Scoring and Modeling—— Underwriting and Loan Approval Process

https://www.fdic.gov/regulations/examinations/credit_card/ch8.html

Types of Scoring

FICO Scores
    VantageScore
    Other Scores
              Application Scoring
              Attrition Scoring
              Bankruptcy Scoring
              Behavior Scoring
              Collection Scoring
              Fraud Detection Scoring
              Payment Projection Scoring
              Recovery Scoring
              Response Scoring
              Revenue Scoring

FICO分数
    VantageScore
     其他分数
               应用程序评分
               磨损评分
               破产评分
               行为评分
               收集评分
               欺诈检测评分
               付款投影得分
               恢复评分
               反应评分
               收入评分

Dual-Scoring Matrix

Credit Scoring Model Development

Basel Considerations Regarding Credit Scoring

Validation

Cut-off Score

Validation Charts and Calibration

Overrides

Credit Scoring Model Limitations

Automated Valuation Models

Summary of Examination Goals – Scoring and Modeling

双重打分矩阵

信用评分模型开发

关于信用评分的巴塞尔考虑

验证

截止分数

验证图表和校准

覆盖

信用评分模型限制

自动评估模型

考试目标总结 - 评分和建模

VIII. Scoring and Modeling

Scoring and modeling, whether internally or externally developed, are used extensively in credit card lending. Scoring models summarize available, relevant information about consumers and reduce the information into a set of ordered categories (scores) that foretell an outcome. A consumer's score is a numerical snapshot of his or her estimated risk profile at that point in time. Scoring models can offer a fast, cost-efficient, and objective way to make sound lending decisions based on bank and/or industry experience. But, as with any modeling approach, scores are simplifications of complex real-world phenomena and, at best, only approximate risk.

计分和建模，无论是内部还是外部开发，都广泛用于信用卡借贷。评分模型总结了关于消费者的可用相关信息，并将信息缩减为预测结果的一组有序类别（分数）。消费者的分数是他或她在那个时间点估计的风险状况的数字快照。评分模型可以提供快速，经济高效且客观的方式，以银行和/或行业经验为基础作出合理的贷款决策。但是，与任何建模方法一样，分数是对复杂的现实世界现象的简化，最多只是大致的风险。

评分模型用于多种目的，包括但不限于：

Scoring models are used for many purposes, including, but not limited to:

Controlling risk selection.
Translating the risk of default（ fail to pay） into appropriate pricing.
Managing credit losses.
Evaluating new loan programs.
Reducing loan approval processing time.
Ensuring that existing credit criteria are sound and consistently applied.
Increasing profitability.
Improving targeting for treatments, such as account management treatments.
Assessing the underlying risk of loans which may encourage the credit card backed securities market by equipping investors with objective measurements for analyzing the credit card loan pools.
Refining solicitation targeting to minimize acquisition costs.

评分模型用于多种目的，包括但不限于：

控制风险选择。
将违约风险转化为适当的定价。
管理信贷损失。
评估新的贷款计划。
减少贷款审批处理时间。
确保现有的信用标准健全且始终如一。
提高盈利能力。
改进治疗目标，如账户管理治疗。
通过为投资者提供分析信用卡贷款池的客观指标来评估可能鼓励信用卡支持证券市场的贷款潜在风险。
细化招标，降低采购成本。

Credit scoring models (also termed scorecards in the industry) are primarily used to inform management for decision making and to provide predictive information on the potential for delinquency or default that may be used in the loan approval process and risk pricing. Further, credit risk models often use segment definitions created around credit scores because scores provide information that can be vital in deploying the most effective risk management strategies and in determining credit card loss allowances. Erroneous, misused, misunderstood, or poorly developed and managed scoring models may lead to lost revenues through poor customer selection (credit risk) or collections management. Therefore, an examiner's assessment of credit risk and credit risk management usually requires a thorough evaluation of the use and reliability of the models. The management component rating may also be influenced if governance procedures, especially over critical models, are weak. Regulatory reviews usually focus on the core components of the bank's governance practices by evaluating model oversight, examining model controls, and reviewing model validation. They also consider findings of the bank's audit program relative to these areas. For purposes of this chapter, the main focus will be scoring and scoring models. A brief discussion on validating automated valuation models (AVM) is included in the Validation section of this chapter, and loss models are discussed in the Allowances for Loan Losses chapter. Valuation modeling for residual interests is addressed in the Risk Management Credit Card Securitization Manual.

信用评分模型（业内称为记分卡）主要用于向管理层通报决策，并提供关于贷款审批流程和风险定价可能使用的拖欠或拖欠风险的预测信息。此外，信用风险模型通常使用围绕信用评分创建的细分定义，因为评分提供的信息对于部署最有效的风险管理策略和确定信用卡损失限额至关重要。错误的，误用的，被误解的或者开发不良和管理得当的模型可能会导致客户选择（信用风险）或收款管理不善而导致收入损失。因此，审查员对信用风险和信用风险管理的评估通常需要对模型的使用和可靠性进行全面评估。如果管理程序，尤其是关键模型的管理程序薄弱，管理组件的评级也可能受到影响。监管审查通常关注银行治理实践的核心组成部分，通过评估模型监督，检查模型控制和审查模型验证。他们还考虑了银行与这些领域有关的审计计划的结果。为了本章的目的，主要重点将是评分和评分模型。关于验证自动评估模型（AVM）的简要讨论包含在本章的验证部分中，损失模型在贷款损失准备章节中讨论。 “风险管理信用卡证券化手册”介绍了剩余利益的估值模型。

Scoring models are developed by analyzing statistics and picking out cardholders' characteristics thought to be associated with creditworthiness. There are many different ways to compress the data into scores, and there are several different outcomes that can be modeled. As such, scoring models have a wide range of sophistication, from very simple models with only a few data inputs that predict a single outcome to very complex models that have several data inputs and that predict several outcomes. Each bank may use one or more generic, semi-custom, or custom models, any of which may be developed by a scoring company or by internal staff. They may also use different scoring models for different types of credit. Each bank weighs scores differently in lending processes, selects when and where to inject the scores into the processes, and sets cut-off scores consistent with the bank's risk appetite. Use of scoring models provides for streamlining but does not permit banks to improperly reduce documentation required for loans or to skip basic lending tenants such as collateral appraisals or valuations.

评分模型是通过分析统计数据并挑选出与信誉相关的持卡人特征来开发的。有许多不同的方法可将数据压缩为分数，并且可以建模几种不同的结果。因此，评分模型具有广泛的复杂性，从非常简单的模型，只有一些预测单一结果的数据输入到具有多个数据输入并预测多个结果的非常复杂的模型。每家银行可能会使用一种或多种通用，半定制或定制模式，其中任何一种都可能由评分公司或内部员工开发。他们也可能针对不同类型的信用使用不同的评分模型。每家银行在贷款流程中对分数进行不同的评分，选择何时何地将分数注入流程，并将截止分数设置为与银行的风险偏好一致。使用评分模型可以简化流程，但不允许银行不正确地减少贷款所需文件或跳过基本贷款租户，如抵押品评估或估值。

Practices regarding scoring and modeling not only pose consumer lending compliance risks but also pose safety and soundness risks. A prominent risk is the potential for model output (in this case scores) to incorrectly inform management in the decision-making process. If problematic scoring or score modeling cause management to make inappropriate lending decisions, the bank could fall prey to increased credit risk, weakened profitability, liquidity strains, and so forth. For example, a model could wrongly suggest that applicants with a score of XYZ meet the bank's risk criteria and the bank would then make loans to such applicants. If the model is wrong and scores of XYZ are of much higher risk than estimated, the bank could be left holding a sizable portfolio of accounts that carry much higher credit risk than anticipated. If delinquencies and losses are higher than modeling suggests, the bank's earnings, liquidity, and capital protection could be adversely impacted. Or, if such accounts are part of a securitization, performance of the securitization could be at risk and could put the bank's liquidity position at risk, for instance, if cash must be trapped or if the securitization goes into early amortization. A poorly performing securitization would also impact the fair value of the residual interests retained.

有关评分和建模的做法不仅会引起消费者贷款合规风险，还会带来安全和健全风险。模型输出的潜力（在这种情况下分数）在决策过程中错误地告知管理层。如果有问题的评分或评分模型导致管理层做出不适当的贷款决策，银行可能会因为信用风险增加，盈利能力减弱，流动性紧张等原因而成为受害者。例如，某个模型可能会错误地暗示XYZ分数符合银行风险标准的申请人，银行会向这些申请人提供贷款。如果模型是错误的，XYZ的分数比估计的风险要高得多，那么银行可能会留下大量的账户，这些账户的信用风险高于预期。如果拖欠和亏损高于模型建议，银行的收益，流动性和资本保护可能受到不利影响。或者，如果这些账户是证券化的一部分，那么证券化的业绩可能面临风险，并可能使银行的流动性状况面临风险，例如，如果现金必须被困住或者证券化进入提前摊销。表现不佳的证券化也会影响保留的剩余利益的公允价值。

Well-run operations that use scoring models have clearly-defined strategies for use of the models. Since scoring models can have significant impacts on all ranges of a credit card account's life, from marketing to closure, charge-off, and recovery, scoring models are to be developed, implemented, tested, and maintained with extreme care. Examiners should expect management to carefully evaluate new models internally developed as well as models newly purchased from vendors. They should also determine whether management validates models periodically, including comparing actual performance to expected performance. Examiners should expect management to:

使用评分模型的运行良好的操作有明确定义的模型使用策略。由于评分模型可能对信用卡账户的所有生命周期产生重大影响，从营销到关闭，关闭和恢复，评分模型将得到非常小心的开发，实施，测试和维护。审查员应期望管理层认真评估内部开发的新型号以及从供应商处新购买的型号。他们还应确定管理层是否定期验证模型，包括比较实际绩效和预期绩效。考官应该期望管理层：

Understand the credit scoring models thoroughly.
Ensure each model is only used for its intended purpose, or if adapted to other purposes, appropriately test and validate it for those purposes.
Validate each model's performance regularly.
Review tracking reports, including the performance of overrides.
Take appropriate action when a model's performance deteriorates.
Ensure each model's compliance with consumer lending laws as well as other regulations and guidance.

底了解信用评分模型。
确保每个模型仅用于其预期用途，或者如果适用于其他用途，则为了这些目的进行适当的测试和验证。
定期验证每个模型的性能。
检查跟踪报告，包括覆盖的性能。
模型性能下降时采取适当的措施。
确保每个模型符合消费者贷款法律以及其他法规和指导。

Most likely, scoring and modeling will increasingly guide risk management, capital allocation, credit risk, and profitability analysis. The increasing impetus on scoring and modeling to be embedded in management's lending decisions and risk management processes accentuates the importance of understanding scoring model concepts and underlying risks.

最有可能的是，评分和建模将越来越多地指导风险管理，资本分配，信用风险和盈利能力分析。将评分和建模纳入管理层的贷款决策和风险管理流程的推动力日增，这突出了理解评分模型概念和潜在风险的重要性。

Types of Scoring
Some banks use more than one type of score. This section explores
scores commonly used. While most scores and models are generally
established as distinct devices, a movement to integrate models and
scores across an account's life cycle has become evident.

