婚姻状态与网络借贷中的信用识别——来自"人人贷"平台的经验证据

在P2P网络借贷模式中,不同的借款人个人特征信息具有不同的价值.利用中国P2P企业"人人贷"的数据,本文探讨了婚姻在网络借贷市场中的信用识别价值.实证结果表明,相对于未婚人士,已婚人士的借款成功率更高,同时贷款违约率也更低.同时,不同特征的人结婚,对于提高自己信用所起到的作用也是不相同的.具体而言,对于提高借贷信用而言,男性结婚不如女性结婚;高年龄借款者结婚强于年轻人结婚;高学历借款者结婚不如低学历借款者结婚.但对于借款违约率,不同特征借款者结婚的影响差异不大.     

P2P网络借贷平台特征对借款人借款利率的影响研究

随着P2P网络借贷行业的飞速发展,行业存在的问题也日益凸显,P2P平台诈骗、跑路、提现困难等问题频频出现,日渐成为P2P网络借贷行业的“新常态”.投资者不仅承担着来自直接借款人的违约风险,同时还承担着来自P2P网络借贷平台的各种风险.在投资过程中,投资者最先感受到的就是来自P2P平台的风险,平台的特征会影响投资者对于该平台的风险的判断,从而影响投资者对投放在该平台上的借款项目的预期收益率.本文旨在通过研究特定的平台特征对借款人的借款利率(也可称为投资人收益率)的影响,为借款人和投资人选择平台提供建议,同时为平台监管提供思路.     

北京市P2P网络借贷平台违约行为影响因素实证研究

P2P网络借贷平台在缓解小微企业融资约束问题中做出了巨大贡献,但与此而来的是P2P网络借贷平台日益增长的高违约率.本文运用Logistic回归模型,研究发现平台注册资本、平台存续时间、平均年化收益率、资金保障措施、平均借款期限等因素对北京市P2P网络借贷平台违约行为产生显著影响,并提出了增进北京市P2P网络借贷平台健康发展的对策建议.     

基于logistic模型的P2P网络借贷风险因素实证分析与对策

P2P网络借贷的风险类型有信用风险、流动性风险、技术风险和生态风险。利用logist ic模型,对P2P网络借贷的风险因素进行了实证分析,结果显示:P2P网络借贷的风险因素分别为:借款规模、使用利率、借款时间、信用分值。其中,借款规模对P2P网络借贷风险影响微弱;使用利率、借款时间对风险具有助长的作用;信用分值具有降低P2P网络借贷风险程度的作用,据此文章提出了防范P2P网络借贷风险的若干建议。     

P2P网络借贷平台监管问题及建议

P2P网络借贷平台,是p2p借贷与网络借贷相结合的金融服务网站。P2P借贷是peer to peer lending的缩写,正式的中文翻译为“人人贷”,主要是指个人通过第三方平台在收取一定费用的前提下向其他个人提供小额借贷的金融模式。客户对象主要有两方面,一是将资金借出的客户,另一个是需要贷款的客户。网络借贷指的是借贷过程中,资料与资金、合同、手续等全部通过网络实现的金融模式。     

我国P2p网络借贷发展特征及策略分析

互联网金融的核心是金融脱媒,P2p网络借贷作为互联网金融的主流模式,在国内外呈爆发式增长。文章在探索P2p网络借贷发展起源的基础上,分析了国内P2p网络借贷平台的主要特征,并从理论研究、政策制定、风险教育、征信系统等视角提出了适合我国P2p网络借贷发展需要的具体策略。     

大数据时代P2P网络借贷中个人信息安全面临的威胁及其对策

在大数据时代,P2P网络借贷作为互联网金融的一部分,其平台数量呈井喷式增长.P2P网络借贷的出现无疑方便了人们对资金的需求,提高了个人资金的利用率,但P2P网络借贷中个人信息面临着滥用与泄露的严重威胁.本文介绍了P2P网络借贷的发展及借贷流程,从投资人和借款人的安全意识、P2P网络借贷平台的内控与系统、黑客攻击、法律法规等方面分析了P2P网络借贷中个人信息安全面临的威胁,并提出了相应的对策,为政府决策提供参考,以促进P2P网络借贷健康、可持续发展.     

P2P行业潜在风险研究

随着我国经济社会的不断发展和互联网技术的普及,互联网和金融业逐渐融合,加速了金融脱媒,互联网金融成了金融领域新的参与形式.在互联网金融的众多新兴业态中,P2P行业得到了快速的发展.其借助互联网平台,定位于小微企业及个人投资者,借贷匹配快捷,近年来呈现井喷增长态势.但从实际来看,P2P行业存在信用、运营、网络安全、资金流动性以及平台破产等方面的潜在风险.因此,本文从我国P2P行业发展现状入手,对潜在风险进行了深入的分析,并在此基础上提出应对的有效策略,以期对丰富投资者的投资渠道,促进我国P2P行业健康持续发展有所裨益.     

