消费信贷风险管理[文献翻译].doc

Managing Consumer Credit Risk
Peter Burns Anne Stanley
September 2001
Abstract 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 Consulting’s 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 today's risk management professionals in underwriting consumer risk. This paper summarizes key elements of Bower's presentation.
Keywords: consumer credit, credit scoring, portfolio management
PORTFOLIO MANAGEMENT AND ANALYTICS
In Bower’s 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 maximize 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-making
credit-scoring
loss forecasting
portfolio 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 firm’s 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 analysis
pricing strategies
loan amount determination
credit 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 Bower’s 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。
回应分析
定价战略
贷款数量定性
行用卡遗失预测
投资管理战略
收集战略
通过这些相互关联的活动最有效的申请分析办法就是通过生意过程分析数据并且管理主要的信息宝库。
在处理回应分析报告时，冒险挑战就是避开不利的后果，这些后果就是可能会引来更多危险的剽窃者，预言经营技巧强调这个问题并且要求严密的回答去不停提升对消费行为的理解。通过使用一系列的输入顾客被认为是建立了一些标识。再一次，通过从1990年中期一系列发行者不愉快的经历来看，信用卡工厂在这个领域被相对延伸。在另一个市场，一些被挑选的小型贷方依然被引用作为成功范例应用到市场分析当中。
对众多的消费贷款人而言，对风险的定价策略仍然是一大挑战，因为这些人一直倾向于遵循竞争策略。此外，许多贷款人未能有效地测定定价模式，以探讨不同阶层通过权衡年费----及其他变数后所作的反应。企业领导者利用盈利能力和现金流模拟系统方式以便能深入了解某些投资领域及更好的管理因错误定价所造成的风险。确定适当的贷款额会直接影响到投资组合的损失，判断标准 或者更糟的影响到营销驱动战略，这样通常会导致更多的信贷风险。同样，如现金流模拟这样的分析技术与相对于风险分类的贷款金额相比较，能创建结果情况。统计方法的作用是对有决定作用的最佳分部所设定的贷款和线路进行更好的管理，以使损失减到最低和量化使用的概率。正如这几年，我们从一些消费贷款业务看到，过去一直使用的由信贷损失预测和设想来设定价格和贷款金额的方法已经被证明了它们无法做到精确有效。1990年中期，信用卡贷款机构发现对银行破产趋势的历史性预测是不能让人信服的。对此，业内大多数领导人已经大幅度增加他们在这领域的分析技术，以便更好的获取产品动态。那些为更好的估计个别组合同伙行为的策略如 滚转率模型，趋势及季节性索引，和复古曲线技术，是目前企业领导所产用的比较先进的方法。
项目组合管理是对于消费贷款者而言是一个关键问题，他们检查重新定价的做法和保留策略，处理信用额度管理协议。重新定价组合是以判断标准为基础的，例如，可导致收入减少或增加投资组合的风险。企业领导者把现金流量的盈利能力与建模行为元素相结合以更准确地确定价格调整的具体客户群的影响。
企业领导人明白，收集策略可以对减轻信贷损失产生重大影响。根据鲍尔斯的经验，收集有统计行为模型的，明显比没有建立行为模型的要好很多。他还指出，周密的计划是导致分割有针对性地收集战略，减少拖欠国家从一个滚率下。
总结
近年来，随着企业领导者和顾问开始重视日益复杂的分析工具的发展，对于改善消费信贷风险管理的工具正在显著提高。仓储技术的进步和整体计算机效率的日趋完善，大大促进上述这些的发展。同时，这些新方法的应用在企业和行业部门之间也相差甚远。一般来说，信用卡行业往往是发展道路中走的最远的，但即使是这样，也存在变数。一些贷款企业已经开发了高精分割组合设计和提出了加强风险为基础的记分卡策略，但只有少数人达到了充分的运用多变量回归分析的综合模型的水平。与此同时，鲍尔指出，在消费贷款业务中的风险管理实践要比90年代初强，并且企业对经济下滑的预测要比十年前定位的更准确。