., 2010) suggest the use of specific information from potential borrowers in order to calculate creditworthiness. Since debt insolvency is a significant risk to investors, Iyer et al. (2009) find that lenders were, in a significant number of cases, unable to use information on borrower creditworthiness and suggest that existing and publicly available information can be utilized. On the other hand, Chen et al. (2022) examine network centrality effects in the industry and show that central lender nodes have an advantage which results in greater investment volumes. ...
Article
We explore the potential outcomes for financial stability when using peer‐to‐peer lenders to finance economic activity. Combining Random Regression Forests, a machine‐learning process, with an agent‐based model, we perform simulations on artificial economies with various degrees of adoption of peer‐to‐peer lending. We find that as peer‐to‐peer lenders proliferate, there is increased financial instability, lower GDP and higher unemployment. On the other hand, peer‐to‐peer lending increases the total volume of loans given out but demonstrates a preference towards consumer loans (over corporate loans), which has a negative effect in the long run. Finally, introducing peer‐to‐peer lenders increases the access of the unbanked to services which conventional banking is not able to offer within the extant regulatory framework. Our results can help policymakers as they address the issue of regulation in the peer‐to‐peer finance industry.
... For small business loans, Mester (1997) found that the credit history of business owners has better prediction capability than the variables in the financial statement. On the other hand, when it comes to the P2P lending platforms, Iyer et al. (2009) found that the credit score, borrower's current and total defaulted loans, debt-to-income ratio and loan amount are the most important default factors. Everett (2015) considered the credit score, age of borrower, house ownership, guarantor and loan amount as important factors. ...
Article
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In this study, we constructed the credit-scoring model of P2P loans by using several machine learning and artificial neural network (ANN) methods, including logistic regression (LR), a support vector machine, a decision tree, random forest, XGBoost, LightGBM and 2-layer neural networks. This study explores several hyperparameter settings for each method by performing a grid search and cross-validation to get the most suitable credit-scoring model in terms of training time and test performance. In this study, we get and clean the open P2P loan data from Lending Club with feature engineering concepts. In order to find significant default factors, we used an XGBoost method to pre-train all data and get the feature importance. The 16 selected features can provide economic implications for research about default prediction in P2P loans. Besides, the empirical result shows that gradient-boosting decision tree methods, including XGBoost and LightGBM, outperform ANN and LR methods, which are commonly used for traditional credit scoring. Among all of the methods, XGBoost performed the best.
... Yet, in contrast, most borrowers with social ties are more likely to pay late or default. Iyer et al. (2009) found that crowdlending can derive about one-third of the information for borrowers' actual creditworthiness from "soft" information (subjective, unverified information such as pictures and descriptions that borrowers volunteer) in borrowers' applications. ...
Article
The crowdlending industry is a fast-growing financial technology (fintech) sector that brings together borrowers and lenders. As an alternative financial intermediary, the crowdlending industry plays an essential role in reducing the financial exclusion of small and medium-sized enterprises (SMEs) struggling to obtain funds from traditional financial intermediaries such as commercial banks. With the onset of Covid-19 and the deteriorating economies worldwide, Singapore crowdlending platforms have come under pressure due to the increasing default rate of their borrowers. This case study illuminates the challenges faced by Aurora (pseudonym), a crowdlending platform that operates in Singapore, Indonesia, and Malaysia. In response to high default rates during Covid-19, Aurora’s management made improvement to its current machine learning-based credit scoring model in June 2021. This case study describes the challenges Aurora faced in identifying relevant features for the machine learning model, data preparation and cleansing, and selecting the appropriate credit model algorithms to replace its current approval process.
... In recent P2P markets, studies are underway that take into account 'soft' information derived from explanations written in loan applications in addition to the 'hard' data used for existing credit evaluations for default prediction (Zopounidis et al., 2018). Iyer et al. (2009) found that information posted by borrowers on platforms, along with their official financial information, is reliable and has a significant correlation with creditworthiness, and robust screening was possible for small loans such as those in the P2P market. Galak et al. (2011) found that investors prefer private borrowers with whom they share a stronger social affinity. ...