一些银行使用不止一种类型的分数。本节将探讨常用的分数。尽管大多数乐谱和模型通常都是作为不同设备建立的，但整合帐户生命周期中模型和乐谱的运动变得明显起来。

FICO Scores
Credit bureaus offer several different types of scores. Credit bureau scores are typically used for purposes which include:

Screening pre-approved solicitations.
Determining whether to acquire entire portfolios or segments thereof.
Establishing cross-sales of other products.
Making credit approval decisions.
Assigning credit limits and risk-based pricing.
Guiding account management functions such as line increases, authorizations, renewals, and collections.

FICO分数
信用局提供几种不同类型的分数。信用局分数通常用于以下目的：

筛选预先批准的招标。
确定是否收购整个投资组合或其分部。
建立其他产品的交叉销售。
做出信贷审批决定。
指定信用额度和基于风险的定价。
指导账户管理功能，如增加行，授权，续订和收款。

The most commonly known and used credit bureau scores are called
FICO scores. FICO scores stem from modeling pioneered by Fair, Isaac and
Company (now known as Fair Isaac Corporation) (Fair Isaac), hence the
label "FICO" score. Fair Isaac devised mathematical modeling to predict
the credit risk of consumers based on information in the consumer's
credit report. There are three main credit bureaus in the United States
that house consumers' credit data: Equifax, TransUnion, and Experian.
The credit-reporting system is voluntary, and lenders usually update
consumers' credit reports monthly with data such as, but not limited to,
types of credit used, outstanding balances, and payment histories. A
consumer's bureau score can be significantly impacted by a bank's
reporting practices. For instance, some banks have not reported certain
information to the bureaus. If credit limits are not reported, the score
model might use the high balance (the reported highest balance ever
drawn on the account) in place of the absent credit limit, potentially
inflating the utilization ratio and lowering the credit score. Errors
in, or incompleteness of, consumer-provided or pubic record information
in credit reports can also impact scoring. Consumer-supplied information
comes mainly from credit applications, and items of public record
include items such as bankruptcies, court judgments, and liens.

最常用的信用局分数称为FICO分数。 FICO评分来自Fair，Isaac and Company（现在称为Fair Isaac Corporation）（Fair Isaac）率先推出的模型，因此标签“FICO”得分。 Fair Isaac设计了数学模型，根据消费者信用报告中的信息预测消费者的信用风险。美国有三家主要的信用局存储消费者的信用数据：Equifax，TransUnion和Experian。信用报告系统是自愿的，贷款人通常每月更新消费者的信用报告，数据包括但不限于信用类型，未付余额和付款历史。银行的报告实践可能会严重影响消费者局的分数。例如，一些银行并未向局方报告某些信息。如果没有报告信用额度，评分模型可能会使用高余额（报告中记录的最高余额）来代替信用限额缺失，从而可能会提高利用率并降低信用评分。信用报告中消费者提供或公共记录信息的错误或不完整也会影响评分。消费者提供的信息主要来自信用申请，公共记录项目包括诸如破产，法院判决和留置权等项目。

Each bureau generates its own scores by running the consumer's
file through the modeling process. Although banks might not use all
three bureaus equally, the scoring models are designed to be consistent
across the bureaus (even though developed separately). Thus, an
applicant should receive the same or a similar score from each bureau.
In reality, variations (usually minor) arise due to differences in the
way the bureaus collect credit information (for example, differences in
the date of data collection) or due to discrepancies among information
the bureaus, which could include inaccurate information. FICO scores
rank-order consumers by the likelihood that they will become seriously
delinquent in the 24 months following scoring. FICO scores of 660 or
below may be considered illustrative of subprime lending (as set forth
in the January 2001 Expanded Guidance for Subprime Lending), although other characteristics are normally considered in subprime lending determinations as well.

每个局都通过建模过程运行消费者文件来生成自己的分数。虽然银行可能不会同时使用所有三个局，但评分模型在各局的设计是一致的（即使单独开发）。因此，申请人应该从每个局获得相同或相似的分数。实际上，由于各局收集信用信息的方式不同（例如数据收集日期的不同），或者由于各局之间的信息差异（可能包含不准确的信息）而产生变化（通常较小）。 FICO在评分后的24个月内可能会严重拖欠客户，从而对消费者进行排名。 FICO评分660或以下可能被视为次级贷款的说明（如2001年1月次级贷款扩展指导中所述），但其他特征通常也在次级贷款决定中考虑。

Benefits of credit bureau scoring include that it is readily available, is relatively easy to implement, can be less expensive compared to internal models, and is usually accompanied by various bureau-provided resources. Disadvantages include that scoring details are, for the most part, confidential and that it is available to every lender (no competitive differentiation).

信用局评分的好处包括它容易获得，相对容易实施，与内部模型相比可以更便宜，并且通常伴随着各局提供的资源。缺点包括评分细节绝大多数都是保密的，并且它对每个贷款人都是可用的（没有竞争差异）。

As is the case for any type of scores generated by models, FICO scores are inherently imperfect. Nevertheless, they usually maintain effective rank ordering and can be useful tools, particularly when resource or volume limitations preclude the development of a custom score. Several types of FICO scores are in use including Classic FICO, NextGen FICO Risk, FICO Expansion, and FICO Industry Options. Collectively, the scores are called FICO scores in this manual.

There are three different Classic FICO scores, one at each of the bureaus. According to www.fairisaac.com, they are branded as Beacon scores at Equifax; FICO Risk or Classic (formerly known as EMPIRICA) scores at TransUnion; and Experian/Fair Isaac Risk Model scores at Experian. Scores range from 300 to 850, with higher scores reflecting lower credit risk.

就像模型产生的任何类型的分数一样，FICO分数本质上是不完美的。尽管如此，他们通常会保持有效的排名顺序，并且可以成为有用的工具，特别是在资源或数量限制排除开发自定义分数的情况下。 FICO得分有几种类型，包括Classic FICO，NextGen FICO风险，FICO扩展和FICO行业选项。本手册中的分数统称为FICO分数。

有三种不同的经典FICO分数，每个分局各一个。根据www.fairisaac.com，他们在Equifax被标记为Beacon分数; FICO风险或经典（以前称为EMPIRICA）在TransUnion的得分; 和Experian的Experian / Fair Isaac风险模型评分。得分范围从300到850，分数越高反映信用风险越低。

NextGen FICO Risk scores draw their name from being touted as the "next generation" of credit bureau scores. They are branded as Pinnacle at Equifax; FICO Risk Score, NextGen (formerly PRECISION) at TransUnion; and Experian/Fair Isaac Advanced Risk Score at Experian. Compared to Classic scores, NextGen scores are reported to use more complex predictive variables, an expanded segmentation scheme, and a better differentiation between degrees of future payment performance. According to www.fairisaac.com, the score range, 150 to 950, is widened, although odds-to-score ratios at interval score ranges remain the same. Cumulative odds may vary.

For accounts lacking sufficient credit file information to generate a Classic or NextGen FICO score, some lenders use the FICO Expansion score. The FICO Expansion score, introduced in 2004, likely draws its name from "expanding" the credit information considered in the score to beyond that collected in a standard credit report. The expanded information includes items such as payday loans, checking account usage, and utility and rental payments. The FICO Expansion score has the same range and scaling as the Classic scores.

FICO Industry Options scores draw their name from being specific to several options of industries, such as bankcard.

NextGen FICO风险评分将他们的名字从被吹捧为信用局分数的“下一代”中得名。它们在Equifax被打成品尼高品牌; FICO风险评分，TransUnion的NextGen（原PRECISION）;和Experian的Experian / Fair Isaac高级风险评分。与经典分数相比，NextGen分数据报道使用更复杂的预测变量，扩展的分割方案以及未来支付表现程度之间的更好区分。根据www.fairisaac.com的数据，得分范围从150到950不等，虽然间隔得分范围内的赔率与得分比率保持不变。累积赔率可能会有所不同。

对于缺乏足够信用档案信息以生成Classic或NextGen FICO评分的帐户，某些贷方会使用FICO Expansion评分。 2004年推出的FICO扩展评分可能会将评分中考虑的信用信息“扩展”到标准信用报告中收集的信息之外。扩展信息包括诸如发薪日贷款，支票帐户使用情况以及公用事业和租赁付款等项目。 FICO扩展分数与经典分数具有相同的范围和缩放比例。

FICO行业选项分数从特定于行业的多个选项中抽取其名称，例如银行卡。

VantageScore
The bureaus historically used their own proprietary models (based on
Fair Isaac modeling) to develop FICO scores. However, in 2006, the
bureaus introduced a new scoring system under which a single methodology
is used to create scores at all three bureaus. The new system is called
VantageScore. Because a single methodology is used, the score for each
consumer should virtually be the same across all three bureaus. Any
differences are attributed to differences in data in the consumer's
files. The score will continue to incorporate typical consumer report
file content but will range from 501 to 990. The scores are scaled
similar to the letter grades of an academic scale (A, B, C, D, and F).
Again, the higher the score, the lower the credit risk. Consumers may
likely have VantageScores that are higher than their FICO scores. This
is due to scaling and that phenomena alone does not indicate that a
consumer is a better credit risk than he or she was under the
traditional FICO score system. Further, when determining whether
subprime lending exists, the new scale will need to be considered (in
other words, 660 may not be a benchmark when looking at VantageScores).
The industry's rate of replacement of custom and generic scores with
VantageScore remains to be seen as of the writing of this manual.

该局历来使用他们自己的专有模型（基于Fair Isaac建模）来开发FICO评分。然而，在2006年，该局引入了一种新的评分系统，在该系统下，所有三个局都使用单一方法来创造评分。新系统称为VantageScore。由于使用了单一的方法，因此每个消费者的分数在所有三个分局中应该几乎相同。任何差异都归因于消费者文件中数据的差异。评分将继续包含典型的消费者报告文件内容，但范围从501到990.评分与学术等级（A，B，C，D和F）的字母等级相似。再次，得分越高，信用风险越低。消费者可能拥有高于FICO分数的Vantage分数。这是由于规模扩张，仅凭这些现象并不表明消费者的信用风险比传统的FICO评分系统更高。此外，在确定次级贷款是否存在时，需要考虑新的规模（换句话说，在查看VantageScores时，660可能不是基准）。在撰写本手册时，业界用VantageScore替代定制和通用分数的比率仍然不会被看到。

Other Scores
In addition to or instead of generic credit bureau scores, many banks
use other types of scores. Brief discussions on a variety of these
scores follow, in alphabetical order. The bureaus and other vendors
offer models for many of these types of scoring.

Application Scoring:申请评分

Application scoring involves assigning point values to predictive
variables on an application before making credit approval decisions.
Typical application data include items like length of employment, length
of time at current residence, rent or own residence, and income level.
Points for the variables are summed to arrive at an application score.
Application scores can help determine the credit's terms and
conditions.

Attrition Scoring:

Attrition scores attempt to identify consumers that are most
likely to close their accounts, allow their accounts to go dormant, or
sharply reduce their outstanding balance. Identification of such
accounts may allow management to take proactive measures to
cost-effectively retain the accounts and build balances on the accounts.

Bankruptcy Scoring:

Bankruptcy scores attempt to identify borrowers most likely to
declare bankruptcy. HORIZON (by Fair Isaac) is a common credit bureau
bankruptcy score.

