台湾地区联征中心个人信用评分体系经验借鉴

个人信用评分具有风险排序的功能,是信用信息服务的重要手段,本文从研究台湾地区个人信用评分的发展模式入手,系统分析了台湾地区个人信用评分体系建设成果及经验,以其为大陆地区开展相关研究及实践提供有益参考.     

我国信用局个人信用评分发展研究

信用局个人信用评分作为信用风险管理的一个重要工具在国外得到广泛应用.我国个人基础信用信息数据库已经建成,开发信用局个人信用评分对我国社会信用体系建设和金融机构的风险管理有重要意义.在分析信用局个人信用评分特点、意义及其发展现状的基础上,提出我国信用局个人信用评分发展的几点建议.     

美国的个人信用制度及其启示

本介绍了美国信用制度的起源，发展和运作，介绍了FICO信用分模型的原理、评分方法和在消费信贷决策中的使用方法。本还介绍了美国法律对个人信用制度的一些规定以及美国公众对个人信用制度的批评意见。最后，本借鉴美国个人信用制度正反两方面的经验，对我国个人信用制度的建设提出了几点建议。     

个人信用评分系统应用现状与展望

一、个人信用评分系统概述 1.个人信用评分的现实意义 (1)个人信用评分是世界上普遍采用的评估个人信用风险的方法.在欧美发达国家,信用评分专业公司根据银行的业务需要开发不同的信用评分模型.因为每一家银行的业务经营存在差异,从目标客户的选择到客户服务水平都可能有所不同,这使得各个银行开发模型所依赖的数据不同.实践证明,个人信用风险的管理有三个突破,每个突破都会给银行带来较大的利润增长.这三个突破是信用评分、自动化管理系统和决策优化.     

对我国个人信用评分及监管的分析与思考

个人信用评分是征信或一些市场机构提供的一种产品或服务。我国开展此项工作已经10多年,对经济社会发展起到了积极作用。但目前仍存在一些问题,应继续加强研究、及时解决。一、个人信用评分的概念和原理征信领域的个人信用评分是一个舶来品。在美国《公平信用报告法》(Fair Credit Reporting Act)中。     

一种全新的两阶段个人信用评分方法探究

目前我国还没有一套规范的个人信用评分指标体系和方法。本文利用真实的个人消费信贷数据,首先建立了个人信用评分的多元线性判别分析模型和BP神经网络模型,然后将线性判别分析模型的结果与其它变量一起作为输入变量建立了混合两阶段个人信用评分模型。实证研究表明,混合两阶段个人信用评分模型相对于前两种单一模型能同时满足预测精度和稳健性的双重要求,从而,突破了通常应用单一模型于个人信用评分领域的局限。     

中美银行个人信用评分体系比较与借鉴

个人信用评分体系是个人消费信贷的一项基础性工作,是大规模开展消费信贷的重要环节。个人信用评分体系信息点的设置是否科学、合理、实用,对于保证消费信贷的质量至关重要。本文试将在我国率先推出个人信用评分系统的中国建设     

美国个人信用评分制度及对我国的启示

2006年1月，由中国人民银行组织建设的全国统一的个人信用信息基础数据库正式运行，标志着我国信用体系建设进入了新阶段。但由于我国个人信用数据的收集整理工程刚刚开始，目前还存在许多亟待解决的问题。为此，笔者对美国个人信用评分制度进行了研究，希望借鉴发达国家个人信用评分制度的经验做法，为我国完善个人信用评估体系建设开拓思路。     

P2P网贷的信用风险评估研究

互联网背景下,P2P网贷高速发展,个人信用风险评估尤为重要,但我国还没有建立完善的个人信用评分体系,传统的风险评估方法很难达到满意的效果。本文借鉴以往的相关研究,综合考虑指标体系设置的各项原则,选取了婚姻状况、年龄、工作年限等十项指标,运用Logistic回归模型,通过实证分析,对个人信用风险进行了评估。     

怎样才能拥有良好的个人信用

随着市场经济体制的不断发展和完善,人们对个人信用越来越重视,拥有良好的信用记录已经成为公民在经济活动中不可或缺的"通行证",个人信用这种无形资产开始发挥其特有的使用价值.本文从如何避免不良信用的产生以及怎样提高个人信用等级入手,提供了许多较为实用的建议.     

行为评分模型在个人信用评估应用中的实证研究

本文将Logistic模型和马尔科夫链模型相结合,在Logistic模型的基础上综合考虑客户行为状态的变化,将其加入信用评估模型中,得到优于单一运用Logistic模型的结果,据此得到的动态信用评分,为商业银行信贷决策及客户关系管理决策提供更有力的依据。     

基于拒绝推论的小企业信用评分模型研究

在小企业信用评分模型的构建中,因数据缺失和样本选择性偏差可能导致模型参数估计有偏,对模型的预测能力和应用会有很大影响。本文利用从万德数据库中筛选出的小企业信息资料,模拟银行信贷筛选,产生带有缺失数据的模拟信贷样本,利用Heckman二阶段模型预测新的信用评分模型,将其结果与忽略缺失数据的审查模型和基于完全信息的标准模型进行比较。结果显示,Heckman二阶段模型的表现优于直接忽略缺失样本数据的审查模型,更接近标准模型的结果。这表明拒绝推论能够有效解决信用评分建模中数据缺失导致的样本选择偏差,提高信用评分模型的有效性和预测能力。     

Reject inference in consumer credit scoring with nonignorable missing data

We generalize an empirical likelihood approach to deal with missing data to a model of consumer credit scoring. An application to recent consumer credit data shows that our procedure yields parameter estimates which are significantly different (both statistically and economically) from the case where customers who were refused credit are ignored. This has obvious implications for commercial banks as it shows that refused customers should not be ignored when developing scorecards for the retail business. We also show that forecasts of defaults derived from the method proposed in this paper improve upon the standard ones when refused customers do not enter the estimation data set.     