我国P2P网络借贷债权人的法律保护

P2P网络借贷作为一种融合互联网技术并实现金融脱媒的创新型金融模式,打破了传统民间借贷的时空限制,实现了小额资金在更大空间的最优配置.我国自2007年第一家P2P网络借贷平台拍拍贷成立以来,网贷平台不断涌现、借贷资金规模不断攀升.但是由于我国P2P网络借贷线上的交易模式、社会信用机制尚不健全,资金托管相对混乱,自2013年以来频繁出现平台倒闭、负责人跑路等事件,严重威胁债权人利益.本文对我国P2P网贷平台的法律性质、经营模式及所涉法律关系进行梳理,具体分析债权人所面临风险,提出了保护债权人权利的法律建议.     

基于BP神经网络的P2P网贷借款者的信用风险评估

近两年国内P2P网贷平台迅速发展,信用风险开始迅速突显,问题平台持续显现。文章通过对国内P2P网络贷款平台人人贷进行研究,建立了一种针对P2P网贷借款者的信用评估指标体系,用于评估借款者信用的优劣。通过BP神经网络算法进行仿真及优化,得到了优异的信用评估效果,同时利用传统Logist ic二元回归模型对相同数据进行检验,其预测准确率不及BP神经网络模型,证明BP神经网络模型更加适合P2P网络贷款平台进行信用风险评估。     

Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending

Online Peer-to-Peer (P2P) lending has emerged recently. This micro loan market could offer certain benefits to both borrowers and lenders. Using data from the Lending Club, which is one of the popular online P2P lending houses, this article explores the P2P loan characteristics, evaluates their credit risk and measures loan performances. We find that credit grade, debt-to-income ratio, FICO score and revolving line utilization play an important role in loan defaults. Loans with lower credit grade and longer duration are associated with high mortality rate. The result is consistent with the Cox Proportional Hazard test which suggests that the hazard rate or the likelihood of the loan default increases with the credit risk of the borrowers. Finally, we find that higher interest rates charged on the high-risk borrowers are not enough to compensate for higher probability of the loan default. The Lending Club must find ways to attract high FICO score and high-income borrowers in order to sustain their businesses.     

Evaluating borrower’s default risk in peer-to-peer lending: evidence from a lending platform in China

Recent years have witnessed the popularity of online peer-to-peer lending, which allows individuals to borrow from and lend to each other on an Internet-based platform. Using data from a large P2P platform in China, this article explores the factors that determine the default risk based on the demographic characteristics of borrowers. Moreover, we propose a credit risk evaluation model, which can quantify the default risk of each P2P loan. Empirical results reveal that gender, age, marital status, educational level, working years, company size, monthly payment, loan amount, debt to income ratio and delinquency history play a significant role in loan defaults. Finally, we analyse the relationship between default risk and these contributory variables, and the possible causes are also discussed in this study.     

基于分类最优原理的小企业信用风险评价

基于分类最优原理进行小企业信用风险评价,即以违约样本和非违约样本的重心距离最大为目标函数,以指标的三角模糊熵及变异系数组成的指标权重区间为约束条件,确定指标的组合权重,评价小企业信用风险状况。应用实例结果表明:利用该方法评价小企业的信用风险,能够更好地区分违约客户与非违约客户,使模型的判别精度有所提高。     

Two stages credit evaluation in bank loan appraisal

Traditionally, banks conduct standard credit evaluation such as credit scoring following the receipt of loan request and make the accept/reject decision accordingly. This research explores the possibility of two stages credit evaluation in lending process. When the evaluation cost drops below the trigger cost, it pays to conduct the second-stage loan appraisal. We derive two trigger cost thresholds for borrowers who are rated as credible and default in the first stage, respectively. Contingent on the share of good borrowers relative to the bad ones, the optimal strategy of the bank can be differentiated to implement second-stage evaluation on either (1) both types, or (2) only one type, or (3) neither type of the borrowers. We find that during severe economic contractions or in geographic areas/industries which are in deep troubles, whilst the borrowers who repay the loan are out-numbered by the borrowers who fail to pay, the trigger cost for good borrower is higher than that of default borrower. In this scenario, the banks are more inclined to undertake the second-stage credit evaluation on good borrowers. On the other hand, if the percentage of credible borrowers is higher than that of default borrowers, the trigger cost for good borrower lies below the trigger cost of default borrower. As a result, the banks are less inclined to undertake the second-stage evaluation on good borrowers.     

