《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。     

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.     

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.     

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.     

The use of credit scoring in the mortgage industry

In times of increased focus on risk management, acquiring or growing comparatively low risk mortgage portfolios has become an attractive value proposition. Banks that pursue an aggressive growth strategy in this sector, do, however, require risk control mechanisms that enable them to make a clear judgment on how great a growth appetite they can afford to have in order to still grow profitably. Moreover, under Basel II, the proper quantification of mortgage portfolio risk tends to help the release of own capital, because the mortgage portfolio is one of those portfolios where the relative benefits of internal ratings-based approaches compared with the standardised approach are greatest. Credit scoring models in general, and credit scorecards in particular, are suitable methods for quantifying the risk of an individual mortgage applicant or mortgage customer. In addition to score card development, this paper reviews alternative scoring model types that could be used for mortgage scoring. It presents reasons why it is beneficial to build such models in-house, before focusing on the steps necessary for building a mortgage scorecard. Finally, it discusses the important topics of creating segments, deploying models and eventually monitoring models.     

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.     

Interpretable credit model development via artificial neural networks

Poor credit granting decisions are coming back to haunt providers of loan finance. Past poor credit granting decisions are in part due to the Equal Credit Opportunity Act (1975). This act requires lenders to explain the decision to grant or refuse credit. As a result, models such as artificial neural networks, which offer improved ability to identify poor credit risks but which do not offer easy explanations of why a loan applicant has scored badly, remain unused. This paper investigates whether these models can be interpreted so that explanations for credit application rejection can be provided. The results indicate that while the artificial neural networks can be used (with caution) to develop credit scoring models, the limitations imposed by the credit granting process make their use unlikely until interpretation techniques are developed that are more robust and that can interpret multiple hidden-layer artificial neural networks. Copyright © 2008 John Wiley & Sons, Ltd.     

The impact of credit scoring on consumer lending

We study the adoption of automated credit scoring at a large auto finance company and the changes it enabled in lending practices. Credit scoring appears to have increased profits by roughly a thousand dollars per loan. We identify two distinct benefits of risk classification: the ability to screen high‐risk borrowers and the ability to target more generous loans to lower‐risk borrowers. We show that these had effects of similar magnitude. We also document that credit scoring compressed profitability across dealerships, and provide evidence consistent with the view that credit scoring may have substituted for varying qualities of local information.