The Surprising Use of Credit Scoring in Small Business Lending by Community Banks and the Attendant Effects on Credit Availability, Risk, and Profitability

The literature has documented a positive relationship between the use of credit scoring for small business loans and small business credit availability, broadly defined. However, this literature is hampered by the fact that all of the studies are based on a single 1998 survey of the very largest U.S. banking organizations. This paper addresses a number of deficiencies in the extant literature by employing data from a new survey of the use of credit scoring in small business lending, primarily by community banks. The survey evidence suggests that the use of credit scores in small business lending by community banks is surprisingly widespread. Moreover, the scores employed tend to be the consumer credit scores of the small business owners, rather than the more encompassing small business credit scores that include data on the firms as well as on the owners. Our empirical analysis suggests that credit scoring is associated with an initial increase in small business lending activity that moderates over time and no change in the quality of the loan portfolio. Supplementary analysis suggests that the use of credit scores for small business lending has a negative initial effect on community bank profitability that moderates over time.     

A two-stage dynamic credit scoring model,based on customers’ profile and time horizon

As credit card usage has expanded rapidly worldwide, credit scoring has become a very important task for banks, which can benefit from reducing possible risks of default. Credit scoring models help decision makers to decide whether to issue a credit card to a new applicant on the basis of both financial and nonfinancial criteria. The scope of the current study is to develop a dynamic scoring model that (a) estimates the credit performance of an applicant using generalised linear models and (b) accommodates the changes of a borrower's characteristics after the issuance of the credit card and forecasts the time of default using survival analysis.     

Economic benefit of powerful credit scoring

We study the economic benefits from using credit scoring models. We contribute to the literature by relating the discriminatory power of a credit scoring model to the optimal credit decision. Given the receiver operating characteristic (ROC) curve, we derive (a) the profit-maximizing cutoff and (b) the pricing curve. Using these two concepts and a mixture thereof, we study a stylized loan market model with banks differing in the quality of their credit scoring model. Even for small quality differences, the variation in profitability among lenders is large and economically significant. We end our analysis by quantifying the impact on profits when information leaks from a competitor’s scoring model into the market.     

A credit scoring model for Vietnam's retail banking market

As banking markets in developing countries are maturing, banks face competition not only from other domestic banks but also from sophisticated foreign banks. Given the substantial growth of consumer credit and increased regulatory attention to risk management, the development of a well-functioning credit assessment framework is essential. As part of such a framework, we propose a credit scoring model for Vietnamese retail loans. First, we show how to identify those borrower characteristics that should be part of a credit scoring model. Second, we illustrate how such a model can be calibrated to achieve the strategic objectives of the bank. Finally, we assess the use of credit scoring models in the context of transactional versus relationship lending.     

基于数据挖掘的持卡人信用风险管理研究

目前的信用卡信用风险研究主要是如何提高模型的预测准确率。针对银行信用卡数据的异质性和信用数据的高度非线性,本文提出了对持卡人信用风险管理的混合数据挖掘方法。该方法包含两个阶段,在聚类阶段,样本数据被聚成同质的类,删除孤立点,不一致样本点重置标签,使样本更具有代表性;在分类阶段,基于样本进行训练生成支持向量机分类器法,对待分样本分类。基于实际数据进行了数值实验,并根据各类样本的特点提出了相应的风险管理策略。     

Modelling small‐business credit scoring by using logistic regression,neural networks and decision trees

Previous research on credit scoring that used statistical and intelligent methods was mostly focused on commercial and consumer lending. The main purpose of this paper is to extract important features for credit scoring in small‐business lending on a dataset with specific transitional economic conditions using a relatively small dataset. To do this, we compare the accuracy of the best models extracted by different methodologies, such as logistic regression, neural networks (NNs), and CART decision trees. Four different NN algorithms are tested, including backpropagation, radial basis function network, probabilistic and learning vector quantization, by using the forward nonlinear variable selection strategy. Although the test of differences in proportion and McNemar's test do not show a statistically significant difference in the models tested, the probabilistic NN model produces the highest hit rate and the lowest type I error. According to the measures of association, the best NN model also shows the highest degree of association with the data, and it yields the lowest total relative cost of misclassification for all scenarios examined. The best model extracts a set of important features for small‐business credit scoring for the observed sample, emphasizing credit programme characteristics, as well as entrepreneur's personal and business characteristics as the most important ones. Copyright © 2005 John Wiley & Sons, Ltd.