Behavior Scoring:

Behavior scoring involves assigning point values to
internally-derived information such as payment behavior, usage pattern,
and delinquency history. Behavior scores are intended to embody the
cardholder's history with the bank. Their use assists management with
evaluating credit risk and correspondingly making account management
decisions for the existing accounts. As with credit bureau scores, there
are a number of scorecards from which behavior scores are calculated.
These scorecards are designed to capture unique characteristics of
products such as private label, affinity, and co-branded cards.

Behavior scoring systems are often periodically supplemented with
credit bureau scores to predict which accounts will become delinquent.
Using a combination allows management to evaluate the composite level of
risk and thus vary account management strategies accordingly.

Adaptive control systems (ACS) commonly use behavior scoring. ACS
bring consumer behavior and other attributes into play for decisions in
key management disciplines (for instance, line management, collections,
and authorizations) so as to reduce credit losses and increase
promotional opportunities. ACS include software packages that assist
management in developing and analyzing various strategies taking into
account the population and economic environment. They are a combination
of software actionable analytics and optimization techniques and use
risk/reward logic. ACS recognize that accounts can go in several
directions. They consider the possible outcomes of the options and
determine the "best" move to make. With ACS, challenger strategies can
be tested on a portion of the accounts while retaining the existing
strategy (champion strategy) on the remainder. Continual testing of
alternative strategies can help the bank achieve better profits and
control losses. Many large banks use TRIAD (developed by Fair Isaac) or a
similar ACS, but smaller banks may lack the capital or the
infrastructure to implement such a process.

其他分数
除了通用信用局分数之外，许多银行还使用其他类型的分数。按照字母顺序对各种这些分数进行简要讨论。局和其他供应商提供了许多这些类型的评分模型。

应用评分：

应用程序评分涉及在作出信用审批决定之前将点值分配给应用程序中的预测变量。典型的应用数据包括工作时间，当前住所的时间长短，租金或自己的住所以及收入水平等项目。将变量点数加起来得出应用程序得分。应用程序分数可以帮助确定信贷的条款和条件。

消耗评分：

消耗分数试图识别最有可能关闭账户的消费者，允许他们的账户进入休眠状态，或大幅减少他们的未结余额。识别这些账户可能会使管理层采取积极措施，以经济有效地保留账户并在账户上建立余额。

破产评分：

破产分数试图找出最有可能宣布破产的借款人。 HORIZON（Fair Isaac）是一个共同的信用局破产评分。

行为评分：

行为评分包括将点值分配给内部派生信息，例如支付行为，使用模式和犯罪历史记录。行为分数旨在体现持卡人与银行的历史记录。它们的使用有助于管理层评估信用风险并相应地为现有账户制定账户管理决策。与信用局分数一样，还有一些计分行为得分的记分卡。这些记分卡旨在捕捉产品的独特特征，如私人标签，亲和力和联名卡。

行为评分系统通常会定期补充信贷局分数以预测哪些账户会出现违规行为。使用组合可以让管理层评估风险的综合水平，从而相应地改变客户管理策略。

自适应控制系统（ACS）通常使用行为评分。 ACS在关键管理领域（例如，生产线管理，收集和授权）中为决策带来消费者行为和其他属性，以减少信用损失并增加促销机会。 ACS包括软件包，可帮助管理层在考虑人口和经济环境的情况下开发和分析各种战略。它们是软件可操作的分析和优化技术的组合，并使用风险/回报逻辑。 ACS认识到账户可以分几个方向。他们考虑选择的可能结果并确定“最佳”举措。对于ACS，挑战者策略可以在部分账户中进行测试，同时保留剩余部分的现有策略（冠军策略）。对替代策略的持续测试可以帮助银行实现更好的利润并控制损失。许多大型银行使用TRIAD（由Fair Isaac开发）或类似的ACS，但小型银行可能缺乏实施此类流程的资金或基础设施。

Collection Scoring:

Collection scoring systems rank accounts by the likelihood of
taking delivery of payments due. They are used to determine collection
strategies, collection queue assignments, dialer queue assignments,
collection agency placement, and so forth. Collection scores are
normally used in the middle to late stages of delinquency.

Fraud Detection Scoring:

Fraud detection scores attempt to identify accounts with potential
fraudulent activity. Fraud continues to be pervasive in the credit card
lending industry and detection of potential fraudulent activity can
help identify and control losses as well as assist management in
developing fraud prevention controls.

Payment Projection Scoring:

Payment projection scoring models use internal data to rank
accounts, normally by the relative percentage of the balance that is
likely to be repaid. Some models only forecast the relative percentage,
while others rank the likelihood a cardholder will pay a moderate to
high level of the account balance. The scores are normally used in the
early to middle stages of delinquency.

Recovery Scoring:

Recovery scoring models rank order the amount of recovery that is
expected after charge-off. They aid management in deploying the
necessary resources where collection is most likely and help with agency
placement and sale decisions.

Response Scoring:

Response scoring models are used to manage acquisition costs. By
identifying the consumers that are most likely to respond, a bank is
able to tailor its marketing campaigns so as to target its marketing
toward those consumers that are most likely to respond and to steer away
from spending marketing dollars on consumers that are least likely to
respond.

Revenue Scoring:

Revenue scoring models rank order the potential revenue expected
to be generated on new accounts during the first 12-month period. The
models use predictive indicators such as usage ratios, the level of
revolving balances, and other card-usage patterns. Revenue scoring
allows management to focus marketing initiatives on what are expected to
be the most profitable accounts. Used in conjunction with credit bureau
scores in screening applicants, they allow management to evaluate the
revenue potential as well as the risk ranking of prospects.
Consequently, management is better able to identify its target market
and tailor its solicitations to that market.

Revenue scoring is also used to manage existing accounts according
to revenue potential. Strategies can be formulated recognizing the
risk, revenue, and frequency of cardholder use. From this information,
management is better able to reward low-risk, product-loyal consumers by
reducing APRs or waiving fees. Conversely, management is apt to raise
APRs and fees for consumers who exhibit higher risk or that evidence
little product loyalty.

收藏评分：

收款评分系统根据交付到期付款的可能性对帐户进行排名。它们用于确定收集策略，收集队列分配，拨号程序队列分配，收集代理机构布局等。收集分数通常用于犯罪的中后期阶段。

欺诈检测评分：

欺诈检测分数试图识别具有潜在欺诈活动的账户。欺诈行为在信用卡借贷行业仍然普遍存在，发现潜在的欺诈活动可帮助识别和控制损失，并协助管理层制定防欺诈控制措施。

付款投影得分：

支付预测评分模型使用内部数据来对账户进行排名，通常是通过可能偿还的余额的相对比例。有些模型只预测相对百分比，而另一些模型则将持卡人支付中等到高等级账户余额的可能性排列在等级上。该分数通常用于犯罪的早期至中期阶段。

恢复评分：

恢复计分模型将排序后的恢复量排序。它们帮助管理部门在收集最有可能的情况下部署必要的资源，并有助于机构安置和销售决策。

反应评分：

反应评分模型用于管理采购成本。通过识别最有可能响应的消费者，银行能够定制其营销活动，以便将其营销针对最有可能响应的消费者，并且避免将营销资金花费在最不可能的消费者身上响应。

收入评分：

收入评分模型排名第一个12个月期间预计在新账户中产生的潜在收入。这些模型使用预测指标，如使用率，循环余额水平和其他卡使用模式。收入评分使管理层可以将营销活动集中在预期效益最高的账户上。与筛选申请人的信用局分数一起使用时，它们允许管理层评估收入潜力以及潜在客户的风险等级。因此，管理层能够更好地确定其目标市场并调整其对该市场的招揽。

收入评分也用于根据收入潜力来管理现有账户。可以制定策略来识别持卡人使用的风险，收入和频率。从这些信息中，管理层能够更好地通过减少年利率或免收费用来奖励低风险，产品忠诚的消费者。相反，管理层倾向于提高展现较高风险或证明产品忠诚度不高的消费者的年利率和费用。

Dual-Scoring Matrix
A dual-scoring matrix is a system which uses one score on one axis
and another score on its other axis. Examiners should normally expect to
see dual scoring in more complex credit card operations. Any scoring
system may interface with another, but a commonly employed dual-scoring
matrix uses application and credit bureau scores. The use of two scores
allows management to more effectively segment applicants. Each score has
a cut-off level (as discussed later in this chapter). Applicants that
either pass or fail both cut-off scores are either accepted or rejected,
respectively. A gray area arises when an applicant passes one cut-off
but fails the other. These situations afford management a greater
opportunity to maximize approvals or minimize losses by including
potentially good credit risk or by excluding potentially bad credit risk
that may have gone undetected in a single-scoring system. Taking
advantage of this opportunity requires a thorough tracking system so
that management can determine the historical loss rates for the score
combinations in the gray area. Cut-off scores can then be adjusted so
that the best scoring combinations are approved and so that applicants
who would be approved under a single-score system, yet still pose
unacceptable risks, can be identified and excluded.

双重打分矩阵
双重打分矩阵是一个系统，它在一个轴上使用一个分数，在另一个轴上使用另一个分数。审查员通常应该期望在更复杂的信用卡操作中看到双重打分。任何评分系统都可能与另一个评分系统交互，但通常使用的双重评分矩阵使用申请和信用评分机构评分。使用两个分数可以让管理层更有效地细分申请人。每个分数都有一个截止水平（如本章后面所讨论的）。分别通过或未通过截止分数的申请人分别被接受或拒绝。当申请人通过一次切断但另一次切断时，会出现灰色区域。这些情况为管理层提供了更大的机会，通过纳入潜在的良好信用风险或排除在单一评分系统中未被发现的潜在不良信用风险，从而最大限度地获得批准或最大限度地减少损失。利用这个机会需要一个完善的跟踪系统，以便管理层可以确定灰色地带中得分组合的历史损失率。然后可以调整截止分数，以便批准最好的评分组合，并且可以识别并排除那些在单一评分系统下批准但仍然会造成不可接受风险的申请人。

Credit Scoring Model Development
Scoring can be done with generic models, semi-custom models, or
custom models. When properly designed, models are usually more reliable
than subjective or judgmental methods. However, development and
implementation of scoring models and review of these models present
inherent challenges. These models will never be perfectly right and are
only good if users understand them completely. Further, errors in model
construction can lead to inaccurate scoring and consequently to booking
riskier accounts than intended and/or to a failure to properly identify
and address heightened credit risk within the loan portfolio. Errors in
construction can range from basic formula errors to sample-bias to use
of inappropriate predictive variables.

可以使用通用模型，半定制模型或定制模型来完成评分。如果设计得当，模型通常比主观或判断方法更可靠。然而，评分模型的开发和实施以及对这些模型的审查提出了固有的挑战。这些模型永远不会是完全正确的，只有用户完全理解它们才是好的。此外，模型构建中的错误可能导致计分不准确，从而导致预订风险较高的账户比预期的和/或未能正确识别和处理贷款组合中的加强信贷风险。施工中的错误可能从基本公式误差到样本偏差到使用不适当的预测变量。

A scoring model evaluates an applicant's creditworthiness by
bundling key attributes of the applicant and aspects of the transaction
into a score and determines, alone or in conjunction with an evaluation
of additional information, whether an applicant is deemed creditworthy.
In brief, to develop a model, the modeler selects a sample of consumer
accounts (either internally or externally) and analyzes it statistically
to identify predictive variables (independent variables) that relate to
creditworthiness. The model outcome (dependent variable) is the
presumed effect of, or response to, a change in the independent
variables.

