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.

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。

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.

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。

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。

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,

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.

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。

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.

Peer-to-Peer网络中文件查询的研究

随着P2P网络应用研究的热化,文件查询即给出文件名后如何确定文件的存放位置,已经成为大规模分布式网络中最常遇到的问题之一。文件查询的效率直接影响到整个P2P网络的性能。本文从P2P网络的基本现状出发,给出了目前文件查询在非结构化,结构化和松散结构化P2P网络中的解决方案并进行了深入分析和比较。文章最后指出了目前P2P网络中文件查询算法的缺点和今后的研究方向。

A novel mathematical model on Peer-to-Peer botnet

Peer-to-Peer （P2P） botnet has emerged as one of the most serious threats to lnternet security. To effectively elimi- nate P2P botnet, a delayed SEIR model is proposed,which can portray the formation process of P2P botnet. Then, the local stability at equilibria is carefully analyzed by considering the eigenvalues＇ distributed ranges of characteristic equations. Both mathematical analysis and numerical simulations show that the dynamical features of the proposed model rely on the basic re- production number and time delay r. The results can help us to better understand the propagation behaviors of P2P botnet and design effective counter-botnet methods.

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.

Analytical Comparison of Resource Search Algorithms in Non-DHT Mobile Peer-to-Peer Networks

One of the key challenges in ad-hoc networks is the resource discovery problem.How efciently&quickly the queried resource/object can be resolved in such a highly dynamic self-evolving network is the underlying question?Broadcasting is a basic technique in the Mobile Ad-hoc Networks(MANETs),and it refers to sending a packet from one node to every other node within the transmission range.Flooding is a type of broadcast where the received packet is retransmitted once by every node.The naive ooding technique oods the network with query messages,while the random walk scheme operates by contacting subsets of each node’s neighbors at every step,thereby restricting the search space.Many earlier works have mainly focused on the simulation-based analysis of ooding technique,and its variants,in a wired network scenario.Although,there have been some empirical studies in peer-to-peer(P2P)networks,the analytical results are still lacking,especially in the context of mobile P2P networks.In this article,we mathematically model different widely used existing search techniques,and compare with the proposed improved random walk method,a simple lightweight approach suitable for the non-DHT architecture.We provide analytical expressions to measure the performance of the different ooding-based search techniques,and our proposed technique.We analytically derive 3 relevant key performance measures,i.e.,the avg.number of steps needed to nd a resource,the probability of locating a resource,and the avg.number of messages generated during the entire search process.

Study on semantic-oriented hybrid indexing strategy of resource metadata in peer-to-peer network

Applying ontology to describe resource metadata richly in the peer-to-peer environment has become current research trend. In this semantic peer-to-peer environment, indexing semantic element of resource description to support efficient resource location is a difficult and challenging problem. This paper provided a hybrid indexing architecture, which combines local indexing and global indexing. It uses community strategy and semantic routing strategy to organize key layer metadata element and uses DHT (distributed hash table) to index extensional layer metadata element. Compared with related system, this approach is more efficient in resource location and more scalable.

KRBKSS: a keyword relationship based keyword-set search system for peer-to-peer networks

may incur significant bandwidth for executing more com- plicated search queries such as multiple-attribute queries. In order to reduce query overhead, KSS (keyword-set search) by Gnawali partitions the index by a set of keywords. However, a KSS index is considerably larger than a standard inverted index, since there are more word sets than there are individual words. And the insert overhead and storage overhead are obviously un- acceptable for full-text search on a collection of documents even if KSS uses the distance window technology. In this paper, we extract the relationship information between query keywords from websites’ queries logs to improve performance of KSS system. Experiments results clearly demonstrated that the improved keyword-set search system based on keywords relationship (KRBKSS) is more efficient than KSS index in insert overhead and storage overhead, and a standard inverted index in terms of communication costs for query.

Rate-distortion optimized bitstream switching for peer-to-peer live streaming

Peer-to-peer (P2P) technology provides a cost-effective and scalable way to distribute video data. However, high heterogeneity of the P2P network, which rises not only from heterogeneous link capacity between peers but also from dynamic variation of available bandwidth, brings forward great challenge to video streaming. To attack this problem, an adaptive scheme based on rate-distortion optimization (RDO) is proposed in this paper. While low complexity RDO based frame dropping is exploited to shape bitrate into available bandwidth in peers, the streamed bitstream is dynamically switched among multiple available versions in an RDO way by the streaming server. Simulation results show that the proposed scheme based on RDO achieves great gain in overall perceived quality over simple heuristic schemes.

A PEER-TO-PEER INCENTIVE SCHEME FOR OVERCOMING FREE RIDING

Free riding has a great influence on the expandability,robustness and availability of Peer-to-Peer(P2P) network.Controlling free riding has become a hot research issue both in academic and industrial communities.An incentive scheme is proposed to overcoming free riding in P2P network in this paper.According to the behavior and function of nodes,the P2P network is abstracted to be a Distributed and Monitoring-based Hierarchical Structure Mechanism(DMHSM) model.A utility function based on several influencing factors is defined to determine the contribution of peers to the whole system.This paper also introduces reputation and permit mechanism into the scheme to guarantee the Quality of Service(QoS) and to reward or punish peers in the network.Finally,the simulation results verify the effectiveness and feasibility of this model.