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.

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.

Does Mood Affect the Efficiency of Credit Approval？ Evidence from Online Peer-to-Peer Lending

Using a large dataset obtained from ＂Paipaidai,＂ an online peer-to-peer lending platform in China, we examine whether credit officers＇ mood affects the efficiency of credit approval from a perspective of individual decision-making. Refering to studies in psychology and financial economics, we employ season, temperature and weather as mood proxies, and control the variables related to the quality of the loan to study credit approval behavior under different mood conditions. The results suggest that the efficiency of credit approval by individual credit officers is significantly correlated with their mood--a positive mood improves efficiency, while a negative mood reduces it. Specifically, loans examined under better mood conditions （e.g., during spring, comfortable temperatures, and sunny days） have a significantly higher probability of approval, but a lower probability of default if approved; and those examined under poorer mood conditions show a lower probability of approval and a higher probability of default if approved. This effect of mood is even stronger when a loan application is more complex, atypical, or unusual to evaluate. Moreover, investor sentiment, denoted by closed-end fund premiums, has the same effect on credit approval as well.

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.

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.

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.

Peer-to-Peer Energy Trading Method of Multi-Virtual Power Plants Based on Non-Cooperative Game

The current electricity market fails to consider the energy consumption characteristics of transaction subjects such as virtual power plants.Besides,the game relationship between transaction subjects needs to be further explored.This paper proposes a Peer-to-Peer energy trading method for multi-virtual power plants based on a non-cooperative game.Firstly,a coordinated control model of public buildings is incorporated into the scheduling framework of the virtual power plant,considering the energy consumption characteristics of users.Secondly,the utility functions of multiple virtual power plants are analyzed,and a non-cooperative game model is established to explore the game relationship between electricity sellers in the Peer-to-Peer transaction process.Finally,the influence of user energy consumption characteristics on the virtual power plant operation and the Peer-to-Peer transaction process is analyzed by case studies.Furthermore,the effect of different parameters on the Nash equilibrium point is explored,and the influence factors of Peer-to-Peer transactions between virtual power plants are summarized.According to the obtained results,compared with the central air conditioning set as constant temperature control strategy,the flexible control strategy proposed in this paper improves the market power of each VPP and the overall revenue of the VPPs.In addition,the upper limit of the service quotation of the market operator have a great impact on the transaction mode of VPPs.When the service quotation decreases gradually,the P2P transaction between VPPs is more likely to occur.

PeerIS:基于Peer-to-Peer的信息检索系统

介绍了对等计算(peer-to-peer,简称 P2P)的特征、潜在优势和应用范围,指出了当前 P2P 数据共享系统(甚至缺乏语义)和粗粒度(文件水平)共享等局限性.针对这种现状,提出了基于 P2P 的信息检索,既可充分发掘 P2P 技术的潜在优势,克服传统信息检索系统的可伸缩瓶颈等问题,又可实现 P2P 数据共享系统语义丰富和细粒度的信息检索与共享;并开发出 PeerIS:基于 P2P 的信息检索系统.描述了 PeerIS 的整体构架与节点的内部结构;重点阐述了 PeerIS 的通信机制、自配置机制、查询机制以及自适应路由机制等实现关键技术;并用实验证明了 PeerIS 的优异性.

应用于移动互联网的Peer-to-Peer关键技术

对现有的应用于移动互联网的P2P技术方面的研究进行了分析.首先介绍了P2P技术和移动互联网的概念,并提出将P2P技术应用在移动互联网所面临的挑战和应用模式.其次,分别针对集中式架构、超级节点体系架构和ad hoc架构对应用于互联网的P2P网络体系架构进行了阐述.再其次,针对移动终端的两种接入模式,分别在资源定位算法和跨层优化两个方面进行了介绍.对各关键技术的特点进行了详细的分析,指出其存在的不足.最后,对未来的工作进行了展望.

基于贝叶斯网络的Peer-to-Peer识别方法

网络业务分类与识别是网络管理、网络规划和安全的必要途径,而Peer-to-Peer(P2P)流量由于采用伪装端口、动态端口以及应用层加密,已成为业务分类与识别的主要难点.该文提出了P2P业务的精确识别方法,通过对流统计特性的分析,提取相关特征属性,应用遗传算法选取最优特征属性子集,并采用贝叶斯网络机器学习方法识别P2P流量.实验表明K2,TAN和BAN能有效快速地识别P2P业务,分类精度高达95%以上,很大程度上优于朴素贝叶斯分类和BP神经网络方法.同时该系统具有可扩展性,能够识别未知的P2P流量,并适用于实时分类识别环境.

