文献出处：Kruppa J, Schwarz A, Arminger G Kruppa J, Schwarz A, Arminger G, et al. C Consumer credit risk asssment [J].
, et al. C Consumer credit risk asssment [J]. Expert Systems with Applications, 2014，40(13): 25-41
原文原文
Consumer credit risk asssment
Kruppa J, Schwarz A, Arminger
PORTFOLIO MANAGEMENT AND ANALYTICS
In Bower’s view, In Bower’s view, consumer credit risk must be understood in terms of a portfolio consumer credit risk must be understood in terms of a portfolio management strategy that balances capital prervation with capital optimization, that is, “ . . . a continuous process of identifying and capitalizing upon appropriate海狗图片
opportunities while avoiding inappropriate exposure in such a way as to maximize the value of enterpri.” Capturing data across all steps in the customer relationship and integrating information ma
nagement 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 parate 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 gments.
BEST PRACTICES IN CREDIT-RISK-MANAGEMENT
Credit-risk-management practices vary considerably among firms and between gments 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, the leade
rs are employing expert systems that can adopt to changes in the economy or within specific customer gments.
excel横排数据变竖排
The u of credit-scoring varies from tho that are using only credit bureau
大棚种西瓜技术data, to tho that blend bureau data with other information bad o data, to tho that blend bureau data with other information bad on the firm’s own n the firm’s own experience, to the most advanced applications using adaptive algorithms. The models, ud 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 gments.
Loss forecasting techniques have advanced considerably from their early reliance on historical delinquency rates and charge-off trend analys. Delinquency flow models and gmented vintage analys are now commonly ud to recognize
portfolio dynamics and behavior patterns bad on pools with common characteristics. The credit card industry has perhaps gone the furthest with its u of massive
gmentation profiles, with the more advanced issuers complementing the profiles with regional economic data and other analytical dynamics.
Over all portfolio management employs all of the techniques with most firms tracking current vs. historical performance and establishing concentration limits for particular risk gments. Some lenders employ risk adjusted return on capital (RAROC) models but Bower and his colleagues argue that multi-year net prent value cash flow 4 models reprent a more effective way to understand optimal
risk/reward relationships. In their experience, at this point, only a few firms appear to be testing the more advanced approaches.
Bower concluded this ction of the discussion by noting that “the future of consumer credit risk management lies in organizing portfolio performance and account level detail into databas; and then, applying refined analytical models to discern discern patterns or trends.” In doing so, lenders can more effectively manage loss patterns or trends.” In doing so, lenders can more effectively manage loss exposures and apply risk-bad credit pricing.
ANALYTICAL TECHNIQUES分数乘法应用题
Analytical techniques are especially applicable to consumer lending. Consumer portfolios, unlike tho in commercial lending, tend to be compod of many relatively homogeneous loans. The relatively common behavior characteristics of portfolio gments make statistical modeling techniques especially uful. As Bower noted, “Analytical techniques that forecast, gment, and classify individual loans into
homogeneous pools have provided the competitive advantage to leading-edge consumer lenders.”做好事
The discussion then turned to the application of risk management analytics in
dealing with the full spectrum of credit management activities:
keys to effective application of analytics across the often interrelated activities are collecting data throughout the business process and managing a common information repository, or risk data warehou.
In dealing with respon analysis, the risk management challenge is to avoid
adver lection conquences that result in incread concentrations of high-risk
三百六十行borrowers. Predictive modeling techniques address this issue but require rigorous
respon testing to continually improve understanding of customer behavior. Using a
range of inputs from origination files (demographics, transactional data, etc.),
customer characteristics are gmented and analyzed to develop identifiers of the
desired respon. 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 lect group of small-business lenders were also cited as having successfully applied the gmented respon 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 gments’ respons to the trade-offs among annual fees, APR introductory periods, and other pricing variables. Industry leaders u profitability and cash flow modeling to provide insights into portfolio gments and better manage mispriced risk gments.
Determining the appropriate loan amount directly affects portfolio loss. Judgmental criteria - or, wor, marketing-driven strategies - will generally lead to incread credit exposure. Again, analytical techniques such as cash flow modeling can create outcome scenarios comparing loan amounts relative to risk gments. Statistical methodologies exist that add better control over loan or line tting by determining optimal gments to minimize loss and quantifying probabilities of u.
As we have en in a number of consumer lending business over the years,
credit loss forecasts or assumptions ud to t 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
respon, 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 asonal 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 gments bad on judgmental criteria, for example, can lead to
lower revenues or incread 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 gments.
家常凉拌菜Industry leaders understand that collection strategies can have a significant
impact on lesning credit loss. In Bower’s experience, collection efforts that have 7 been augmented with statistical behavior models are demonstrably more effective than tho with no behavioral modeling support. He also noted that well-conceived gmentation 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 focud on the
development of increasingly sophisticated analytical tools. Advances in data warehousing technology and overall computational efficiencies have greatly facilitated the developments. At the same time, application of the new methodologies varies substantially among firms and between industry gments. 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 development path, but even
developed highly refined portfolio gmentation designs and enhanced risk-bad
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.
译文
消费信贷风险评估
克鲁帕；施瓦兹；阿米格
证券投资管理及分析
鲍尔斯认为，鲍尔斯认为，消费信贷风险，消费信贷风险，消费信贷风险，必须被认为是按照证券投资组合管理策略，必须被认为是按照证券投资组合管理策略，必须被认为是按照证券投资组合管理策略，以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，贷款人的范围，且信用卡公司通常是先锋。且信用卡公司通常是先锋。且信用卡公司通常是先锋。通常，通常，通常，这一工序是用于处理单独的旧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。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，毕马威会计师事务所顾问发现，毕马威会计师事务所顾问发现，在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。件来提供有效的证券投资管理。例如，例如，例如，应用程序数据丢失，应用程序数据丢失，
应用程序数据丢失，将意味着关键记分卡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。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：信贷决策信贷决策;；信用评分信用评分;；流失
>卫生局长
