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.

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.

A predictive indicator using lender composition for loan evaluation in P2P lending

Most loan evaluation methods in peer-to-peer(P2P)lending mainly exploit the borrowers’credit information.However,the present study presents the maturity-based lender composition score,which exploits the investment capability of a group of lenders who fund the same loan,to enhance the P2P loan evaluation.More specifically,we extract lenders’profiles in terms of performance,risk,and experience by quantifying their investment history and develop our loan evaluation indicator by aggregating the profiles of lenders in the composition.To measure the ability of a lender for continuous improvement in P2P investment,we introduce lender maturity to capture this evolvement and incorporate it into the aggregation process.Our empirical study demonstrates that the maturity-based lender composition score can serve as an effective indicator for identifying loan quality and be included in other commonly used loan evaluation models for accuracy improvement.

Risks and Countermeasures of Chinese P2P Lending Network in the Internet Financial Background

In the era of big data-oriented development in today＇s society, with the Internet as the background of the financial lending rapid development of P2P network, and its role in promoting economic development has played, but also generated a lot of negative impact. In this paper, the basic concepts of lending comb P2P networks, based on analyzes the risks faced by P2P networks borrowing against these risks P2P network is proposed to strengthen the regulatory lending advice and countermeasures.

P2P network lending in the credit risk rating of the individual

P2P lending network is person to person lending network, lnternet-based applications, individuals lending financial model to others through the network intermediary,＇ platform. Currently P2P lending network has developed rapidly, but the P2P network lending platform also are lacing increasing risks, the biggest risk is credit risk. This article from the credit rating perspective, comparative analysis of the existing credit rating methodology, Analysis to establish a relatively sound credit rating mechanisms, thus reducing credit risk.

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.

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.

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.

Financial technology and the future of banking

This paper presents an analytical framework that describes the business model of banks.It draws on the classical theory of banking and the literature on digital transformation.It provides an explanation for existing trends and,by extending the theory of the banking firm,it illustrates how financial intermediation will be impacted by innovative financial technology applications.It further reviews the options that established banks will have to consider in order to mitigate the threat to their profitability.Deposit taking and lending are considered in the context of the challenge made from shadow banking and the all-digital banks.The paper contributes to an understanding of the future of banking,providing a framework for scholarly empirical investigation.In the discussion,four possible strategies are proposed for market participants,(1)customer retention,(2)customer acquisition,(3)banking as a service and(4)social media payment platforms.It is concluded that,in an increasingly digital world,trust will remain at the core of banking.That said,liquidity transformation will still have an important role to play.The nature of banking and financial services,however,will change dramatically.

Internet Financial Risks and Investors’Risk Awareness--Evidence from Transaction Data of Online Lending Platforms

For the first time,this paper uses the operation data of 575 online P2P lending platforms to test whether investors have a strong risk awareness of online lending products.It is found that investors’behavior shows a certain risk awareness,both for the individual risk of specific platforms and for the overall market risk of the industry.On the one hand,raising interest rates and shortening the term does attract more investment,but for potentially problematic platforms,the effect of attracting investment is significantly worse,with excessive interest rates on the platforms even causing investors to invest less.On the other hand,when there are more online lending platforms in the market,investors will behave more cautiously.

Heterogeneous Traders, the Leverage Effect and Volatility of the Chinese P2P Market

The new financial industry represented by peer-to-peer lending has gradually become a new source of volatility due to the increasing complexity of the Chinese financial market.This volatility leads to greater risk to P2P investors and has become the focus of the regulatory authorities in China.Based on the background data of the P2P platform,Honglingchuangtou,we use the factor analysis method to construct a platform volatility(PV)index and we construct an HAR model to study the heterogeneous traders and leverage effect in the Chinese P2P market.The empirical results show that there are both short-term and long-term heterogeneous traders in the Chinese P2P market and that long-term traders have the greatest impact on market volatility.Similar to traditional financial markets,the volatility of the P2P market also shows a leverage effect,which means that the negative volatility of trader actions should have a negative impact on market fluctuations.With regard to the leverage effect,the LHAR-PV model is superior because of a higher goodness of fit and a lower prediction error.

Voluntary disclosures and peer-to-peer lending decisions:Evidence from the repeated game

This study investigates the effect of voluntary disclosures on lending decisions in the repeated game.Using a unique dataset from a peer-to-peer lending platform,"ppdai" (poipaidai),we document that voluntary disclosures in the repeated game play a stronger role in promoting funding success than those in the one-shot game.We argue that voluntary disclosures improve the bidding activity in the repeated game through which they increase funding success.In addition,the greater impact of voluntary disclosures on funding success in the repeated game only holds for loans without a personal guarantee attribution.Our extended results suggest that the subjective voluntary disclosures in the repeated game have greater information content only when borrowers have a successful borrowing experience.We also point out that voluntary disclosures in the repeated game are associated with a lower probability of default.Our results are robust to the Heckman two-step estimation that addresses the self-selection effect and a specification designed to rule out the alternative explanation from reputation in the repeated game.Our study provides new insights into the real effects of costless,voluntary and unverifiable disclosures on lending decisions.

How Firms’Ownership Structure Impacts Online Peer-to-Peer Lending Performance in China

A dramatic surge in online peer-to-peer(P2P)lending emerged in China,where(under conditions of credit deficiency)it took only three years for the size of the P2P lending market in China to reach four times that of the United States and ten times that of the United Kingdom.The literature indicates that ownership structure is an important factor that influences P2P lending firms’performance,while research on the underlying mechanisms remain insufficient.This study analyzes the data of P2P lending companies between June 2016 and March 2017.The results demonstrate that although ownership structure has minimal direct effect on the turnover volume and number of lenders and borrowers,it moderates the effects of firm age,interest rate,and loan term on firm performance.These results enrich the property theory and shed light on how P2P lending firms with different ownership structures could succeed when there is institutional deficiency.

Do consumer internet behaviours provide incremental information to predict credit default risk?

The peer-to-peer lending industry has experienced recent turmoil,posing risks to fintech companies and banks.Based on a random sample of 33,669 borrowers who had downloaded peer-to-peer lending platforms prior to submitting loan applications to a wellknown fintech company,Du Xiaoman Financial(formerly Baidu Finance),this article evaluates the predictive power of borrowers’internet behaviours on credit default risk.After controlling for borrowers’basic characteristics that are widely used in academic research and enterprise practices,the coefficients of key factors selected from 3,100 variables are economically and statistically significant.The average Kolmogorov-Smirnov value of the prediction model calculated using the hold-out method is approximately 37.09%.The results remain robust in several additional analyses.This study indicates the importance of non-credit information,particularly borrowers’internet behaviours,in supplementing borrowers’credit records for both fintech companies and banks.