Article
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Peer-to-peer (P2P) lending has emerged as an alternative method of financing. Keeping pace with this development, many P2P lending studies have provided approaches to select investment portfolios for individual lenders. However, none of these approaches consider how long it takes for an individual loan to be fully funded so as to reduce the opportunity cost incurred due to delayed investment. In this paper, we propose a goal programming framework to develop an optimal P2P lending portfolio that considers not only the expected returns but also this opportunity cost for individual investors. First, for each loan proposal, a logistic regression model is used to predict the loan default probability while a Weibull regression is used to determine the opportunity cost incurred due to the time taken to obtain the loan. Next, goal programming is applied to construct a portfolio that minimizes the slack from the desired return on investment as well as the surplus from the preset opportunity cost due to a prolonged bidding period. The proposed approach is then applied to Prosper platform data and is expected to help investors’ portfolio decisions in the P2P lending market.
... Some researchers are concerned about borrowers' final activities, namely, factors influencing the default rate, and some are focused on whether principal and interest of online loan can be paid on schedule. Iyer et al. [6] test empirically the role of credit score to online loan's default rate based on data of Prosper, and the results showed that credit level has significant influence on the default rate. Liao et al. [7] test empirically the relationship between interest rates and default rates using data from the Renrendai website. ...
Article
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Financial big data are obtained by web crawler, and investors’ recognition abilities for risk and profit in online loan markets are researched using heteroskedastic Probit models. The conclusions are obtained as follows: First, the preference for the item is reflected directly in the time and indirectly in the number of participants for being full, and the larger the preference, the shorter the time and the fewer the participants. Second, investors can discriminate the default risk not reflected by the interest rate, and the bigger the default risk, the longer the time and the more participants being full. Third, investors can discriminate the pure return rate deducted from the maturity term and credit risk, and the higher the return, the shorter the time and the fewer the participants being full. Fourth, default risks are reflected well by online loan platform interest rates, and inventors do not choose the item blindly according to the interest rate but consider comprehensively the profit and the risk. In the future, interest rate liberalization should be deepened, the choosing function of interest rates should be played better, and the information disclosure, investor education, and investor effective usage of other information should be strengthened.
... Freedman and Jin find that such kind of social network information available on Prosper help lenders make good judgments about borrowers, while Lin, Prabhala and Viswanathan prove that friendships increase the probability of successful funding, lower interest rates on funded loans, and are associated with lower ex post default rates. Iyer et al. in (Iyer, Khwaja, Luttmer, & Shue, 2009) mainly study how lenders in the P2P lending markets judge the creditworthiness of borrowers. They find that although lenders consider more the standard banking "hard" information, like credit score or loan repayment stream, the "soft" information available on Prosper like communication with borrowers belonging to a group, maximum interest rate the borrower is willing to pay, or the number of words used in the listing text descriptions also play a role in the success of borrowing. ...
... Freedman and Jin find that such kind of social network information available on Prosper help lenders make good judgments about borrowers, while Lin, Prabhala and Viswanathan prove that friendships increase the probability of successful funding, lower interest rates on funded loans, and are associated with lower ex post default rates. Iyer et al. in (Iyer, Khwaja, Luttmer, & Shue, 2009) mainly study how lenders in the P2P lending markets judge the creditworthiness of borrowers. They find that although lenders consider more the standard banking "hard" information, like credit score or loan repayment stream, the "soft" information available on Prosper like communication with borrowers belonging to a group, maximum interest rate the borrower is willing to pay, or the number of words used in the listing text descriptions also play a role in the success of borrowing. ...