The sample selected to build the model is one of the most
important aspects of the developmental effort. A large enough sample is
needed to make the model statistically valid. The sample must also be
characteristic of the population to which the scorecard will be applied.
For example, as stated in the March 1, 1999 Interagency Guidance on Subprime Lending
(Subprime Lending Guidance), if the bank elects to use credit scoring
(including application scoring) for approvals or pricing in a subprime
lending program, the scoring model should be based on a development
population that captures the behavioral and other characteristics of the
subprime population targeted. Because of the significant variance in
characteristics between subprime and prime populations, banks offering
subprime products should not rely on models developed solely for
products offered to prime borrowers.

Both a large number of good and bad accounts are necessary to
maximize the model's effectiveness. There are no hard and fast rules,
but the sample selected normally includes at least 1,000 good, 1,000
bad, and about 750 rejected applicants. Often, the sample contains a
much higher volume of accounts. The definition of good and bad accounts
(the dependent variable) differs among banks, especially between prime
and subprime issuers. Furthermore, definitions of bad for scoring
purposes are not necessary the same as definitions of bad used by banks
for charge-off or nonaccrual consideration. For prime portfolios, good
accounts tend to be defined as accounts with sufficient credit history
and little or no delinquency. Bad accounts for prime portfolios are
normally distinguished by adverse public records, delinquency of 90 days
or more, accounts with a history of delinquency, and accounts
charged-off. Rejected applicants are applicants that management refused
to accept because of their risk parameters. Certain inferences are made
to break down the rejected applicants into good and bad accounts. This
procedure, known as reject inferencing,
makes certain assumptions on how rejected applicants would have
performed had they been accepted and attempts to mitigate any
accept-only bias of the sample. The process is used as it would be
cost-prohibitive and potentially detrimental to make loans to consumers
who would otherwise be rejected just for the sake of improving models.

After a representative sample has been assembled, the accounts are
analyzed to determine the characteristics and attributes common to each
group. The characteristics may be based on data sources such as the
consumer's credit report, the consumer's application, and the bank's
records. Characteristics are the questions asked on the application or
performance categories of the credit bureau report. Attributes are the
answers given to questions on the application or entries on the credit
bureau report. For example, if education is a characteristic, college
degree or high school diploma illustrate possible attributes.

The characteristics, which may number in the hundreds, are refined
into a much smaller group of predictive variables, which are those
items thought to best indicate whether a new applicant will eventually
fall into the good or bad performance category. Ideally, the predictive
variables also maintain a stable relationship with the performance
measurement over-time. Commonly used predictive variables include, but
are not limited to, prior credit performance（F）, current level of
indebtedness（R）, amount of time credit has been in use（F）, pursuit of new
credit（R）, time at present address（R）, time with current employer（F）, type of
residence, and occupation（F）. Examiners should expect that management has
excluded factors lacking predictive value or that by law cannot be used
in the credit decision-making process (such as race).

Once the predictive variables have been selected, points are
assigned to the attributes of those variables. Each attribute is awarded
points, and determining the number of points to award each attribute
may be the most difficult element of the process. There are several
methods for calculating and assigning points, all using a form of
multivariate statistics. A scoring table is constructed, for which
characteristics are on one axis and attributes are on the other axis.
Points are awarded to each cell of the matrix. The consumer's
characteristics and attributes are compared with the scoring table, or
scorecard, and are awarded points according to where they fall within
the table. The points are tallied to arrive at the overall score.
Whether a high score means low or high risk depends on the model's
construction.

Once designed and prior to implementation, the model is evaluated
for integrity, reliability, and accuracy by a party independent of its
design. This process is referred to as validation. A sample from the
development sample may be held-out and scored with the new model.
Performance is then monitored, and a model that demonstrates separation
and rank ordering on the hold-out sample is considered valid.
Validations for independent samples are also usually conducted prior to
release of the model and post-implementation.

Validation has long been fundamental to a successful score
modeling process, and evaluating a bank's model validation process has
long been a central component of the examination. The Subprime Lending
Guidance requires management to review and update models for subprime
lending to ensure that assumptions remain valid. Validation is also an
integral part of the proposed rulemaking for the revised Basel capital
accord.

评分模型通过将申请人的关键属性和交易的各个方面捆绑到一个评分中来评估申请人的信誉，并单独或与评估附加信息一起确定申请人是否被视为信誉良好。简而言之，为了开发一个模型，建模者选择一个消费者账户样本（内部或外部）并对其进行统计分析，以确定与信誉相关的预测变量（独立变量）。模型结果（因变量）是独立变量变化的假设影响或对其的反应。

选择构建模型的样本是开发工作中最重要的一个方面。需要足够大的样本来使模型在统计上有效。样本也必须是记分卡将应用到的人群的特征。例如，正如1999年3月1日关于次级贷款机构间指导（次级贷款指导）所述，如果银行选择使用信用评分（包括应用评分）进行次级贷款项目的批准或定价，评分模型应为基于一个发展人口，捕捉次级人口目标的行为和其他特征。由于次级抵押贷款和主要人口之间特征的显着差异，提供次级抵押贷款产品的银行不应该仅仅依赖为提供借款人提供的产品而开发的模型。

为了使模型的有效性最大化，大量好的和坏的帐户都是必需的。没有硬性规定，但所选样本通常包括至少1,000个好的，1,000个差的和大约750个被拒绝的申请人。通常，样本包含的账户数量要高得多。好与坏账户（因变量）的定义在银行之间有所不同，特别是在主要和次级发行人之间。此外，对于评分不好的定义并不需要与银行用于冲销或非重要考虑的坏的定义相同。对于主要投资组合，良好的账户往往被定义为具有足够信用记录且很少或没有犯罪的账户。优秀投资组合的坏账通常由不良公共记录，拖欠90天或以上，具有拖欠历史的账户以及账户被撇帐来区分。被拒绝的申请人是管理层因其风险参数拒绝接受的申请人。有些推论将被拒绝的申请人分成好的和坏的帐户。这种被称为拒绝推理的程序对被拒绝的申请人在被接受时如何履行的做法作出了某些假设，并试图减轻样本的任何接受偏差。这个过程被使用，因为它会成本过高，并且可能不利于向消费者提供贷款，否则这些消费者只是为了改进模型而被拒绝。

在组装代表性样本之后，对账户进行分析以确定每个组的共同特征和属性。这些特征可能基于数据来源，例如消费者的信用报告，消费者的申请以及银行的记录。特征是在信用局报告的申请或表现类别上提出的问题。属性是针对申请或信用局报告中的条目提出的问题的答案。例如，如果教育是一个特征，大学学位或高中文凭可以说明可能的属性。

数以百计的特征可以细化为一小部分预测变量，这些变量是被认为最能说明新申请人是否最终会陷入好或差的表现类别的那些项目。理想情况下，预测变量与绩效衡量的持续时间也保持稳定的关系。常用的预测变量包括但不限于先前的信用表现，当前的负债水平，信贷使用的时间量，追求新的信用，目前地址的时间，当前雇主的时间，居住类型以及OCCUP

Basel Considerations Regarding Credit Scoring
A brief discussion on the new Basel capital accord is housed in the
Capital chapter. Under the proposed rulemaking, banks that use an
Internal Ratings Based (IRB) approach would use internal estimates of
certain risk parameters as key inputs when determining their capital
requirements. The IRB approach requires banks to assign each retail
exposure to a segment or pool with homogeneous risk characteristics.
These characteristics are often referred to as primary risk drivers and
may include credit scores.

A bank must be able to demonstrate a strong relationship between
the IRB risk drivers (such as scores) and comparable measures used for
credit risk management. Thus, even if a bank uses custom scores for
underwriting or account management, generic bureau scores could possibly
be used for IRB segmentation purposes if the bank can demonstrate a
strong correlation between these measures. A bank using credit scores as
segmentation criterion would have to validate the choice of the score
(bureau, custom, and so forth) as well as demonstrate that the scoring
system has adequate controls.

Examiners will expect that all aspects of the risk segmentation
system, including credit scoring, are subject to thorough, independent,
and well-documented validation. Validation for the risk segmentation
system is ultimately tied to validation of the bank's quantification of
IRB risk parameters. Examiners will also expect that the IRB validation
process include:

Evaluating the developmental evidence or logic of the system.
Ongoing monitoring of system implementation and reasonableness (verification and benchmarking).
Comparing realized outcomes with predictions (back-testing).

Validation
Examiners should determine whether management provides for
appropriate, ongoing validation of scoring models, whether used as part
of an IRB framework, for credit risk management, or for other purposes.
Validation is a process that tests the scoring system's ability to rank
order as designed and essentially answers whether the model is accurate
and working properly. Model validation does not only increase
confidence in the reliability of a model but also promotes improvements
and a clearer understanding of a model's strengths and weaknesses among
management and user groups. Model validation can be costly, particularly
for smaller banks. But, using un-validated models to manage risks is a
poor business practice that can be even more costly as well as lead to
safety and soundness concerns. Risks from not validating are elevated
when a bank bases its credit card lending decisions on the scoring model
alone (and does not consider other factors in the decision-making
process), when the model is otherwise vital, or when the model is
complex.

Examiners do not validate models; rather, validation is the
responsibility of bank management. Examiners do, however, test the
effectiveness of the bank's validation function by selectively reviewing
aspects of the bank's validation work. Examiners could also identify
concerns with a model's performance as a by-product of the credit risk
review or other examination procedures.

Examiners should evaluate the bank's validation framework,
including written validation policies, to determine if it is proper. Key
elements of a sound validation policy generally include:

Competent and Independent Review - The review should be as
independent as practicable. The reviewer can be an auditor with
technical skills, a consultant, or an internal party. In practice, model
validation requires not only technical expertise but also considerable
subjective business judgment.
Defined Responsibilities - The responsibility for model
validation should be formalized and defined just as the responsibility
for model construction should be formalized and defined.
Documentation - Validation cannot be properly performed if a
sufficient paper trail of the model's design is not available. Weak
documentation can be particularly damaging to the bank if the modeler
leaves and the replacement is left with little to reference. Model
documentation should summarize the general procedures used and the
reasons for choosing those procedures, describe model applications and
limitations, identify key personnel and milestone dates in the model's
construction, and describe validation procedures and results. Technical
complexity does not excuse modelers from the responsibility of providing
clear and informative descriptions of the model to management.
Ongoing validation - Validation should occur both pre- and
post-implementation. Models should be subject to controls so that coding
cannot be altered, except by approved parties. Most models are normally
altered in response to changes in the environment or to incorporate
improvements in understanding of the subject. Model alterations that are
inappropriate can result in dodging risk limits or disguising losses.
Auditor involvement - Examiners should expect that the bank's
audit program ensures that validation policies and procedures are being
followed.

A clear understanding of the scoring model's intended use is
critical to properly assessing a model's performance. But, regardless of
the intended use, the three key components of a validation process, as
mentioned in the prior section, apply: evaluation of the conceptual
soundness of the model; ongoing monitoring that includes verification
and benchmarking; and outcomes analysis.

Evaluating conceptual soundness involves assessing the quality of
the model's construction and design. Examiners should determine whether
management reviews documentation and empirical evidence supporting the
methods used and the variables selected in the model's design. Modelers
adopt methods, decide on characteristics, and make adjustments. Each of
these actions requires judgment, and validation should ensure that
judgments are well-informed. Examiners should expect management to
review developmental evidence for new models and when a material change
is made to an existing model.

