Consumer lending efficiency:commercial banks versus a fintech lender

Fintechs are believed to help expand credit access to underserved consumers without taking on additional risk.We compare the performance efficiency of LendingClub’s unsecured personal loans with similar loans originated by banks.Using stochastic frontier estimation,we decompose the observed nonperforming loan(NPL)ratio into three components:the best-practice minimum NPL ratio,the excess NPL ratio,and a statistical noise,the former two of which reflect the lender’s inherent credit risk and lending inefficiency,respectively.As of 2013 and 2016,we find that the higher NPL ratios at the largest banks are driven by inherent credit risk,rather than lending inefficiency.Smaller banks are less efficient.In addition,as of 2013,LendingClub’s observed NPL ratio and lending efficiency were in line with banks with similar lending volume.However,its lending efficiency improved significantly from 2013 to 2016.As of 2016,LendingClub’s performance resembled the largest banks–consistent with an argument that its increased use of alternative data and AI/ML may have improved its credit risk assessment capacity above and beyond its peers using traditional approaches.Furthermore,we also investigate capital market incentives for lenders to take credit risk.Market value regression using the NPL ratio suggests that market discipline provides incentives to make less risky consumer loans.However,the regression using two decomposed components(inherent credit risk and lending inefficiency)tells a deeper underlying story:market value is significantly positively related to inherent credit risk at most banks,whereas it is significantly negatively related to lending inefficiency at most banks.Market discipline appears to reward exposure to inherent credit risk and punish inefficient lending.

Local digital lending development and the incidence of deprivation in Kenya

In the developing world,vulnerable communities often lack access to regular income sources to cope with unforeseen events.Recent advancements in financial technology have enabled microcredit to be delivered via digital platforms.Although digital credit may quicken remote access to consumer credit without the need for collateral,little is known about its contribution to the welfare of underserved communities.This study examines the effects of local digital lending development on deprivation and explores the implications of these effects on rural inhabitants.The results show a negative association between local digital lending development and food deprivation on one hand and health deprivation on the other.The evidence suggests that local digital lending development can reduce the probability of food and health deprivation.Furthermore,the evidence reveals that inhabitants of rural communities benefit more from digital lending development.This study recommends the decentralization of financial inclusion policies as a pathway to promote digital lending at the local level.

A hybrid decision support system with golden cut and bipolar q‑ROFSs for evaluating the risk‑based strategic priorities of fintech lending for clean energy projects

In the last decade,the risk evaluation and the investment decision are among the most prominent issues of efficient project management.Especially,the innovative financial sources could have some specific risk appetite due to the increasing return of invest-ment.Hence,it is important to uncover the risk factors of fintech investments and investigate the possible impacts with an integrated approach to the strategic priorities of fintech lending.Accordingly,this study aims to analyze a unique risk set and the stra-tegic priorities of fintech lending for clean energy projects.The most important contri-butions to the literature can be listed as to construct an impact-direction map of risk-based strategic priorities for fintech lending in clean energy projects and to measure the possible influences by using a hybrid decision making system with golden cut and bipolar q-rung orthopair fuzzy sets.The extension of multi stepwise weight assessment ratio analysis(M-SWARA)is applied for weighting the risk factors of fintech lending.The extension of elimination and choice translating reality(ELECTRE)is employed for con-structing and ranking the risk-based strategic priorities for clean energy projects.In this process,data is obtained with the evaluation of three different decision makers.The main superiority of the proposed model by comparing with the previous models in the literature is that significant improvements are made to the classical SWARA method so that a new technique is created with the name of M-SWARA.Hence,the causality analysis between the criteria can also be performed in this proposed model.The find-ings demonstrate that security is the most critical risk factor for fintech lending system.Moreover,volume is found as the most critical risk-based strategy for fintech lending.In this context,fintech companies need to take some precautions to effectively manage the security risk.For this purpose,the main risks to information technologies need to be clearly identified.Next,control steps should be put for these risks to be managed properly.Furthermore,it has been determined that the most appropriate strategy to increase the success of the fintech lending system is to increase the number of financi-ers integrated into the system.Within this framework,the platform should be secure and profitable to persuade financiers.

How signaling and search costs affect information asymmetry in P2P lending:the economics of big data

In the past decade,online Peer-to-Peer(P2P)lending platforms have transformed the lending industry,which has been historically dominated by commercial banks.Information technology breakthroughs such as big data-based financial technologies(Fintech)have been identified as important disruptive driving forces for this paradigm shift.In this paper,we take an information economics perspective to investigate how big data affects the transformation of the lending industry.By identifying how signaling and search costs are reduced by big data analytics for credit risk management of P2P lending,we discuss how information asymmetry is reduced in the big data era.Rooted in the lending business,we propose a theory on the economics of big data and outline a number of research opportunities and challenging issues.

