消费信贷风险管理外文翻译.doc

消费信贷风险管理外文翻译 Managing Consumer Credit Risk Peter Burns Anne Stanley September 2001Abstract On July 31, 2001, the Payment Cards Center of the Federal Reserve Bank of Philadelphia hosted a Work shop that examined current credit risk management practices in the consumer credit industry. The session was led by Jeffrey Bower, senior manager in KPMG Consultings financial services practice. Bower discussed best practices in the credit risk management field, including credit scoring, loss forecasting, and portfolio management. In addition, he provided an overview of developing new methodologies used by todays risk management professionals in underwriting consumer risk. This paper summarizes key elements of Bowers presentation.Keywords: consumer credit, credit scoring, portfolio managementPORTFOLIO MANAGEMENT AND ANALYTICS In Bowers view, consumer credit risk must be understood in terms of a portfolio management strategy that balances capital preservation with capital optimization, that is, “ a continuous process of identifying and capitalizing upon appropriate opportunities while avoiding inappropriate exposure in such a way as to imize the value of enterprise.” Capturing data across all steps in the customer relationship and integrating information management are the keys to effective portfolio management. While this is a fairly straightforward prescription, executing it is often beyond the scope of many lenders, with the credit card companies generally in the vanguard. Often, the process steps are managed on separate legacy systems, which complicate efforts to integrate information. KPMG consultants find that many firms typically purge specific files before the information is extracted and combined with other data to provide effective portfolio management. The loss of application data, for example, would mean that critical score-card and demographic information would not be available to model behavior in defined customer or risk segments.BEST PRACTICES IN CREDIT-RISK-MANAGEMENT Credit-risk-management practices vary considerably among firms and between segments of the consumer lending industry. To illustrate the variability, Bower described the range of management practices in:Credit decision-makingcredit-scoringloss forecastingportfolio management. On the front end of the credit process, industry leaders are investing in analytics to improve the credit decision-making process. Building on experience with credits coring technologies, these leaders are employing expert systems that can adopt to changes in the economy or within specific customer segments. The use of credit-scoring varies from those that are using only credit bureau data, to those that blend bureau data with other information based on the firms own experience, to the most advanced applications using adaptive algorithms. These models, used by some of the leading credit card issuers, are updated regularly to reflect changing characteristics of the applicant population. A significant challenge for even the most sophisticated lenders is how to model probable performance when dealing with new customer segments Loss forecasting techniques have advanced considerably from their early reliance on historical delinquency rates and charge-off trend analyses. Delinquency flow models and segmented vintage analyses are now commonly used to recognize portfolio dynamics and behavior patterns based on pools with common characteristics. The credit card industry has perhaps gone the furthest with its use of massive segmentation profiles, with the more advanced issuers complementing these profiles with regional economic data and other analytical dynamics Over all portfolio management employs all of these techniques with most firms tracking current vs. historical performance and establishing concentration limits for particular risk segments. Some lenders employ risk adjusted return on capital RAROC models but Bower and his colleagues argue that multi-year net present value cash flow 4 models represent a more effective way to understand optimal risk/reward relationships. In their experience, at this point, only a few firms appear to be testing these more advanced approaches. Bower concluded this section of the discussion by noting that “the future of consumer credit risk management lies in organizing portfolio performance and account level detail into databases; and then, applying refined analytical models to discern patterns or trends.” In doing so, lenders can more effectively manage loss exposures and apply risk-based credit pricing.ANALYTICAL TECHNIQUES Analytical techniques are especially applicable to consumer lending. Consumer portfolios, unlike those in commercial lending, tend to be composed of many relatively homogeneous loans. The relatively common behavior characteristics of portfolio segments make statistical modeling techniques especially useful. As Bower noted, “Analytical techniques that forecast, segment, and classify individual loans into homogeneous pools have provided the competitive advantage to leading-edge consumer lenders.” The discussion then turned to the application of risk management analytics in dealing with the full spectrum of credit management activities:response analysispricing strategiesloan amount determinationcredit loss forecasting portfolio management strategies collection strategies The keys to effective application of analytics across these often interrelated activities are collecting data throughout the business process and managing a common information repository, or risk data warehouse. In dealing with response analysis, the risk management challenge is to avoid adverse selection consequences that result in increased concentrations of high-risk borrowers. Predictive modeling techniques address this issue