Article
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Peer-to-Peer (P2P) lending is an online lending process allowing individuals to obtain or concede loans without the interference of traditional financial intermediaries. It has grown quickly the last years, with some platforms reaching billions of dollars of loans in principal in a short amount of time. Since each loan is associated with the probability of loss due to a borrower's failure, this paper addresses the borrower's default prediction problem in the P2P financial ecosystem. The main assumption, which makes this study different from the available literature, is that borrowers sharing the same homeownership status display similar risk profile, thus a model per segment should be developed. We estimate the Probability of Default (PD) of a borrower by using Logistic Regression (LR) coupled with Weight of Evidence encoding. The features set is identified via the Sequential Feature Selection (SFS). We compare the forward against the backward SFS, in terms of the Area Under the Curve (AUC), and we choose the one that maximizes this statistic. Finally, we compare the results of the chosen LR approach against two other popular Machine Learning (ML) techniques: the k Nearest Neighbors (k-NN) and the Random Forest (RF).
... As one of 'soft information' that is easily accessible and informative, the loan description has attracted more and more attention from scholars. Iyer, etc. (2009) believe that 'soft information' can help identify lenders' credit scores [10]. Xin, etc. (2017) studied the relationship between the borrower's personality tendencies embodied in loan descriptions and credit risk through extracting information about the personality tendency of P2P lending borrowers [11]. ...
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Peer-to-Peer (P2P) lending provides convenient and efficient financing channels for small and medium-sized enterprises and individuals, and therefore it has developed rapidly since entering the market. However, due to the imperfection of the credit system and the influence of cyberspace restrictions, P2P network lending faces frequent borrower credit risk crises during the transaction process, with a high proportion of borrowers default. This paper first analyzes the basic development of China’s P2P online lending and the credit risks of borrowers in the industry. Then according to the characteristics of P2P network lending and previous studies, a credit risk assessment indicators system for borrowers in P2P lending is formulated with 29 indicators. Finally, on the basis of the credit risk assessment indicators system constructed in this paper, BP neural network is built based on the BP algorithm, which is trained by the LM algorithm (Levenberg-Marquardt), Scaled Conjugate Gradient, and Bayesian Regularization respectively, to complete the credit risk assessment model. By comparing the results of three mentioned training methodologies, the BP neural network trained by the LM algorithm is finally adopted to construct the credit risk assessment model of borrowers in P2P lending, in which the input layer node is 9, the hidden layer node is 11 and output layer node is 1. The model can provide practical guidance for China and other countries’ P2P lending platforms, and therefore to establish and improve an accurate and effective borrower credit risk management system.
... In an attempt to promote and identify the creditable borrowers', direct P2P lending platforms like Prosper, Zopa, Lending Club use social networks and encourage the potential borrowers to provide the relevant financial information as much as possible throughout the social networks to reduce default risks (Ge, Feng & Gu, 2016). Iyer, Khwaja, Luttmer, and Shue (2009) classified the infor-1 According to Schroeder (2020) Economist Muhammad Yunus (founder of Grammen Bank of Bangladesh) termed Microcredit as a common form of microfinance that involves an extremely small loan given to an individual for self-employment projects, with the intention of allowing households that would otherwise be credit constrained to engage in income-generating activities. mation as "hard" and "soft", direct P2P platforms collect "hard" information like credit score, debt-to-income ratio, annual income as well as the business plan for utilizing the funds and "soft" information like a picture of the interested borrower through the social networks. ...
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Peer-to-Peer lending which is also known as P2P is an online financial investment platform where individual investors finance projects by lending money to individual borrowers through social networks. P2P models usually contributing to less privileged people especially entrepreneurs and frontier groups who do not have access to formal financial services. However, due to the economic conditions and lack of government support, P2P lending platforms in developing countries often fail to reveal the ‘credit history’ and ‘indebtedness’ of individual borrowers which have an expressive impact on loan performance. The objective of this study is to demonstrate theoretically the factors those influence the lenders to participate in the P2P lending platform in developing countries and the associated risks. For this purpose, two propositions are developed to examine the factors to demonstrate the role of the social network is also combined to further explain the P2P lending.
... Jahanzeb and Muneer in their research stated that although investments have been made based on forecasting, reviewing investment performance and taking into account market times there is still a difference between the actual investment value and that calculated [18]. In other words, investors also make irrational decisions when conducting information. ...