The purpose of the second component of validation, ongoing
monitoring, is to confirm that the model was implemented appropriately
and continues to perform as intended. Process verification and
benchmarking are its key elements. Process verification includes making
sure that data are accurate and complete; that models are being used,
monitored, and updated as designed; and that appropriate action is taken
if deficiencies exist. Benchmarking uses alternative data sources or
risk assessment approaches to draw inferences about the correctness of
model outputs before outcomes are actually known. The time needed to
generate a sufficient number of representative accounts (good and bad)
to evaluate the effectiveness of the model post-implementation will vary
depending on the product-type or customer group. Consequently,
benchmarking becomes an important tool in the validation process because
it provides an earlier-read of model performance than is available from
back-testing.

对评分模型的预期用途的清晰理解对于正确评估模型的性能至关重要。但是，无论预期用途如何，验证过程的三个关键组成部分（如前一节所述）均适用于：评估模型的概念完整性;包括验证和基准在内的持续监测;和结果分析。

评估概念的可靠性涉及评估模型构建和设计的质量。审查员应确定管理层是否审查文件和支持所用方法和模型设计中所选变量的经验证据。建模者采用方法，决定特性并进行调整。这些行动中的每一个都需要判断，验证应该确保判断是充分知情的。审查员应期望管理层审查新模型的发展证据，以及何时对现有模型进行重大变更。

验证的第二个组成部分，即持续监控的目的是确认模型是否得到了适当实施，并继续按预期执行。过程验证和基准测试是其关键要素。过程验证包括确保数据的准确性和完整性;模型正在按照设计使用，监控和更新;如果存在缺陷，则采取适当行动。标杆管理使用替代数据来源或风险评估方法，在结果实际已知之前，对模型输出的正确性进行推断。生成足够数量的代表性帐户（好的和坏的）来评估模型实施后的有效性所需的时间将取决于产品类型或客户群。因此，基准测试成为验证过程中的一个重要工具，因为它提供了比回溯测试更早的模型性能。

The third component of validation, outcomes analysis, compares the
bank's forecasts of model outputs with actual outcomes. It should
include back-testing, which is the comparison of the outcomes forecasted
by the models with actual outcomes during a sample period not used in
model development (out-of-sample testing).

Benchmarking and back-testing differ in that when differences are
observed between the model output estimates and the benchmark, it does
not necessarily indicate that the model is in error. Rather, the
benchmark is an alternative prediction, and the difference may be due to
different data or methods. When reviewing the bank's benchmarking
exercises, examiners should find out whether management investigates the
source of the differences and determines whether the extent of the
differences is appropriate.

Examiners can compare the delinquency rate at each score interval
as a simple test of overall performance of the scoring system. If the
system is performing adequately, a correlation between the scores and
delinquency rates (that is, delinquency rates increase as projected risk
(as reflected in the scores) increases) should be evident. Examiners
may also want to review the results of various tests that management may
be using. For example, divergence statistics and the population
stability index are sometimes used. Divergence statistics measure the
distance between the average score of satisfactory accounts and average
score of unsatisfactory accounts. The greater the distance, the more
effective the scoring system is at segregating good and bad accounts. If
the difference is small, a new or redeveloped scoring system may be
warranted. The population stability index compares divergence with the
original development sample and helps identify and measure erosion in
the model's predictive power. Other advanced statistical tools include
Chi square, Kolomogorov-Smirnov (K-S) tests, and Gini coefficients.
While examiners generally do not need to know the specifics of all of
these types of tests, they should be aware that these tests are common
in the industry and should expect management to be able to explain the
validation tools used. Management's development of effective processes
and exercise of sound judgment are just as important as the measurement
technique used.

Incorporation of combinations of model expertise and skill levels
in the validation process is not uncommon. For example, internal staff
could be used to verify the integrity of data inputs while a third party
could be used to validate model theory and code. Examiners should
determine what management's procedures are for ensuring that vendors'
validation procedures are appropriate and meet the bank's standards.
Management is ultimately responsible for ensuring the validation
processes used, whether internal or external, are appropriate and
adequate.

While scoring models developed in-house are becoming more
prevalent, banks continue to purchase a number of models from vendors
and the bureaus. Vendors are sometimes unwilling to share key formulas,
assumptions, and/or program coding. In these cases, the vendor typically
supplies the bank with validation reports performed by independent
parties. The independent party's work can only be relied on if the
information provided is sufficient to determine the adequacy of the
scope, the proper conveyance of findings to the vendor, and the adequacy
of the vendor's response thereto. Examiners assessing risks of modeling
activities should pay particular attention to situations in which
management has exclusively relied on a vendor's general acceptance by
others in the industry as sufficient evidence of reliability and has not
conducted its own comprehensive review of the vendor and its practices.

Examiners should evaluate management's processes for re-tooling or
re-developing models that exhibit eroding performance. If evidence
reliably shows that the behavior shift is small and likely to be of
short duration, a policy shift or change to the model may not be
warranted. But, if evidence suggests that the behavior shift is
material and is likely to be long-term, there are several approaches
management may consider to limit losses, depending on the ability to
identify the most likely reason(s) for the performance shift. It can
adjust its underwriting policy to narrow the market to a group believed
to perform better than the population in general. This usually involves
making changes to the bank's business strategy and, thus, is rather
limited as a short-term risk management tool. Banks may also develop or
purchase scoring models based on more recent information about the
current population. In this case, the bank must weigh the costs of
developing or purchasing a model against that of carrying an increased
number of bad accounts booked by the existing model. One of the most
common, and often the easiest, adjustments is to manage the cut-off
score to maintain a targeted loss rate consistent with profit
objectives.

Cut-off Score
Each bank develops its own policies and risk tolerances for its
credit card lending programs. Setting cut-off scores is one way banks
implement those risk tolerances. A cut-off score is the point below
which credit will not be extended and at or above which credit will be
extended (assuming a higher score equates to better creditworthiness). A
bank might have more than one cut-off score, with each tailored to a
specific population. The ability to customize cut-off scores allows
management to maximize the approval rate without sacrificing asset
quality. Some banks have cut-off bands, which define a range of scores
for which the consumer would undergo additional judgmental review.

Selecting a cut-off score involves determining the optimum balance
between approval and loss rates. Management evaluates how much
additional revenue will be added if the approval rate is increased and
what the cost associated with the incremental increase in the bad rate
will be. They also often give consideration to marketing expenses and
customer service expenses. How management chooses to balance the
competing goals determines the cut-off score. Odds charts are often
involved in setting cut-off scores and are discussed in the next
section.

As time passes, cut-off scores and models become less predictive
because of economic changes, demographic shifts, and entry into new
markets. Examiners should assess management's practices for reviewing
cut-off scores and models, including resulting acceptance and loss
rates. By monitoring the rates, management can appropriately adjust the
cut-off score to change either acceptance rates or loss rates, depending
on the strategic goals. For example, management could grow the
portfolio by lowering the cut-off score (when lower scores equate to
higher risk), taking on an elevated degree of credit risk and accepting
increased loss rates. These dynamics of the scoring environment
highlight the need for thorough tracking and calibration procedures.

Validation Charts and Calibration
Most scores are rank-order measurements that, by themselves, are
generally not indicative of the likelihood or magnitude of an event or
outcome. Rather, they summarize a plethora of consumer data and
essentially do little except rank order the consumer's risk against the
risk of other consumers. But, in addition to this rank-ordering, scores
must give accurate outcome (usually default) probabilities to be the
most useful. Calibration is the process by which a model's output (in
this case scores) is converted into the actual rate of the outcome
(default) and includes adjusting or modifying for the difference between
the expected rate based on the historical database and the actual rate
observed. The process is aimed at converting or modifying the model's
output into a probability based on the expected odds for the historical
population and adjusting for the relevant population. Often, it is
thought of as the process of determining and fine tuning the grades or
gradation of a quantitative measuring system by comparing them with a
set standard or starting point. Frequently the standard used might be a
bureau's validation chart.

In general, validation charts (also commonly known as odds charts)
reflect the estimate of the percentage of borrowers in a defined
population who will evidence a certain trait or outcome, such as
delinquency, loss, or bankruptcy. Examiners normally expect management
to develop its own odds chart(s) when it has sufficient historical data.
When properly developed, customized odds charts are more predictive
than odds charts that are available from the bureaus. Validation charts
available from the bureaus display the odds of poor performance (such as
delinquency, loss, or bankruptcy) observed at a given bureau score.
Each set of charts available from the bureaus is specific to a model, an
industry, and an application (where application refers to how the
scores will be used). For example, the bureaus have validation charts
available for the bankcard industry and for subprime lending. The
bureaus' validation charts can be helpful as a starting point for
management in setting risk strategies but do not precisely predict the
actual odds that each bank will experience. Rather, a bank's particular
market will have different characteristics and, thus, different odds.
The risk ranking based on bureau score will generally hold, but the
actual odds of going bad that each score represents will vary between
banks and portfolios. Thus, management must provide for sufficient
calibration processes. For example, if the bureau odds chart indicates
that 1 out of every 20 consumers with a credit score of XYZ will be a
bad account and the bank is realizing 5 out of every 20 consumers with a
credit score of XYZ is a bad account, calibration most likely is
needed.

Calibration most often adjusts or refines an odds chart when
significant variation exists from the general forecast. But, there are
other instances for which the scores and scaling could be adjusted, or
calibrated. For example, calibration might be used to make all scores
positive. For example, if a model's scores are (52), (6), and 15, an
entity could add 52 points, so the scores would be 0, 46, and 67. Also,
calibration might be used to compress the scale (for example, if every
31 points doubles the odds of bad, a bank could calibrate the scale such
that the bad odds are doubled every 20 points). Calibrations might also
be done to make users feel comfortable (for example, if an existing cut
off score is XYZ based on an internal model that predicts that one
percent of accounts with a score of XYZ will be bad, then calibration
could be used to ensure that accounts that are scored XYZ would continue
to tie to the likelihood that one percent will be bad. In this way, the
bank would not have to change the cut-off score to keep getting the
same caliber of customers). Examiners should ascertain whether recent
calibrations are well-documented and have been properly executed.

Overrides
Overrides are discussed in the Underwriting and Loan Approval Process
chapter. Exceptions outside of management's credit scoring parameters
are called overrides and may be high-side or low-side. When management
overrides the cut-off score, they introduce information into the
ultimate credit decision that is not considered in the scoring system.
If the scoring system is effectively predicting loss rates for a
designated population and the system reflects management's risk
parameters, examiners should expect that management use overrides with
considerable caution. Excessive overrides may negate the benefit for an
automated scoring system. A high volume of overrides is equivalent to
having no cut-off score and jeopardizes management's ability to measure
the success of the credit scoring system. Once a bank approves credits
that fail to meet the scoring system's criteria, it has broken its odds
and may be taking on higher levels of risk than acceptable for the
bank's risk appetite and/or capabilities to control. However, business
reasons may justify a temporary increase in override rates. For example,
when transitioning to a new system, override rates might rise until a
reasonable level of confidence in the new approach is achieved.