关于我国P2P的风险控制探讨——基于“lending club”和“人人贷”的对比分析

P2P,英文叫做Peer-to-Peer lending,即点对点网贷或个人对个人网贷。近年来,P2P行业在我国呈爆发式增长,但跑路、自融、诈骗等问题平台迭出,如何完善平台风险控制,是摆在监管层、行业和平台自身的切实问题。本文通过分析P2P发展模式,结合我国P2P发展现状及面临的风险现状,将国外最大的P2P公司"Lending Club"和国内较知名的P2P公司"人人贷"进行风险控制模式对比,从P2P平台、行业、监管等方面提出风险管理对策,以促进P2P网贷更有序、更阳光的发展。

P2P网贷平台盈利模式研究——以宜人贷和LendingClub为例

近年来,国内P2P网贷行业经历过爆发式增长之后,发展逐渐回归理性。随着监管制度逐渐完善,国内P2P网贷平台更加难实现盈利,盈利问题受到了整个社会的广泛关注。本文对发展较为成熟的两大P2P网贷平台宜人贷和Lending Club的盈利模式进行了深入研究,并对国内P2P网贷平台盈利模式的改善提出了相应的对策建议。

COVID‑19 pandemic risk and probability of loan default:evidence from marketplace lending market

As the COVID-19 pandemic adversely affects the financial markets,a better understanding of the lending dynamics of a successful marketplace is necessary under the conditions of financial distress.Using the loan book database of Mintos(Latvia)and employing logit regression method,we provide evidence of the pandemic-induced exposure to default risk in the marketplace lending market.Our analysis indicates that the probability of default increases from 0.056 in the pre-pandemic period to 0.079 in the post-pandemic period.COVID-19 pandemic has a significant impact on default risk during May and June of 2020.We also find that the magnitude of the impact of COVID-19 risk is higher for borrowers with lower credit ratings and in countries with low levels of FinTech adoption.Our main findings are robust to sample selection bias allowing for a better understanding of and quantifying risks related to FinTech loans during the pandemic and periods of overall economic distress.

Team rivalry and lending on crowdfunding platforms:an empirical analysis

Background:Prosocial crowdfunding helps the underprivileged obtain non-profitseeking loans from multitudinous lenders.Some platforms introduce teamcompetition to motivate member participation and may thus induce team rivalry.Methods:We investigate how team rivalry affects lending decisions using data fromKiva.org.We argue that a rivalry relationship may engage teams to compete directlyagainst rivals by lending to the same project or prevent them from doing sobecause they intend not to cooperate.Result:We find that a team is less likely to lend to a project that has receivedfunding from its rival team,suggesting that rival teams tend to avoid cooperation.Conclusions:We discuss the implications of our findings for crowdfundingand competition-based motivation mechanisms in general.

我国P2P行业的风险防范与应对——基于Lending Club违规事件的探讨

近两年来,我国P2P理财行业经历着井喷式的发展,新平台一家接一家诞生,行业交易额屡创新高,受到多家风险机构以及知名互联网企业的高度关注,但与此同时行业问题也逐步显现,尤其是2015年以来跑路事件等各类问题的集中爆发,

对中国P2P网贷平台的思考——基于Lending Club欺诈事件

Lending Club欺诈事件给美国甚至全球P2P行业的发展蒙上了一层阴影。放眼国内,近几年P2P发展迅速,但风险事件频发,现恰逢我国P2P网贷行业专项整治的特殊时期,从Lending Club事件中得到启示具有重要的现实意义。

A Consortium Blockchain Paradigm on Hyperledger-Based Peer-to-Peer Lending System

This paper focuses on how to use consortium blockchain to improve the regulation of peer-to-peer(P2 P) lending market. The partial decentralized consortium blockchain with limited pre-set nodes can well improve transparency and security, which is suitable for financial regulation. Considering irregularities of the P2P lending market, the Hyperledger-based Peer-to-Peer Lending System(HyperP2PLS) is proposed. First elaborate the application scenario and business logic of the system, where a national P2P Lending Trading Center will be established to integrate all transactions and information of P2P lending market. Then construct the system architecture consisting of the blockchain network, HTTP server, and applications. The algorithm of implementation process and the web application for users have been well illustrated. The performance analysis shows that HyperP2PLS can guarantee the reliability, safety, transparency and efficiency.

Borrower’s default and self-disclosure of social media information in P2P lending

Background:We examine the signaling effect of borrowers’social media behavior,especially self-disclosure behavior,on the default probability of money borrowers on a peer-to-peer(P2P)lending site.Method:We use a unique dataset that combines loan data from a large P2P lending site with the borrower’s social media presence data from a popular social media site.Results:Through a natural experiment enabled by an instrument variable,we identify two forms of social media information that act as signals of borrowers’creditworthiness:(1)borrowers’choice to self-disclose their social media account to the P2P lending site,and(2)borrowers’social media behavior,such as their social network scope and social media engagement.Conclusion:This study offers new insights for screening borrowers in P2P lending and a novel usage of social media information.

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

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

Machine learning approach to drivers of bank lending:evidence from an emerging economy

The study analyzes the performance of bank-specific characteristics,macroeconomic indicators,and global factors to predict the bank lending in Turkey for the period 2002Q4–2019Q2.The objective of this study is first,to clarify the possible nonlinear and nonparametric relationships between outstanding bank loans and bank-specific,macroeconomic,and global factors.Second,it aims to propose various machine learning algorithms that determine drivers of bank lending and benefits from the advantages of these techniques.The empirical findings indicate favorable evidence that the drivers of bank lending exhibit some nonlinearities.Additionally,partial dependence plots depict that numerous bank-specific characteristics and macroeconomic indicators tend to be important variables that influence bank lending behavior.The study’s findings have some policy implications for bank managers,regulatory authorities,and policymakers.