... Consequently, lenders tend to adopt a herding behaviour, as shown by Herzenstein et al. (2011), Lee and Lee (2012) and Käfer (2018). In some articles, authors discuss the role of social networks and "soft" information (e.g. group membership, friend endorsement, textual statements and other voluntary information) in mitigating information asymmetry and herding behaviour (Iyer et al. 2010;Michels 2012;Everett 2015;Ge et al. 2017). In particular, their results show that the inclusion of this kind of information in the risk assessment process can reduce the overall probability of default and improve investments' performance. ...
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In recent years, lending-based crowdfunding (LBCF) has been analysed from different perspectives but there has been little discussion about the strategies and business models adopted by LBCF companies. The aim of this study is to define a more correct classification of LBCF companies, based on their business models. We find that the existing classification can be improved by simultaneously considering strategic choices concerning target customers (borrowers and lenders), intermediation model and additional risk management services provided by platforms. We analyse an original worldwide sample of 30 LBCF companies and use a clustering algorithm to group them on the basis of financial and commercial fundamentals to identify four business models denoted by strategies, financial services, risk positions and customer target.
... Stern et al. (2017) were studying the factors that caused the appearance of peer-to-peer lending platforms in different provinces of China. Iyer et al. (2009) were assessing the process of construction of credit ratings and their values at one of the most famous peer-to-peer platforms, Prosper. Zeng et al., (2017) constructed investment solution models at peer-to-peer platforms, were studying the behavior of existing and new investors, their level confidence and a probability to grant to new loans. ...
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Chapter
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Theories based on incomplete contracting suggest that small organizations have a comparative advantage in activities that make extensive use of “soft” information. We provide evidence consistent with small banks being better able to collect and act on soft information than large banks. In particular, large banks are less willing to lend to informationally “difficult” credits, such as firms with no financial records. Moreover, after controlling for the endogeneity of bank-firm matching, we find that large banks lend at a greater distance, interact more impersonally with their borrowers, have shorter and less exclusive relationships, and do not alleviate credit constraints as effectively.
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We present new evidence on consumer liquidity constraints and the credit market conditions that might give rise to them. We analyze unique data from a large auto sales company serving the subprime market. Short-term liquidity appears to be a key driver of consumer behavior. Demand increases sharply during tax rebate season and purchases are highly sensitive to down-payment requirements. Lenders also face substantial informational problems. Default rates rise significantly with loan size, providing a rationale for loan caps, and higher-risk borrowers demand larger loans. This adverse selection is mitigated, however, by risk-based pricing. (JEL D14, D82, D83, G21)
Article
A central question surrounding the current subprime crisis is whether the securitization process reduced the incentives of financial intermediaries to carefully screen borrowers. We examine this issue empirically using data on securitized subprime mortgage loan contracts in the United States. We exploit a specific rule of thumb in the lending market to generate exogenous variation in the ease of securitization and compare the composition and performance of lenders' portfolios around the ad hoc threshold. Conditional on being securitized, the portfolio with greater ease of securitization defaults by around 10%-25% more than a similar risk profile group with a lesser ease of securitization. We conduct additional analyses to rule out differential selection by market participants around the threshold and lenders employing an optimal screening cutoff unrelated to securitization as alternative explanations. The results are confined to loans where intermediaries' screening effort may be relevant and soft information about borrowers determines their creditworthiness. Our findings suggest that existing securitization practices did adversely affect the screening incentives of subprime lenders. (c) 2010 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology..