Credit Scoring Model Limitations
Determining whether scoring models are managed by people who
understand the models' strengths and weaknesses is an integral part of
the examination process. Users lacking a complete understanding of how
the models are made, how they should be used, or how they interface with
the bank's lending policies and procedures can expose the bank to
risks, as discussed throughout this chapter. Scoring is only useful if
its limitations are properly understood, and examiners should draw a
conclusion about whether an understanding of the model's limitations by
management is evident.

One limitation is that scoring model output is only as good as the
input that is used. If data going into the scoring model is inaccurate
(for instance, if information on the consumer's credit bureau report is
erroneous), the model's output (score) will be erroneous. Depending on
how the erroneous information is weighted in the scoring formula, the
impact on the score could be substantial. Moreover, if management does
not select and properly weight the best predictive variables, the
model's output will likely be less effective than had the most
predictive variables been used and properly weighted. Management must
make sure that the variables used in the models are appropriate,
predictive, and properly weighted to arrive at the best credit decision
and that data inputs are complete and accurate.

The effectiveness of the model output (scores) can also be
constrained by factors such as changing economic conditions and business
environments. Examiners should identify whether management monitors
warning signs of market deterioration, such as increases in personal
bankruptcies, which may affect the accuracy of model assumptions. Robust
models are typically more resilient to these types of changes.

Models, even if good at risk-ranking an overall market segment,
can be limited if they do not reflect the bank's population. A model is
typically developed for a certain target population and may be difficult
to adapt to other populations. In most cases, a credit scoring model
should only be used for the product, range of loan size, and market that
it was developed for. When a bank tries to adapt the model to a
different population, performance of that population may likely deviate
from expectation. When a bank implements or adapts a model to a new
market or population for which it was not designed, examiners should
determine whether management performs an analysis similar in scope to
the one used to validate the model at implementation.

Credit scoring is good at predicting the probability of default
but generally not at predicting the magnitude of losses. (Normally,
other models, such as loss models, focus on predicting the level
(magnitude) of risk.) Generic credit scoring models in particular most
likely rank order the risk appropriately but generally do not accurately
predict the level of the risk. Thus, banks that use generic models
should not assume that their loss rates will be the same as those
reflected in industry odds charts. How accounts ultimately perform
depends on a number of factors, including account management techniques
used, the size of line granted, and so forth.

Scorecards could be considered, by their very nature, to be
antiquated when they are put into production. They are based on lengthy
historic data and take time to develop. Moreover, models are calibrated
using historical data, so if relevant un-modeled conditions change, the
model can have trouble forecasting out of sample.

Along similar lines, during times of strong economic growth,
models may be ill-prepared to predict borrower performance in
recessionary conditions, particularly if the historic period observed
did not include recessionary conditions. There are several behaviors
that could impact the model's effectiveness in recessionary times. One
is that consumers might prioritize their payments to pay off secured
debt rather than unsecured debt. In hard times, this could leave a bank
that is holding the consumer's unsecured credit card debt as one of the
last to get paid, if paid at all.

The effectiveness of scoring models can also be limited by human
involvement. For example, when models are augmented by managerial
judgment (for instance, in the case of overrides), results from the
model and subsequent validation processes can become seriously
compromised. In addition, unsupported overconfidence in the models could
lead some banks to move up or down market to make larger or more risky
loans, respectively. Without proper model validation, such movements
could result in the bank taking on more credit risk than it can control.

Automated Valuation Models
Automated valuation models (AVMs) are sometimes used to support
evaluations or appraisals. Examiners should look at management's
periodic validation of AVMs for mitigating the potential valuation
uncertainty in the model and should confirm whether its documentation
covers analyses, assumptions, and conclusions. Validation includes
back-testing a representative sample of the valuations against market
data on actual sales (where sufficient information is available) and
should cover properties representative of the geographic area and
property type for which the tool is used. Many vendors provide a
"confidence score" which usually relates to the accuracy of the value
provided. Confidence scores come in many formats and are calculated
based on differing systems. Examiners should determine whether
management understands how the models work as well as what the
confidence scores mean and should confirm whether management has
identified confidence levels appropriate for the risk in given
transactions.

Summary of Examination Goals – Scoring and Modeling
The examiner's role is to evaluate scoring, model usage, and model
governance practices relative to the bank's complexity and the overall
importance of scoring and modeling to the bank's credit card lending
activities. The role includes:

Identifying the types of scoring systems used in the credit
card lending programs and whether the models are generic, custom, or
vendor-supplied. A model inventory is normally available for review.
Determining how management uses scores in its decision-making
processes and whether each model's use is consistent with the intended
purpose.
Assessing whether designated staff possess the necessary expertise.
Determining whether management thoroughly understands the models used.
Reviewing cut-off scores and odds charts to assess the level of risk being taken.
Testing the effectiveness of the bank's validation function by
selectively reviewing various aspects of the bank's validation work for
key models.
Evaluating the scope of validation work performed.
Reviewing reports summarizing validation findings and any additional workpapers necessary to understand findings.
Evaluating management's response to the reports, including remediation plans and timeframes.
Assessing the qualifications of staff or vendors performing the validation.
Assessing the bank's calibration procedures, including documentation thereof.
Determining whether credit bureau, behavior, and/or other scores enhance account management and collection practices.
Assessing override policies and practices.
Review the number/volume and types of overrides.
Verify that override reports are reviewed by management and that performance is adequately tracked.
Determine the impact, if any, of overrides on asset quality.
Assessing whether the bank's audit program appropriately considers models and oversight thereof.
Identifying instances in which management has taken action when
performance of the scoring model deteriorated and determine if the
action was appropriate, effective, and timely.
Determining if management is prepared to take future action if the scoring model's performance deteriorates.
Determining if there are any models under development.
Identify potential impacts on the bank from implementation of the forthcoming models.
Understand what prompted the model development.
Ascertain the planned implementation date of the model.
For models developed by third parties, assessing whether the
systems are supervised and maintained in accordance with vendor-provided
specifications and recommendations.

Examiners normally select models for review in connection with the
examination when model use is vital or increasing. Focus may also be
placed on models new or acquired since the prior examination.
Quantitative or information technology (IT) specialists are sometimes
needed for some complex models, but examiners normally can perform most
model reviews.

Chapter VII. – Underwriting and Loan Approval Process
https://www.fdic.gov/regulations/examinations/credit_card/ch7.html

General Underwriting Considerations

Program-Specific Underwriting Considerations

Affinity and Co-Branding Programs
        Private Label Programs
        Corporate Card Programs
        Subprime Credit Card Programs
        Home Equity Credit Card Programs
        Cash Secured Credit Card Lending
        Purchased Portfolios

Comparison of Automated and Judgmental Processes

Credit Bureau Preferences

Post-Screening

FACT Act

Multiple Accounts

Initial Credit Line Assignments

Policy and Underwriting Exceptions

Indices and Reporting

Summary of Examination Goals – Underwriting and Loan Approval Process

VII. Underwriting and Loan Approval Process

Underwriting is the process by which the lender decides whether an applicant is creditworthy and should receive a loan. An effective underwriting and loan approval process is a key predecessor to favorable portfolio quality, and a main task of the function is to avoid as many undue risks as possible. When credit card loans are underwritten with sensible, well-defined credit principals, sound credit quality is much more likely to prevail.

General Underwriting Considerations
To be effective, the underwriting and loan approval process should
establish minimum requirements for information and analysis upon which
the credit is to be based. It is through those minimum requirements that
management steers lending decisions toward planned strategic objectives
and maintains desired levels of risk within the card portfolio.
Underwriting standards should not only result in individual credit card
loans with acceptable risks but should also result in an acceptable risk
level on a collective basis. Examiners should evaluate whether the
bank's credit card underwriting standards are appropriate for the
risk-bearing capacity of the bank, including any board-established
tolerances.

Management essentially launches the underwriting process when it
identifies its strategic plan and subsequently establishes the credit
criteria and the general exclusion criteria for consumer solicitations.
Procedures for eliminating prospects from solicitation lists and
certain screening processes could also be considered initial stages of
the underwriting and loan approval process in that they assist in
weeding out consumers that may be non-creditworthy in relation to the
bank's risk tolerance level, identified target market, or product
type(s) offered.

Compared to other types of lending, the underwriting and loan
approval process for credit card lending is generally more streamlined.
Increasingly, much of the analytical tasks of underwriting are
performed by technology, such as databases and scoring systems. Whether
the underwriting and loan approval process for credit cards is
automated, judgmental, or a combination thereof, consistent inclusion of
sufficient information to support the credit granting decision is
necessary. Underwriting standards for credit cards generally include:

Identification and assessment of the applicant's repayment
willingness and capacity, including consideration of credit history and
performance on past and existing obligations. While underwriting is
based on payment history in most instances, there are cases, such as
some application strategies, in which guidelines also consider income
verification procedures. For example, assessments of income like self
employment income, investment income, and bonuses might be used.
Scorecard data.
Collateral identification and valuation, in the case of secured credit cards.
Consideration of the borrower's aggregate credit relationship with the bank.
Card structure and pricing information.
Verification procedures.

The compatibility of underwriting guidelines with the loan policy,
the strategic plan, and the desired customer profile should be
assessed. Examiners also determine whether such guidelines are
documented, clear, and measurable, such that management can track
compliance with and adherence to the guidelines. Moreover, examiners
should assess management's periodic review process for ensuring that
card underwriting standards appropriately preserve and strengthen the
soundness and stability of the bank's financial condition and
performance and are attuned with the lending environment.

In addition to the decision factors, management should also set
forth guidelines for the level and type of documentation to be
maintained in support of the decision factors. Records typically
include, but are not limited to, the signed application, the verified
identity of the borrower, and the borrower's financial capacity (which
may include the credit bureau report or score). In the case of secured
cards, records to look for include a collateral evaluation and lien
perfection documents. Another item of interest to review includes a
method of preventing application fraud such as name and address
verification, duplicate application detection, social security number
verification, or verification of other application information. The
verification level supported by management normally depends upon the
loan's risk profile as well as the board's risk appetite.

The process for altering underwriting terms and standards can
involve prominent decisions by management to amend policies and
procedures. However, more subtle or gradual modifications to the
application of the card underwriting policies and procedures can also
produce changes in bank's risk profile. For instance, the bank might
increase credit limits or target a higher proportion of solicitations to
individuals in lower score bands without reducing the minimum credit
score. Albeit less apparent, the resultant change can create significant
loan problems if not properly controlled. Examiners should assess
management's records that outline underwriting changes, such as
chronology logs, to determine whether the records are well-prepared and
complete and to identify underwriting changes that, individually or in
aggregate, may substantially impact the quality of accounts booked.

In the hyper-competitive credit card market, some banks may be
inclined to relax lending terms and conditions beyond prudent bounds in
attempts to obtain new customers or retain existing customers. Examiners
should be sensitive to all levels of credit easing and the potential
impact of the ease on the bank's risk profile. Rapid growth can, but
does not necessarily, indicate a decline in underwriting standards. In
addition, rising loss rates may indicate a weakening of underwriting
criteria. Examiners should also consider that the bank's appetite for
risk often involves balancing underwriting and the pricing structure to
achieve desired results. Thus, management may have priced the products
to sufficiently compensate for the increased risk involved in easing
credit standards. Take, for example, subprime loans which typically
exhibit higher loss rates. They can be profitable, provided the price
charged is sufficient to cover higher loss rates and overhead costs
related to underwriting, servicing, and collecting the loans. Examiners
should sample management's documentation that supports credit decisions
made. Management's documentation might include the contribution to the
net interest margin and noninterest income in relation to historical
delinquencies and charge-offs compared to other types of card programs.
When relaxed credit underwriting is identified, examiners should assess
the adequacy of the total strategy.