P2P网络借贷运营模式研究——以陆金所和Lending Club为例

P2P是互联网金融中最热门的代表形式之一,较之于传统金融机构的高利率、高门槛,日益成为民间借贷的有效补充。从2013年开始,P2P网贷平台在中国进入了一个爆发增长的时期,但随后就是不断出现的系列风险事件。P2P网贷平台的运营模式引起了人们的广泛关注。本文以中美最具影响力的P2P网贷平台陆金所和Lendingclub为例,分析其运营模式及发展背景差异,并提出优化国内P2P网贷运营模式的对策建议。

Regulatory constraint and small business lending:do innovative peer-to-peer lenders have an advantage?

This paper investigates whether innovative Peer-to-Peer lending by FinTechs’has a regulatory advantage over the big banks in respect of small business lending.We do this through the lens of the regulations imposed by the Dodd-Frank Act,using a difference-in-difference methodology.The Act tightened traditional bank credit standards on business loans,especially for small firms.However,the new FinTech lenders were not subject to the same regulatory burden.We find that traditional banks significantly reduced their lending to small businesses,as compared to their FinTech competitors.Our results suggest that while the Dodd-Frank Act constrained lending to small businesses,innovative new lending models gained a regulatory advantage and the Peer-to-Peer lenders capitalized on this.

A credit scoring model based on the Myers–Briggs type indicator in online peer-to-peer lending

Although psychometric features have been considered for alternative credit scoring,they have not yet been applied to peer-to-peer(P2P)lending because such information is not available on platforms.This study proposed an alternative credit scoring model for P2P lending by extracting typical personality types inferred from the borrowers’job category.We projected a virtual space of borrowers by using the affinity matrix based on the Myers–Briggs type indicator(MBTI)that fits each job category.Applying the distance in this space to Lending Club data,we used locally weighted logistic regression to vary the coefficients of the variables,which affect loan repayments,with each MBTI type for predicting the default probability.We found that each MBTI type’s credit scoring model has different significant variables.This study provides insights into breakthroughs in developing alternative credit scoring for P2P lending.

Modelling trust evolution within small business lending relationships

Trust is a key dimension in the principal-agent relationship and it has been studied extensively.However,the dynamics,evolution,and intrinsic motivation and mechanisms have received less attention.This paper investigates the intrinsic motivation of trust and it proposes a theoretical model of trust evolution that is based on the notion of‘trust response’and‘trust spiral’.We then specifically focus on trust within the lending relationship between banks and small businesses,and we run numerical simulations to further illustrate the evolution of involved mutual trust over time.Our model provides implications for future research in both trust evolution and small business lending relationships.

The impact of bank lending on Palestine economic growth:an econometric analysis of time series data

Banking is an essential sector of Palestine’s economy.More credits provided by banks are considered to have a positive impact on economic growth so that the overall objective of this study is to examine the impact of bank lending on economic growth in Palestine.The study employs the Augmented Dickey-Fuller to test for stationarity in the time series,The Johansen co-integration,Vector Autoregressive Model and Vector Error Correction Model are employed to identify the long-run and short-run dynamics among the variables,and Granger causality test in order to determine the direction of causality.The study finds that a long run relationship exists among the variables and insignificant short run relationship.Also,the study findings show that there is unidirectional causality and runs from GDP to bank lending.The insignificant contribution of bank lending to GDP is attributed to the fact that banks are not highly interested in lending to the production sector of the economy due to the high level of risk.However,the primary empirical evidence reveals that bank lending doesn’t cause economic growth,but economic growth causes bank lending.

An Analysis of Evolution of Lending Patterns of IFAD China's Rural Financial Project

This paper tells us briefly the background and aims of the fundation of IFAD.It points out that IFAD plays an important part in helping the development of agriculture and food production in developing countries,and in reducing the poverty conditions in the poorest countries.It tells us that IFAD has offered donation to China for 8 times,with a total amount reaching to 56.839 million dollars.With the development of the economy of China,IFAD has changed its lending financial policies to China from a highly beneficial policy to a moderate lending policy.The operation of the loan projects have greatly improved the living conditions and of the local and the conditions of agricultural production,promoting the improvement of economy,social stability,and enviroment.It discusses several lending patterns of IFAD China's rural financial projects.The government-leading model,which is represented by the IFAD financial lending loan.Market-oriented model of formal financial institutions,which is represented by IFAD Rural Credit Cooperatives,with market oriented.Government supporting as secondary model,which are dominated by IFAD Women's lending patterns and Village Development Fund lending model.In this paper,advantages and disadvantages of lending patterns are analyzed and evaluated.It discusses the development trend of IFAD financial lending patterns in terms of Rural Credit Cooperatives and a pattern which is community-oriented and farmers-benefited.