Article
A central question surrounding the current subprime crisis is whether the securitization process reduced the incentives of financial intermediaries to carefully screen borrowers. We examine this issue empirically using data on securitized subprime mortgage loan contracts in the United States. We exploit a specific rule of thumb in the lending market to generate exogenous variation in the ease of securitization and compare the composition and performance of lenders' portfolios around the ad hoc threshold. Conditional on being securitized, the portfolio with greater ease of securitization defaults by around 10%–25% more than a similar risk profile group with a lesser ease of securitization. We conduct additional analyses to rule out differential selection by market participants around the threshold and lenders employing an optimal screening cutoff unrelated to securitization as alternative explanations. The results are confined to loans where intermediaries' screening effort may be relevant and soft information about borrowers determines their creditworthiness. Our findings suggest that existing securitization practices did adversely affect the screening incentives of subprime lenders.
Article
This paper develops a theory of the allocation of formal authority (the right to decide) and real authority (the effective control over decisions) within organizations, and it illustrates how a formally integrated structure can accommodate various degrees of "real" integration. Real authority is determined by the structure of information, which in turn depends on the allocation of formal authority. An increase in an agent's real authority promotes initiative but results in a loss of control for the principal. After spelling out (some of) the main determinants of the delegation of formal authority within organizations, the paper examines a number of factors that increase the subordinates' real authority in a formally integrated structure: overload, lenient rules, urgency of decision, reputation, performance measurement, and multiplicity of superiors. Finally, the amount of communication in an organization is shown to depend on the allocation of formal authority.
Article
We show that if firms statistically discriminate among young workers on the basis of easily observable characteristics such as education, then as firms learn about productivity, the coefficients on the easily observed variables should fall, and the coefficients on hard-to-observe correlates of productivity should rise. We find support for this proposition using NLSY79 data on education, the AFQT test, father's education, and wages for young men and their siblings. We find little evidence for statistical discrimination in wages on the basis of race. Our analysis has a wide range of applications in the labor market and elsewhere. © 2000 the President and Fellows of Harvard College and the Massachusetts Institute of Technology
Article
This paper studies peer-to-peer (p2p) lending on the Internet. Prosper.com, the first p2p lending website in the US, matches individual lenders and borrowers for unsecured consumer loans. Using transaction data from June 1, 2006 to July 31, 2008, we examine what information problems exist on Prosper and whether social networks help alleviate the information problems. As we expect, data identifies three information problems on Prosper.com. First, Prosper lenders face extra adverse selection because they observe categories of credit grades rather than the actual credit scores. This selection is partially offset when Prosper posts more detailed credit information on the website. Second, many Prosper lenders have made mistakes in loan selection but they learn vigorously over time. Third, as Stiglitz and Weiss (1981) predict, a higher interest rate can imply lower rate of return because higher interest attracts lower quality borrowers. Micro-finance theories argue that social networks may identify good risks either because friends and colleagues observe the intrinsic type of borrowers ex ante or because the monitoring within social networks provides a stronger incentive to pay off loans ex post. We find evidence both for and against this argument. For example, loans with friend endorsements and friend bids have fewer missed payments and yield significantly higher rates of return than other loans. On the other hand, the estimated returns of group loans are significantly lower than those of non-group loans. That being said, the return gap between group and non-group loans is closing over time. This convergence is partially due to lender learning and partially due to Prosper eliminating group leader rewards which motivated leaders to fund lower quality loans in order to earn the rewards.
Article
A major problem for institutional lenders is ensuring that borrowers exercise prudence in the use of the funds so that the likelihood of repayments is enhanced. One partial solution is peer monitoring: having neighbors who are in a good position to monitor the borrower be required to pay a penalty if the borrower goes bankrupt. Peer monitoring is largely responsible for the successful financial performance of the Grameen Bank of Bangladesh and of similar group lending programs elsewhere. But peer monitoring has a cost. It transfers risk from the bank, which is in a better position to bear risk, to the cosigner. In a simple model of peer monitoring in a competitive credit market, this article demonstrates that the transfer of risk to an improvement in borrowers' welfare. Copyright 1990 by Oxford University Press.