Results of credit underwriting weaknesses are not limited to
elevated credit risk. For example, the weaknesses may cause difficulties
in securitization or sales of the underwritten assets, thereby
elevating liquidity risk. Further, future credit enhancements and
pricing for securitizations may be more costly or less readily available
when poorly underwritten receivables adversely affect the bank's
reputation. In some cases, access to securitization-based funding may
vanish. Impairment of a bank's reputation as an underwriter can limit
accessibility to financial markets or can raise the costs of such
accessibility.

Program-Specific Underwriting Considerations

Affinity and Co-Branding Programs
Examiners normally expect banks to refrain from materially modifying
underwriting standards for affinity and co-branded card customers.
Rather, credit card underwriting guidelines for partnered programs
should generally be compatible with the bank's loan policy, strategic
plan, and desired customer profile. If underwriting practices diverge
from the bank's normal standards, examiners need to determine the
appropriateness of program differences and the overall impact on
portfolio quality. They should look for evidence that management has
ensured that the eased standards still result in an acceptable level of
risk and that any elevated risks are appropriately addressed.

Private Label Programs
Examiners should expect management to pay careful attention to the
financial condition of the retail partner when it determines whether to
offer private label cards. They also normally expect management to
refrain from materially modifying underwriting standards to accommodate
its retail partners. A retailer that aims to maximize the number of
cards in circulation may expect the bank to lower its credit standards.
If the bank lowers its credit standards, management should ensure that
the standards still result in an acceptable level of risk and that any
elevated risks are appropriately addressed.

Loss-sharing agreements can be an effective means to mitigate risk
and give merchants reason to accept more conservative underwriting
standards. With a loss-sharing agreement, either the bank's loss rate is
capped at a certain percentage or the merchant covers a certain
percentage of the dollar volume of losses. The retail partner's share of
losses can be quite high, and the bank's role may be more similar to
that of a servicer than a lender. Examiners should analyze management's
practices for ensuring that the retailer has the financial capacity to
cover its portion of the losses. They should also gauge management's
procedures for analyzing and responding to contingencies, such as if the
retailer was to file bankruptcy and the cardholders were not compelled
to repay their balances.

Corporate Credit Card Programs
Corporate credit card programs may pose more commercial credit risk
than consumer credit risk because the company may be primarily liable
for the debt. In cases where the corporation is primarily liable for the
debt, examiners should expect that management's decision to grant the
line of credit is consistent with the institution's commercial loan
underwriting standards. The credit granting process should also consider
relationships that the company has with the bank's commercial banking
department. Examiners should review the contract terms of corporate
credit card programs in a manner similar to how they would review any
other commercial loan file. Documentation should include management's
assessment of the financial condition of the company along with its
willingness to pay in a timely manner. Examiners should also ascertain
whether the bank or the corporate borrower decides which company
employees receive corporate cards. It the borrower decides, examiners
should determine what controls the bank uses to reduce risk.

Subprime Credit Card Programs
Subprime lending is generally defined as providing credit to
consumers who exhibit characteristics that suggest a much higher risk of
default as compared to the risk of default with traditional bank loan
customers. Examiners should evaluate whether management has carefully
attended to underwriting standards for subprime credit card programs.
Underwriting for subprime credit cards is usually based upon credit
scores generated by sophisticated scoring models, which use a
substantial number of attributes
to determine the probability of loss for a potential borrower. Those
attributes often include the frequency, severity, and recency of
delinquencies and major derogatory items, such as bankruptcy. When
underwriting subprime credit cards, banks generally use risk-based
pricing as well as tightly controlled credit limits to mitigate the
increased credit risk evident in the consumer's profile. Banks may also
require full or partial collateral coverage, typically in the form of a
deposit account at the bank. Credit availability and card utility
concerns are other important considerations.

Home Equity Credit Card Programs
Home equity lending in general has recently seen rapid growth and
eased underwriting standards. The quality of real estate secured credit
card portfolios is usually subject to increased risk if interest rates
rise and/or home values decline. As such, sound underwriting practices
are indispensable in mitigating this risk. Examiners should look for
evidence that management considers all relevant risk factors when
establishing product offerings and underwriting guidelines. Generally,
these factors include borrowers' income and debt levels, credit score
(if obtained), and credit history, as well as loan size, collateral
value (including valuation methodology), and lien position. Examiners
should determine whether effective procedures and controls for support
functions, such as perfecting liens, collecting outstanding loan
documents, and obtaining insurance coverage, are in place.

For real estate secured programs, compliance with the following guidance is considered:

Part 365 of the FDIC Rules and Regulations – Real Estate Lending Standards, including Appendix A which contains the Interagency Guidelines for Real Estate Lending Policies.
Interagency Appraisal and Evaluation Guidelines.
Interagency Guidance on High Loan-to-Value Residential Real Estate Lending.
Home Equity Lending Credit Risk Management Guidance issued May 24, 2005.

Other laws, several of which are reviewed during the compliance examination, also apply.

Part 365 requires banks to maintain written real estate lending
policies that are consistent with sound lending principles and
appropriate for the size of the institution as well as the nature and
scope of its operations. It specifically requires policies that include,
but are not limited to:

Prudent underwriting standards, including LTV limits.
Loan administration procedures.
Documentation, approval and reporting requirements.

Consistent with the agencies regulations on real estate lending
standards, prudently underwritten home equity credit card loans should
include an evaluation of a borrower's capacity to adequately service the
debt. Considering the real estate product's sizable credit line
typically extended, an evaluation of repayment capacity should most
often consider a borrower's income and debt levels and not just the
borrower's credit score. A prominent concern is that borrowers will
become overextended, and the bank may have to consider foreclosure
proceedings. As such, underwriting standards should emphasize the
borrower's ability to service the card line from cash flow rather than
the sale of the collateral. If the bank has offered a low introductory rate, repayment capacity should consider the rate that could be in effect at the conclusion of the introductory term.

A potentially dangerous misstep in underwriting home equity credit
cards is placing undue reliance upon a property's value in lieu of an
adequate initial assessment of an applicant's repayment ability.
However, establishing adequate real estate collateral support in
conjunction with appropriately considering the applicant's repayment
ability is a sensible and necessary practice for home equity credit card
lending.

Examiners should expect that management has established criteria
for determining an appropriate real estate valuation methodology (for
example, higher-risk accounts should be supported by more thorough
valuations) and requires sufficient documentation to support the
collateral valuation. Banks have streamlined real estate appraisal and
evaluation processes in response to competition, cost pressures, and
technological advancements. These changes, coupled with elevated LTV
risk tolerances, have heightened the importance of strong collateral
valuation policies and practices. The Interagency Appraisal and Evaluation Guidelines
sets forth expectations for collateral valuation policies and
procedures. Use of automated valuation models (AVMs) and other
collateral valuation tools for the development of appraisals and
evaluations is increasingly popular. AVMs are discussed in the Scoring
and Modeling chapter.

Management is expected to establish limitations on the amount
advanced in relation to the value of the collateral (LTV limits) and to
take appropriate measures to safeguard its lien position. Examiners
should determine whether management verifies the amount and priority of
any senior liens prior to the loan closing when it calculates the LTV
ratio and assesses the collateral's credit support. The Interagency Guidelines for Real Estate Lending Policies (Appendix A to Part 365) and the Interagency Guidance on High LTV Residential Real Estate Lending address
LTV considerations, including supervisory LTV limitations. There are
several factors besides LTV limits that influence credit quality.
Therefore, credit card loans that meet the supervisory LTV limits should
not automatically be considered sound, and credit card loans that
exceed the supervisory LTV limits should not automatically be considered
high risk. Examiners should refer to the mentioned guidance and to the
Risk Management Manual of Examination Policies for LTV details, such as
reporting requirements and aggregate limits in relation to capital
levels.

Cash Secured Credit Card Lending
While cash secured credit card lending may be less susceptible to
credit risk than other types of credit card lending, credit risk is not
eliminated. The outstanding balance on an account could exceed the
collateral amount either due to the account being only partially
collateralized at account set-up or due to allowing the cardholder to go
over-limit. Partially secured cards represent unsecured credit to
higher-risk consumers to the extent that the line or balance exceeds the
deposit amount. Underwriting for these types of accounts (as well as
for those fully secured) should clearly substantiate the consumer's
willingness and ability to service the debt.

Examiners should verify whether management has established clear
underwriting policies and practices for cash secured lending. These
polices should include, among other items, guidelines for credit limit
assignments in relation to the amount of collateral required. Examiners
should also determine management's practices for performing credit
analysis on the applicant, which may include verifying the applicant's
income, and for ensuring that a perfected security interestin
the deposit is established and maintained. If the bank retains
possession of the deposit, its security interest in the deposit is
generally perfected.

Purchased Portfolios
Similar to expectations for partnership agreements (that is,
co-branded and similar programs), examiners should expect that the bank
refrain from materially modifying underwriting standards when it
purchases portfolios of credit card receivables. If underwriting
criteria are eased in comparison to the banks' internally-established
underwriting criteria it could result in elevated credit risk that
management would need to take appropriate action for, which may include
holding higher levels of loss allowances, hiring additional collectors,
and so forth. And, if the cardholder base is significantly different
than that normally held by the bank, management could be at risk of not
fully understanding the expectations of those cardholders, thereby
raising reputation risk. Examiners should confirm whether management
considers underwriting criteria used by originators in its due diligence
processes for portfolio purchases. If underwriting criteria for
purchased portfolios diverge from the bank's typical underwriting
standards, examiners need to determine the appropriateness of the
differences in relation to management's capabilities and to the overall
impact on portfolio quality and the bank's risk profile. Purchased
credit card portfolios are discussed in the Purchased Portfolios and
Relationships chapter.

Comparison of Automated and Judgmental Processes
Once a consumer completes an application, the application either is
processed through an automated processing system or is processed
manually, or judgmentally. Regardless of the type of process used, the
audit department should examine it with any deviations communicated
promptly to management.

Automated underwriting and loan approval processes are
increasingly popular and vary greatly in complexity. In an automated
system, credit is generally granted based on the cut-off score and the
desired loss rate. These systems are often based on statistical models
and apply automated decision-making where possible. Banks sometimes
establish auto-decline or auto-approve ranges where the system either
automatically approves or declines the applicant based on established
criteria, such as scores. The automated systems may also incorporate
criteria other than scores (such as rules or overlays) into the credit
decision. For example, the presence of certain credit bureau attributes
(such as bankruptcy) outside of the credit score itself could be a
contributing factor in the decision-making process. Examiners should
gauge management's practices for validating the scoring system and other
set parameters within automated systems as well as for verifying the
accurateness of data entry for those systems.

Judgmental underwriting processes also vary in complexity but are
not as popular as they have been in the past, mainly due to advances
made in automated underwriting processes. While not as popular,
judgmental processes are preferred and/or necessary in some cases, such
as if the bank cannot (or does not want to) pay the amount necessary to
establish and maintain such systems or if the portfolio is very small
and perhaps consists of the bank's traditional customers. In a
judgmental process, credit is granted based on a manual review using the
bank's underwriting guidelines which guide the quality of new accounts.
The bank's control systems for ensuring that analysts consistently
follow policy should undergo review during the examination.