but require rigorous response testing to continually improve understanding of customer behavior. Using a range of inputs from origination files demographics, transactional data, etc., customer characteristics are segmented and analyzed to develop identifiers of the desired response. Again, the credit card industry is relatively further along in this area, having learned from the painful experiences of a number of issuers in the mid-1990s. In another market, a select group of small-business lenders were also cited as having successfully applied these segmented response analytics to their marketplace Pricing strategies for risk remains a challenge for many consumer lenders who tend to “follow the competition.” Furthermore, many lenders fail to effectively test pricing models to explore different segments responses to the trade-offs among annual fees, APR introductory periods, and other pricing variables. Industry leaders use profitability and cash flow modeling to provide insights into portfolio segments and better manage mispriced risk segments. Determining the appropriate loan amount directly affects portfolio loss. Judgmental criteria - or, worse, marketing-driven strategies - will generally lead to increased credit exposure. Again, analytical techniques such as cash flow modeling can create outcome scenarios comparing loan amounts relative to risk segments. Statistical methodologies exist that add better control over loan or line setting by determining optimal segments to minimize losses and quantifying probabilities of use. As we have seen in a number of consumer lending businesses over the years, credit loss forecasts or assumptions used to set pricing and loan amount may well prove inaccurate with the passage of time. Credit card lenders found that historical assumptions for bankruptcy trends proved inadequate during the mid-1990s. In response, most of the industry leaders have greatly enhanced their analytical techniques in this area to better capture portfolio dynamics. Decompositional roll rate modeling, trend and seasonal indexing, and vintage curve techniques to better estimate behavior within individual portfolio cohorts are some of the advanced statistical methodologies currently employed among industry leaders. Portfolio management is a key issue for consumer lenders as they examine repricing practices and retention strategies and deal with credit line management. Repricing portfolio segments based on judgmental criteria, for example, can lead to lower revenues or increased portfolio risk. Industry leaders are integrating behavioral elements with cash flow profitability modeling to more accurately determine the impact of pricing adjustments on specific customer segments Industry leaders understand that collection strategies can have a significant impact on lessening credit losses. In Bowers experience, collection efforts that have 7 been augmented with statistical behavior models are demonstrably more effective than those with no behavioral modeling support. He also noted that well-conceived segmentation schemes are leading to targeted collection strategies, decreasing roll rates from one state of delinquency to the next.SUMMARY The tools for improving management of consumer credit risk have advanced considerably in recent years as industry leaders and their advisors have focused on the development of increasingly sophisticated analytical tools. Advances in data warehousing technology and overall computational efficiencies have greatly facilitated these developments. At the same time, application of these new methodologies varies substantially among firms and between industry segments. Generally speaking, the credit card industry tends to be the furthest along the development path, but even ? here, variability exists. A number of lending firms have developed highly refined portfolio segmentation designs and enhanced risk-based score-card schemes, but only a few have reached the level of fully integrated models that employ multi-variable regression analysis. At the same time, Bower concluded by noting that risk management practices in the consumer lending business are generally much stronger than in the early 1990s and the industry is far better positioned to weather the current economic downturn than it was a decade ago.外文题目:Managing Consumer Credit Risk出处: Ten Independence Mall,Philadelphia,PA19106.1570作 者:Peter Burns and Anne Stanley译文: 消费信贷风险管理 彼得伯恩斯和安妮士丹利摘要2001年7月31日,支付卡中心的费城联邦储备银行主办了一个研讨会,调查在消费信贷行业目前的信贷风险管理做法。这次会议分别由杰弗里鲍尔,毕马威服务业务高级经理毕马威进行财务咨询。鲍尔讨论了信贷风险管理领域的“最佳做法”,包括信用评分,损失预测和投资组合管理。此外,他还提供了一份被当今从事保险业的消费风险管理专业人员所发展起来的新论。本文总结了鲍尔陈述的主要内容。关键词:消费信贷,信用评分,投资组合管理证券投资管理及分析 鲍尔斯认为,消费信贷风险,必须被认为是按照证券投资组合管理策略,以资本优化来平衡资本的保护,即“一个基于适当的机会以识别和积累资本的持续的过程,而避免为了使企业价值最大化而用这样一种方法进行不恰当的泄露。”通在顾客关系之间的所有步骤来收集数据和整合信息管理是进行有效的证券投资管理的关键。虽然这是一个相当简单的方法,但是在执行它时往往超出了许多贷款人的范围,且信用卡公司通常是先锋。通常,这一工序是用于处理单独的旧系统,这种旧系统使信息的整合更加复杂化。毕马威会计师事务所顾问发现,在信息被提取和与其他数据相结合之前,许多公司会有代表性的清除一些特定的文件来提供有效的证券投资管理。例如,应用程序数据丢失,将意味着关键记分卡和人口资料不会被提供给客户或风险细分定义模型。在信用风险管理的最佳做法 信用风险管理的做法在企业和消费贷款之间的细分行业有很大的不同。为了举例说明这一可变性,鲍尔描述了一系列的管理措施:信贷决策信用评分流失预测证券投资管理 在信用卡发展的过程当中,工业领导为了促进制作信用卡过程的进程,在分析学这一块上加以投资,基于对信用卡芯体的经验,这些领导雇佣专家团可以根据经济以及顾客要求采取一些变化。信用卡的使用从信用卡从单使用信用数据到基于自己结实经验基础之上,混合其他一些信息,达到超前的应用适应。一些率先使用信用卡的使用者利用这些模型,去有规律的展现流动的求职人口。对于一些老练的领导者而言,最重要的挑战就是在处理新的顾客部分的时候怎么样去带头做好工作。 许多预测技术根据他们对历史犯罪技术以及冲销技术的依赖已经有很大发展。犯罪流动模型和部分优质分析通常被用来识别投资力度和行为方式。随着更多先进发行者利用宗教经济数据和其他分析编辑补充先行剖面图,信用卡公司可能拓展大量的分割剖面图的使用。通过对所有投资管理的雇员来看,所有这些能够跟踪现代和历史执行,并且为主要危险部分建立。一些贷方雇佣RAROC模型,但是鲍尔和他的同事却对现金价值流动能够取代更有效的方式去理解报酬关系产生争论。在他们的经验里,只有一小部分的调制器可以检测这些更先进的方法。 鲍尔没解释什么,得出了这个方法,那就是消费者以后的信用危机管理存在于组织投资方面,在那之后,应用经过改良分析的模型区察觉样式或者是一种趋势。只有这样做了,贷方才可以能有效率的揭发遗漏并且应用到信用价格上面来。分析经营技巧 分析技巧主要适用于顾客贷款。消费者投资,不像一般的商业贷款,他们趋向于相对同类的贷款,比较普遍的投资行为使统计技术模型更有效。就像鲍尔说的:“分析技术是个预测,并且能够把技术提供把个人贷款分类到同类当中,并且能够为了消费者领先地位提供竞争优势。讨论最后变成了对信用卡范围的分析管理应用。回应