Lending policies of informal, formal and semiformal lenders

This paper compares lending policies of formal, informal and semiformal lenders with respect to household lending in Vietnam. The analysis suggests that the probability of using formal or semiformal credit increases if borrowers provide collateral, a guarantor and/or borrow for business‐related activities. The probability of using informal credit increases for female borrowers. It also appears that the probability of using formal credit increases in household welfare up to a certain threshold, but at a decreasing rate. In addition, the paper discerns the determinants of probability of default across lender types. Default risk of formal credit appears to be strongly affected by formal loan contract terms, e.g., loan interest rate and form of loan repayment, whereas default risk on informal loans is significantly related to the presence of propinquity and other internal characteristics of the borrowing household. Overall, the study raises several important implications for the screening, monitoring and enforcement instruments that may be employed by different types of lenders.     

On the rationale of bank lending in pre-crisis Thailand

Evidence from credit files is provided to examine bank lending determinants of Thai commercial banks. Their lending practice follows reasonable patterns as a standard set of variables, including indirect risk variables, explains much of the variance in interest rate spread. Reflecting institutional differences with mature markets, we find a higher importance of relationship banking and risk control via credit availability. Information about later default reveals prudent relationship lending. However, banks could have made better use of available information about borrowers’ riskiness. These findings do not support a general verdict of bad banking but indicate room to improve lending decisions.     

微型企业信用评价指标体系的构建

针对企业信用评价指标不服从正态分布的非参数特征问题,构建了微型企业信用评价指标筛选模型。具体而言:利用基于Brown-Mood中位数检验与Moses方差检验的组合模型双重筛选显著区别违约状态的微型企业信用评价指标,该做法反映了将分布中心与离散程度两个分布特征进行组合来筛选指标的思路;通过将Kendall秩相关检验与Brown-Mood中位数检验相结合,构建信息重复指标筛选模型,该做法反映了保留最显著区别违约状态指标的筛选思路。最后构建了包括22个指标的微型企业信用评价指标体系。     

The signaling effects of education in the online lending market: Evidence from China

In this study, we use data from an online lending platform named Xinxindai in China to empirically study the signaling effects of education for the default risk of borrowers. Three dependent variables are created, namely, the probability of default, overdue payments and overdue amount, and probit models, count models and Tobit models are employed correspondingly. The number of universities in the “211 Project” of China at the city level is employed as the instrumental variable. The empirical evidence shows that education generally plays a strong signaling role in the identification of borrowers’ default risk in China. The negative marginal effect of education declines as borrowing times increase and as the marketization of regions deepens. This study helps to fill an important gap in the existing literature. Platforms and lenders can use educational level for reference in identifying the default risk of borrowers.     

Parametric versus nonparametric methods in risk scoring: an application to microcredit

The importance of credit access to improve economic opportunities in developing markets is well established in the literature. However, there exists a strong need to mitigate adverse selection problems in microlending. A risk scoring model that more accurately predicts the likelihood of repayment of potential borrowers can help address this market imperfection and to benefit both lenders and borrowers. This paper compares the performance of nonparametric versus semiparametric and traditional parametric risk scoring models based on default probabilities. We show the advantages of relying on less structured, data-driven methods for risk scoring using both simulated data and data from credit loans granted to small and microenterprises in rural Peru. The estimation results indicate that nonparametric methods lead to a better evaluation of credit worthiness and can help prevent including potential “bad” borrowers and excluding “good” borrowers from sensitive microcredit markets.     

Adverse selection as an explanation of credit rationing and different lender types

An adverse selection model is utilized to demonstrate that informational asymmetry may make it wealth optimal for the financial intermediary (FI) to credit ration and to rationalize the existence of different lenders in the credit market. The crucial assumption is that borrowers differ in their tolerance for a lender-imposed default penalty, the severity of which also varies with the lender. The credit rationing portion proves that the FI will: 1) be forced by a binding regulatory constraint to overinvest in capital; 2) ration its worst risk class borrowers; 3) establish its optimal loan interest rate on the basis of the average quality of its loans and the interest rate elasticity of the borrower demand in its best risk category; and 4) decrease the total loan volume and increase the loan interest rate due to an increase in the capital requirement, but the effect on the default risk quality of its loan portfolio is ambiguous. The existence result is that if a lender has a high default penalty, he can charge a lower rate and attract only “good” borrowers, i.e., heterogeneous lender types encourage the screening of borrowers and vice versa.