《信用评分模型技术与应用》

近几年．消费信贷产业蓬勃发展的同时，它也给金融管理带来了深刻挑战。由陈建撰写的本书，着眼于介绍国际上先进的信用评分模型技术，引进已经被发达市场实践反复证明了的成功经验，传播行业中经典管理理念和成熟最佳操作规则。这对于我们了解和借鉴国际上先进的信用管理技术很有帮助。在写作体系上．作者能够理论联系实务。文字深入浅出．是一本读者比较适用的参考书。     

信用评分模型综述

信用评分模型是欧美消费信贷管理中广泛应用的技术手段,是银行、信用卡公司、汽车贷款公司、住房贷款公司、个人贷款公司、电信公司、公共事业公司、保险公司等涉及消费信用的企业实体最重要的核心管理技术之一。信用评分模型运用先进的数据挖掘技术和统计分析方法, 通过对消费     

信用评分模型与方法

信用评分是一种统计或定量方法，用于预测贷款申请或评估现存借款人将发生违约或拖欠的概率。信用评分的目的是帮助银行信用评估部门量化和管理提供信用的金融风险，以便他们能够更好、更客观地作出借贷决策。信用评分的发展可以总结如下。[第一段]     

信用评分模型在个人信贷业务中的应用

信用风险是金融业面临的最大风险。信用评分模型是有效识别个人信用风险的先进技术。本文结合中国实际,提出如何选择信用评分模型,如何在个人信贷业务中嵌入信用评分模型,同时,分析了信用评分模型在应用中的制约因素及对策。     

我国小企业信用评分系统开发策略研究

商业银行将信用评分技术引入小企业贷款业务中,能够揭示小企业的信用风险、减少信息不对称、扩大信贷范围和增强发放贷款的意愿,获得带来利润的客户群,进而促进小企业融资难题的解决,本文从提高小企业贷款发放体系效率的视角,借鉴国外小企业评分系统的开发经验,提出我国小企业评分系统开发的策略构想。     

信用评分模型在汽车和住房贷款中的应用

汽车贷款是一个庞大的金融产业。在美国，几乎每一个成年人都拥有一部以上的汽车，而且绝大多数汽车的购买都通过贷款途径：要么是通过汽车生产商的贷款融资子公司，如通用汽车贷款公司、福特汽车贷款公司、丰田汽车贷款公司、本田汽车贷款公司等，要么是通过银行的汽车贷款业务部门。除了新车的贷款融资外，汽车贷款还包括二手车贷款、汽车租赁、游艇贷款等。在中国，汽车贷款也是一个方兴未艾，潜力巨大的新兴金融产业。     

信用评分模型在信用卡生命周期管理中的应用

信用评分模型的一个最重要的应用领域是信用卡的生命周期管理，信用卡业务具备发展和应用信用评分模型的两个特征：一是数据量庞大，数据中蕴含着丰富的反映消费信用特征和表现的信息．这使发展信用评分成为可能，二是业务量庞大，众多银行发行几百万，几千万张信用卡，但每张信用卡的贷款量较小，这使应用信用评分模型进行批量化、自动化管埋成为必要。     

商业银行信用评分体系的风险管理职能及其影响

随着金融市场的发展，银行的业务量不断扩张，这对于银行的业务处理能力和相应的风险管理能力提出了挑战。     

信用评分模型的拒绝偏差与Heckit纠正

信用评分模型在构建过程中,样本数据通常仅是那些贷款申请被接受,贷款违约与否信息能被观测到的数据,这一样本数据缺陷导致模型在应用中出现被称为拒绝偏差的参数估计偏误,影响了模型的预测准确度。本文的研究采用微观计量经济学中的Heckit方法,借助商业银行的住房按揭贷款微观数据对信用评分模型的拒绝偏差问题进行实证研究。研究结果表明住房按揭贷款信用评分模型存在拒绝偏差,而经过Hecikt方法纠正的信用评分模型能有效地提高模型的预测能力。     

个人信用评分有力推进信贷风险管理转型

随着我国全面建设小康社会进程的加速,社会财富不断增加,个人客户对银行的个人业务提出了更多需求,进一步拉动了个人金融服务的增长.由于个人业务具有笔数多、单笔金额小,数据丰富的特点,所以要对其采用精细化、智能化、自动化的管理模式.然而长期以来,我国商业银行个人业务的经营管理方式一直比较粗放.中国人民银行最新研发的全国个人信用评分,可以对个人客户在未来一定时期内的违约概率进行评估和预测.它可以有效防范风险,为商业银行实现个人信贷风险管理转型提供先进的风险管理技术支持.     