Article
This paper assesses different organizational forms in terms of their ability to generate information about investment projects and allocate capital to these projects efficiently. A decentralized approach with small, single-manager firms is most likely to be attractive when information about individual projects is soft' and cannot be credibly transmitted. Moreover, holding fixed firm size, soft information also favors flatter organizations with fewer layers of management. In contrast, large hierarchical firms with multiple layers of management are at a comparative advantage when information can be costlessly hardened' and passed along within the hierarchy. As a concrete application of the theory, the paper discusses the consequences of consolidation in the banking industry. It has been documented that when large banks acquire small banks, there is a pronounced decline in lending to small businesses. To the extent that small-business lending relies heavily on soft information, this is exactly what the theory would lead one to expect.
Article
This paper asks how well different organizational structures perform in terms of generating information about investment projects and allocating capital to these projects. A decentralized approach-with small, single-manager firms-is most likely to be attractive when information about projects is "soft" and cannot be credibly transmitted. In contrast, large hierarchies perform better when information can be costlessly "hardened" and passed along inside the firm. The model can be used to think about the consequences of consolidation in the banking industry, particularly the documented tendency for mergers to lead to declines in small-business lending. Copyright The American Finance Association 2002.
Article
We analyze the extent to which simple markets can be used to aggregate disperse information into efficient forecasts of uncertain future events. Drawing together data from a range of prediction contexts, we show that market-generated forecasts are typically fairly accurate, and that they outperform most moderately sophisticated benchmarks. Carefully designed contracts can yield insight into the market's expectations about probabilities, means and medians, and also uncertainty about these parameters. Moreover, conditional markets can effectively reveal the market's beliefs about regression coefficients, although we still have the usual problem of disentangling correlation from causation. We discuss a number of market design issues and highlight domains in which prediction markets are most likely to be useful.
Article
We look at an economic environment where borrowers have some information about the nature of each other's projects that lenders do not. We show that joint-liability lending contracts, similar to those used by credit cooperatives and group-lending schemes, will induce endogenous peer selection in the formation of groups in a way that the instrument of joint liability can be used as a screening device to exploit this local information. This can improve welfare and repayment rates if standard screening instruments such as collateral are unavailable.
Article
This paper models the inner workings of relationship lending, the implications for bank organisational structure, and the effects of shocks to the economic environment on the availability of relationship credit to small businesses. Relationship lending depends on the accumulation over time by the loan officer of "soft" information. Because the loan officer is the repository of this soft information, agency problems are created throughout the organisation that may best be resolved by structuring the bank as a small, closely-held organisation with few managerial layers. The shocks analysed include technological innovations, regulatory regime shifts, banking industry consolidation, and monetary policy shocks. Copyright Royal Economic Society 2002
Competition to Default: Racial Discrimination in the Market for Online Peer-to-Peer Lending
  • Walter Theseira
Theseira, Walter, 2008, "Competition to Default: Racial Discrimination in the Market for Online Peer-to-Peer Lending," Working paper, Wharton.
Dissecting Bank Relationships: The Role of Cross-Selling and Connections in Commercial Lending to Small Businesses
  • Lori Santikian
Santikian, Lori, 2009, "Dissecting Bank Relationships: The Role of Cross-Selling and Connections in Commercial Lending to Small Businesses," Working paper, Harvard University.
What's in a Picture? Evidence of Discrimination from Prosper.com Working paper Love & Loans: The Effect of Beauty and Personal Characteristics in Credit Markets
  • Pope
  • Justin G Devin
  • Sydnor
  • Paper
  • Columbia
  • Gsb
Pope, Devin G., and Justin Sydnor, 2008, " What's in a Picture? Evidence of Discrimination from Prosper.com, " Working paper, Wharton. Ravina, Enrichetta, 2008, " Love & Loans: The Effect of Beauty and Personal Characteristics in Credit Markets, " Working Paper, Columbia GSB.
Dissecting Bank Relationships: The Role of Cross-Selling and Connections in Commercial Lending to Small Businesses Working paper
  • Santikian
  • Lori
Santikian, Lori, 2009, " Dissecting Bank Relationships: The Role of Cross-Selling and Connections in Commercial Lending to Small Businesses, " Working paper, Harvard University.