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 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.     

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.     

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.     

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.     

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.     

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。     

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。     

A two-stage credit scoring model based on random forest: Evidence from Chinese small firms

Small firms are major contributors to most economies, often supported by government policies. However, the credit scoring of small firms is complicated and costly, making it a challenging field of research. Using loan data from 3045 small firms in China, we design a two-stage expert system for default prediction that quantifies the variables and thresholds that have a key impact. Firstly, we use SMOTE to deal with the imbalanced data and secondly, we employ random forest to build predictive credit features. Dominance analysis shows that, when making default assessments on Chinese small firms, it is important to consider not only financial factors, but also non-financial and macroeconomic factors. In particular, the net cash profit, the firm's legal disputes and the per capita disposable income of urban residents are key factors in credit scoring. Robustness tests show that our proposed methodology performs better than other machine learning models, and this result is robust with observations from other countries.     

游离于法律边缘的信用卡催收外包业务

信用卡是银行定位于个人的消费信贷工具.与银行其他信贷产品相比,信用卡具有其显著的特点: (1) 没有任何形式的担保和财产抵押,完全建立在个人信用基础上;     

中国信用卡法律法规沿革与法律体系

截至2008年6月,我国银行卡发卡量超过16亿张,其中信用卡已达到1.18亿张,全国银行传受理商户超过88万家;2008年1～5月,全国刷卡消费共约1.21万亿元人民币,占同期全国社会消费品零售总额的28%.信用卡业务已深深融入人们的日常生活之中.     

Basel III capital buffers and Canadian credit unions lending: Impact of the credit cycle and the business cycle

We take advantage of the long-standing regulation of the risk-based capital and the leverage ratio in Canada to provide empirical evidence on the relation between the credit unions' capital buffers and loans to members. Based on a unique sample of the 100 Canadian largest credit unions from 1996 to 2014, we find that both the risk-based capital buffer and the leverage buffer are positively related to changes in loans and loan growth. However, changes in these two types of buffers are negatively related to changes in the loans to assets ratios. This finding suggests that to adjust their capital buffers, Canadian credit unions curtail their loans and underscores the importance of the Basel III conservation and the countercyclical buffer requirements in fostering credit. Further, we show that the risk-based capital buffer is positively related to the credit cycle. However, a mechanical application of the rule based on the credit-to-gross domestic product (GDP) gap to activate the countercyclical buffer, would have misguided Canadian credit unions.     

我国小企业信用评分系统的开发策略

为了缓解借贷双方信息不对称问题,防范和化解借款人较高的逆向选择和道德风险,促进其盈利水平和核心竞争能力的提高,商业银行将信用评分技术引入小企业货款业务中,使用数据对小企业信用风险进行准确度量,准确快速地识别"好客户"和"坏客户".目前小企业信用评分模型已广泛应用干欧美等发达国家中小企业授信业务上,并在大量授信实践中丰富了信用评分的内容,使该项技术日趋成熟.     

中国信用卡法律法规沿革与法律体系(续)

二、我国的信用卡法律体系及其基本问题探究 (一)我国信用卡法律体系概述 根据(<法学辞典>的定义,法律体系是指"由各法律部门组成的一国法律有机联系的整体",它只包括现行有效的法律规范.基于此,我国的信用卡法律体系也就是指所有现行有效的涉及信用卡管理的相关法律规范组成的一个有机整体.作为制度保障,它们共同推进着信用卡业务的发展.     

Impact of the Clinton credit availability program on commercial banks (policy paper)

This article investigates how President Clinton's program for increasing credit availability to small and medium-sized businesses affected bank stocks. The pattern of results observed is consistent with the hypothesis that the program promised to redistribute wealth through the federal safety net and not just to eliminate burdensome regulations.Our tests establish that potentially too-big-to-fail money-center banks benefited more than regional banks, and low-quality banks benefited more than high-quality banks. Systematic risk for money-center banks increased with the program announcement, while risk of regional banks changed insignificantly. Risk differences correlate weakly with bank quality.Trading-volume tests suggest that investors agreed about program implications, except for different degrees of skepticism about the ability of weak banks to horn in on program benefits. Except for banks whose quality fell into the lowest ranking, trading volume was not significantly disturbed.     

Impact of multiple curve dynamics in credit valuation adjustments under collateralization

We present a detailed analysis of interest rate derivatives valuation under credit risk and collateral modeling. We show how the credit and collateral extended valuation framework presented in Pallavicini et al. [Funding valuation adjustment: FVA consistent with CVA, DVA, WWR, collateral, netting and re-hyphotecation, 2011], and the related collateralized valuation measure, can be helpful in defining the key market rates underlying the multiple interest rate curves that characterize current interest rate markets. A key point is that spot Libor rates are to be treated as market primitives rather than being defined by no-arbitrage relationships. We formulate a consistent realistic dynamics for the different rates emerging from our analysis and compare the resulting model performances to simpler models used in the industry. We include the often neglected margin period of risk, showing how this feature may increase the impact of different rates dynamics on valuation. We point out limitations of multiple curve models with deterministic basis considering valuation of particularly sensitive products such as basis swaps. We stress that a proper wrong way risk analysis for such products requires a model with a stochastic basis and we show numerical results confirming this fact.     

Correction to: Earnings beta

Review of Accounting Studies - A Correction to this paper has been published: https://doi.org/10.1007/s11142-021-09585-w