Impact of anti-discrimination laws on credit scoring

The political desire for further integration within the European Union will have an increasing effect on the financial services industry, including banking and credit lending. Harmonisation can potentially have great benefit for consumers as the European banks compete for their business across the Union. Harmonisation, however, brings with it a set of decisions for both credit grantors and regulators. There are issues associated with information that can be used in risk assessment to ensure fair decisions in granting credit. In the past the rules have been developed under national legislation. EU directives seek to harmonise the national rules and provide a new guidance on variables that may be held. The major concern relates to the issue of discrimination. The aim of the law is to promote the principle of equal treatment, which can be interpreted in a number of ways. The legal interpretation of discrimination does not necessarily coincide with the economic standpoint. Furthermore, previous empirical research suggests that prohibition of variables may not only affect the ability to distinguish between good and bad risk, but may also be disadvantageous to the groups the legislation is supposed to protect. This paper explores these issues from both theoretical and practical points of view.     

Problems in applying discriminant analysis in credit scoring models

Since the mid-1960s financial institutions and other creditors with increasing frequency have applied credit scoring and related loan review procedures to appraise the creditworthiness of loan applicants. The passage of the Equal Credit Opportunity Act and promulgation of the Federal Reserve's Regulation B to implement this act place an important burden on institutions that are subject to the regulation and that employ screening models to ensure that their procedures are statistically and methodologically sound.This paper reviews the types of credit scoring models that have been described in various journals. It gives particular attention to the methodological approaches that have been employed and the statistical problems associated with those models using discriminant analysis techniques. The paper points out that the statistical scoring models discussed in the literature have focused primarily on the minimization of default rates, which is in fact only one dimension of the more general problem of granting credit. To the extent that for the lender profit maximization or cost minimization is, or should be, the objective of a scoring model, then most of the applied literature seems incomplete. The paper also shows that, even ignoring these shortcomings, the models used typically suffer from statistical deficiencies. And it finds that some of the problems of these models seem to be inherent in the discriminant analysis techniques employed or seem to be hard to remedy, given the state of the art concerning estimation and sampling procedures.     

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.     

小额信贷:信用卡的生存发展之本

2010年2月24日,中国人民银行副行长朱民在中科院虚拟经济与数据科学中心成果发布会上表示,全国个人通用信用评分系统的开发工作基本完成,年内有望正式上线.     

Correction to: Impact of anti-discrimination laws on credit scoring

Journal of Financial Services Marketing -     

个人通用信用评分系统：我国征信体系建设的新跨越

经过20余年的发展,我国信用经济已经进入快速发展时期,房贷、车贷、信用卡消费快速增长。如何更好地监管个人信用、更好地防范金融风险已经成为金融机构乃至商家关注的重点。现代市场经济是建立在法制基础上的信用经济,高度发达的社会信用体系在防范金融风险、提高市场资源配置效率等方面都发挥着积极作用,征信体系的建设则是整个社会信用体系建设的重要环节。     

Restructuring the credit process: behaviour scoring for german corporates

An instrument for automated monthly credit standing analysis based on data of the corporates current accounts is presented. Different methods of statistics and machine learning are used to develop scoring models for the supervision of debtors. The following methods were selected for model developement:

• Linear Discriminant Analysis
• Pattern Recognition (k‐nearest‐neighbours)
• Genetic Algorithms
• Neural Networks
• Decision Trees

The developed models were compared not only concerning their classification results but also concerning score distribution, transparency and IT‐realisation. Copyright © 2000 John Wiley & Sons, Ltd.