分布式Peer-to-Peer网络Gnutella模型研究

计算机对等联网(peer-to-peernetwork,P2P)技术是目前新一代网络技术研究的活跃领域,而P2P网络模型研究是P2P网络技术研究的重要环节。该文着重介绍了Gnutella网络模型的体系结构及工作原理,分析了其优缺点。并对其未来发展改进前景进行了展望。

Internet中Peer-to-Peer应用流量测量与分析

Peer to Peer网络(简称p2p)作为一种新型的覆盖网络引起了越来越多研究者的兴趣。本文介绍了在我国进行的骨干互联网上p2p网络流量测量。与现有国外研究不同,本文的数据来源于核心路由器,因此克服了它们的缺陷。其研究集中在汇聚流中的3个周期性尖峰群、不同主机发送或接收流量的重尾分布、p2p流量的长相关特性以及提出了ADTE的估计方法来区分信令和数据流量。本文的研究也显示出Napster在p2p流中占大部分,这暗示着超级节点和阶层式拓扑较纯p2p结构潜在的优势。同时,观察到在我国p2p的流量仅占Internet总流量的1%弱,这个值跟国外的数据有很大区别。我们分析了其中的原因并希望该结论为我国p2p软件的发展提供参考。

主动peer-to-peer网络架构

针对当前peer-to-peer(P2P)网络只能通过固定的协议和服务有限地利用节点资源的弱点提出了主动P2P网络(APN)架构.结合P2P网络技术和主动网络技术,核心思想是把P2P网络的服务和协议代码封装在数据包中,随着数据包一起传送,并由需要的节点加载和执行,由此完成服务和协议的动态扩充和部署.通过在APN上部署自动代码理解和查询系统,验证了APN比现有P2P网络更能充分利用节点资源,更灵活,易于管理,同时,APN在安全性和性能方面的相对降低是可接受的.

基于Peer-to-Peer的单点登录服务研究和实现

使用Peer-to-Peer模型设计了一种新的单点登录服务模型,并在国家科技基础条件平台门户应用系统的身份认证系统中实际应用。该方案(NPSSO)把身份认证分配到多个身份认证服务器上,每个身份认证服务器均由一个单独的管理系统来管理,能够大大提高容错和抗攻击能力,并且能保证系统在用户和服务的数量上具有良好的可扩展性。最后,对该套单点登录方案进行了性能分析和相关工作的比较。

基于遗传算法的Peer-to-Peer路由算法R-GA

路由算法是制约Peer-to-Peer系统整体性能的关键因素之一。目前大多数路由算法无法保证全局收敛,而链路延迟、费用、网络带宽等现实制约因素往往在选路时被忽略。针对上述问题,提出了基于遗传算法的R-GA路由算法。通过适度函数和遗传因子,R-GA可以快速地实现全局收敛。同时将链路的延迟、费用、带宽等参数插入到适度函数中,避免了盲目路由。仿真试验的结果表明,R-GA路由算法在大规模Peer-to-Peer系统中是高效和可扩展的。

一种基于Peer-to-Peer的分布式文件共享框架

主要探讨了在基于非集中式Peer to Peer网络中实现分布式文件共享和传输管理需要考虑的主要因素，给出了基于Gnutella框架的改进之处以及实现细节，并提出了以通信自相似性和拓扑幂律分布相结合的特性模型。

基于分布式聚类的Peer-to-Peer路由算法

提出了分布式聚类算法,该算法自动对地理邻近的对等结点聚类,消除了集中式聚类算法的单点瓶颈问题,及分布式Binning算法的landmark放置问题;提出了一种基于该聚类的邻居路由算法,该算法消除了na?ve算法路由效率低、IP-snoop算法实现困难及处理开销大、directed算法需要处理超级节点失效时容错问题。实验结果表明,基于分布式聚类的Peer-to-Peer(P2P)路由算法比现有的结构化路由算法减少了近一倍的路由跳转和路由时延。