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,     

公司信贷风险管理转型

<正>当前公司信贷风险转型面临的环境受中美贸易摩擦、新冠肺炎疫情等负面因素影响,经济增长仍面临较大压力:受中美贸易摩擦等因素影响,2019年全年GDP同比增长6.1%,较上年同期放缓0.5个百分点;2019年1～12月全国规模以上工业企业利润累计同比下降3.3%,规模以上工业企业利润累计增速为负,在过去十年中仅出现3次。与此同时,肇始于2019年12月底的新冠疫情,给我国经济带来较大负面冲击。多数机构预测我国2020年第一季度GDP增速将     

商业银行信贷风险管理数字化转型的探析

推动信贷风险管理全面数字化转型是有效解决商业银行传统信贷经营管理中的痛点和难点、增强风险防控能力的重要手段。本文从传统信贷风险管理存在的问题入手,结合当前实际,提出了商业银行信贷风险管理数字化转型的途径和促进数字化转型的若干建议。     

小企业信用评分信贷风险试金石

商业银行将信用评分技术引入小企业贷款业务,运用数据对小企业信用风险进行准确度量和把握,准确快速地识别“好客户”和“坏客户”。     

个人信用评分模型比较数据挖掘分析

为了有效控制和防范信贷风险,商业银行必须对借款人做出准确的信用评估。本文通过利用传统的Logistic回归与随机森林模型,分别建立信用评分模型,并比较两个模型的优缺点以达到最佳的预测效果,从而有效的降低商业银行的个人信用评估风险,更好地实现银行利润最大化。     

美国个人信用评分系统及其启示

以美国为代表的发达国家消费市场发展迅速,其中一个重要原因是已经建立起一套较为完整的个人信用体系,并运用了较成熟的评估方法。我国建立个人征信系统起步较晚,存在许多不足。本文通过介绍分析美国常用的FICO信用评分方法的内容、不同行业的信用评分模型及其调整过程,对美国先进的个人征信系统进行了分析研究,旨在为国内金融机构开展个人消费信用风险识别和管理提供有益的借鉴。     

个人信用评分关键技术研究的新进展

从系统论的角度总结了个人信用评分发展的前沿问题,从数据预处理、指标体系筛选、以及模型设计三个方面对个人信用评分关键技术的最新研究成果进行了细致分类和综合比较,从而指出个人信用评分研究中存在的难点以及未来发展方向。     

个人信用评分模型综述与应用分析

信用评分模型作为信用风险管理的基础和核心,无论是对于建立社会征信体系还是对于金融机构的信贷资产管理,都有着不可替代的作用。然而,各类信用评分模型在其适用范围、采用的技术手段以及有效性方面都有所差异,不恰当地运用信用评分模型往往事倍功半。本文在梳理分析各类信用评分模型的基础上,结合应用的现实情况,探讨信用评分模型具体操作时应注意的一些问题,以期业内人士更好地理解信用评分技术在信用风险决策中的作用。作为现代金融理论的三大支柱之一,风险管理一直是金融理论界和实务界所共同关注的重点。其中,信用风险的度量和管理在…     

个人信用评分系统在银行业的应用

个人信用评分系统是一套定量评估个人信用风险的应用系统,它通过对个人客户信息进行量化计算得出信用分值,反映个人客户的信用状况。个人信用评分系统广泛地应用于个人信贷、信用卡、保险理赔等金融业务中,为信用政策的制定、分析、评估、优化提供量化支持。     

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.     

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.     

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.     

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.     

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.     

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。     

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.     

信用评分模型开发中的抽样技术

信用评分是运用数据挖掘技术对已知客户的信息进行分析，建立能预测未来客户信用表现的模型。数据准备是评分模型开发过程中非常重要的步骤，数据质量的好坏直接决定了模型的成败。由于银行内部的数据量非常庞大，为了使分析更加有效率，需要对数据进行抽样。因此，如何进行抽样，如何保证样本能够充分代表总体就非常重要。根据信用评分模型的开发经验以及数据挖掘中的抽样理论，现提出如下建立评分模型时应用的抽样技术以及注意事项。