When an applicant is approved or denied contrary to a system's recommendations or guidelines, it is usually called an override.
For example, if the applicant falls outside of the auto-approval range,
the applicant may be referred for manual review in certain cases. As
such, the applicant may be approved despite not meeting the system's
criteria, which is called a low-side override.
And, in other cases, applicants that would be auto-approved might be
referred for manual review and declined based on rules or other
guidelines that management has established or authorized, which is
referred to as a high-side override. High-side overrides generally occur when derogatory information becomes known to management.

The following types of overrides are commonly encountered:

Informational overrides occur when information not included in the scoring process becomes known to management.
Policy overrides occur when management establishes special rules for certain types of applications.
Intuitional overrides occur when management makes decisions based on judgment rather than the scoring model.

Scoring is a predominant feature of most automated underwriting
and loan approval processes. When scoring is used to grant credit,
quality is controlled by setting the cut-off score at the desired loss
rate. Credit scoring is discussed in the Scoring and Modeling chapter.

Credit Bureau Preferences
Information in a consumer's credit file is not necessarily identical
across all credit bureaus. Banks often maintain a table reflecting
preferences for certain credit bureaus to be used in the underwriting
(and account management) process. The table is usually based on the
geographic locations targeted. Management's periodic analysis of bureau
preference to determine optimal credit bureaus for different states or
localities should be subject to examination review. Optimal credit
bureaus are generally described as giving the most comprehensive,
accurate, relevant, and timely information on the consumer such that the
bank can make the most informed credit and pricing decisions possible
based on available information.

Post-Screening
Post screening is a supplementary risk management tool. Sound
pre-screening criteria is a first-line of defense against taking on
undesirable accounts, and post-screening will not correct poor selection
criteria. Nevertheless, it can effectively reduce the exposure from
undesirable accounts. Post-screening is a process used in conjunction
with pre-screened solicitations to identify potentially bad, versus
good, accounts. New credit reports are obtained for respondents after
the consumer accepts the pre-screened offer and are reviewed for
negative information established after the pre-screened list was created
or missed in the initial screening.

The FCRAsignificantly limits an institution's ability
to deny credit once an offer has been accepted. Nevertheless, in some
situations, management may be able to take action to reduce risk to the
bank. A pre-screened credit offer may be withdrawn in certain
situations. Bankruptcy, foreclosures, judgments, attachments, and
similar items may be grounds for withdrawing an offer if such items
occurred between the prescreen and the consumer's acceptance ONLY if
these criteria were part of the original prescreening. Identifying and
rejecting these potentially bad accounts reduces the bank's exposure to
loss. In addition, an institution is not required to grant credit if the
consumer is not creditworthy or cannot furnish required collateral,
provided that the underwriting criteria are determined in advance and
applied consistently. If the consumer no longer meets the lender's
predetermined criteria, the lender is not required to issue the credit
card. For example, if the cut-off score in the predetermined criteria
is 700 and the consumer's credit score has deteriorated to 695 at the
time of post-screen, the institution would most likely not be required
to issue the credit card. However, if the consumer's score fell from
780 to 704, the institution would still have to grant credit because the
consumer met the pre-determined standard. Depending on the specifics of
the offer, the bank might be able to reduce the size of the line
extended, provided that any relationship between the credit score and
the amount of credit line given is also pre-determined by the
institution before the offer was made.

FACT Act
In addition to marketing considerations, certain FACT Act provisions
are applicable to the underwriting and origination process. Section 112
of the FACT Act addresses fraud alerts and active duty status alerts.
According to the provisions, prospective users of a consumer credit
report that reflects fraud alerts or active duty alerts generally may
not establish certain new credit plans or extensions of credit in the
name of the consumer unless certain criteria are met, including
specified verification or contact procedures. Credit cannot be denied
based on the existence of a fraud alert or active duty alert. Rather,
the bank must use the specified methods to verify the identity of
consumers with such alerts on their records. In addition, FACT Act
provisions provide that certain entities that make or arrange certain
mortgage loans secured by 1-4 family properties for consumer purposes
using credit scores must provide the score and a standardized disclosure
to the applicants. Examiners should familiarize themselves with FACT
Act provisions and consult with their compliance counterparts if they
run across concerns.

Multiple Accounts
Without proper controls, multiple account strategies can rapidly and
significantly increase the bank's risk profile. The elevated risk
profile may come in many forms, such as excessive credit risk or
elevated reputation risk. Ill-managed multiple account strategies can
exacerbate portfolio deterioration trends.

Management's practices for considering the bank's entire
relationship with an applicant, including, but not limited to, any other
existing card accounts, should be incorporated into the examination
review. The bank's system to aggregate related credit exposures should
also undergo review. In extreme cases, some banks have granted
additional accounts to borrowers who were already experiencing payment
problems on their existing accounts. Examiners should expect management
to carefully consider and document its decision to offer multiple
accounts, especially when the products offered are accompanied by
substantial fees that limit credit availability and card utility. A best
practice for management is to identify why use of a multiple account
strategy was selected as compared to use of a line increase program. For
banks that offer multiple credit lines, examiners should see evidence
that management has established sufficient reporting to show items such
as count, balance, and performance of cardholders that hold more than
one account. They should also determine whether management compares the
performance of multiple account portfolios against the performance of
portfolios where each cardholder maintains only one account. Regulators
can and have required banks to discontinue multiple account strategies
when management has not provided for these necessary and appropriate
management tools and reporting. If multiple account strategies are not
offered, examiners should evaluate how management prevents multiple
accounts from being issued.

Initial Credit Line Assignments
With the profitability potential that credit cards typically exude,
issuers usually try to assign the highest credit lines possible. But,
the potential rewards must be balanced with the risks for the programs
to be effective. Thus, it is critical that initial credit line
assignments are based on sound credit information. Inadequate analysis
of repayment capacity usually results in consumers receiving higher
credit lines than they may be able to service and the risk of default
heightening.

Criteria for line assignments varies but is often based on a
combination of credit bureau score, income level, and/or other criteria.
In any case, the credit lines assigned should be commensurate with the
consumer's creditworthiness and ability to repay in accordance with
soundly-established terms, including emphasis on a reasonable
amortization period. As discussed in the Marketing and Acquisition
chapter, some banks assign the credit line up front and disclose the
line to the consumer as part of the pre-approved offer while other banks
assign the credit line on the back end, such as by offering the
consumer a credit limit up to a maximum amount in the solicitation. For
back-end credit line assignment, the amount of the credit line is not
assigned until the consumer responds to the solicitation.

As discussed in that chapter, compliance, credit, reputation, and
other risks may arise depending on credit availability and card utility
at account opening. Banks that offer products with limited credit
availability or card utility at account opening are expected to maintain
careful and thorough analysis demonstrating that the product will be
and is marketed and underwritten in such a way to fully address the
various accompanying safety and soundness and consumer protection
concerns raised by such products.

Policy and Underwriting Exceptions
Policy and underwriting exceptions
are conditions in approved loans that contravene the bank's lending
policies or underwriting guidelines. In an automated approval
environment, policy exceptions should be rare. However, if the
underwriting process includes a judgmental element, overrides are more
likely to occur. Examiners should look for evidence that management has
provided guidelines and limitations for granting loans that do not
conform to the lending policy and underwriting guidelines and that it
has established procedures for tracking and monitoring loans approved as
exceptions.

Tracking exceptions is a valuable tool for several reasons. In
addition to aiding the assessment of portfolio risk profiles and the
adequacy of loss allowances, it helps hold staff accountable for policy
compliance and reassess the appropriateness of existing policies and
practices.

Exceptions are tracked both on an individual and aggregate basis.
Tracking the aggregate level of exceptions is common and helps detect
shifts in the risk characteristics of the credit card portfolio. It
facilitates risk evaluation and helps management identify new business
and training opportunities. Analysis of aggregate exceptions eventually
enables management to correlate particular types of exceptions with a
higher probability of default. Policy and underwriting exceptions that
are viewed individually might not appear to substantially increase risk.
But, when aggregated, those same exceptions can considerably increase
portfolio risk. As such, early detection and analysis of adverse trends
in exceptions is a necessary element for ensuring timely and appropriate
corrective action.

An excessive or increasing volume or a pattern of exceptions could
signal unintended or unwarranted relaxation in underwriting practices.
If the volume of exceptions is high, management may be prompted to
reconsider its risk tolerance, revise policies to be better aligned with
the credit culture or current market conditions, establish new limits
on the volume of exceptions, or change the type of exceptions permitted.
When management has revised policies in response to high volumes of
exceptions, examiners should assess the implications of the revisions,
including impacts on the bank's risk profile.

While high volumes of exceptions may indicate increased risk, so
can a lack of exceptions. A lack of exceptions may indicate that the
policy is too general to set clear limits on underwriting risk. If
examiners identify an absence of exceptions, they should carefully
review the bank's policies to ascertain whether such policies provide
adequate and clear guidance and limits.

Examiners should gauge the sufficiency of portfolio managers'
procedures for comparing the performance of exception loans with that of
loans made within established guidelines. To facilitate comparison,
management often uses exception coding and retains it even if the
condition triggering the coding no longer exists. Examiners should
review management's practices for dropping exception codes or re-coding
and should identify whether the practices skew or spoil the
effectiveness of exception tracking.

Indices and Reporting
A variety of indices are available regarding the underwriting
function and its relationship with the marketing function. Items of
interest include, but are not limited to:

Origination cost per account, which is the total origination
cost over a measurement period in relation to the number of accounts
that were originated during that same period. It measures the cost of
establishing a new account relationship.
Approval rate, which is the number of accounts approved over a
measurement period in relation to the number of applications (or
responses) received.
Booking rate, which is the number of accounts actually booked
over a measurement period in relation to either the number of approved
accounts or the number of applications (or responses) received. In some
instances the customer applies for credit but then declines the offer
after approved.
Override rate, which is the number of overrides over a
measurement period in relation to the number of applicants in the
population.
High-side override rate, which is the number of applicants over
the cut-off score who were denied credit in relation to the number of
applicants over the cut-off score.
Low-side override rate, which is the number of applicants below
the cut-off score who were given credit in relation to the number of
applicants below the cut-off score.
Application processing time, which is the amount of time it
takes the institution to process the application from the time of
receipt to the point the credit decision is made and communicated to the
consumer.

Portfolio problems can frequently be traced back to the bank's
business generation and underwriting practices. Management is expected
to devote sufficient resources to analyze changes in underwriting and
credit scores, use appropriate systems and analytical tools to examine
the results, and monitor warning signs of market deterioration. Common
reports found in the underwriting department include, but are not
limited to, those detailing policy changes, average credit score for new
accounts, average initial credit lines assigned, approval rates,
booking rates, and costs associated with the marketing and underwriting
functions. Examiners should also determine whether management is
monitoring reports as detailed in the Multiple Account Strategies and
Policy and Underwriting Exceptions sections of this chapter. They should
also look for evidence that management is using appropriate and
sufficient segmentation techniques (program type, vintage, marketing
channel, score distribution, etc.) within its reporting and is
frequently monitoring marketing reports, usually no less than monthly.

Summary of Examination Goals – Underwriting and Loan Approval Process
Review of the underwriting and loan approval process is important
because the goal of the examination is not only to identify current
portfolio problems, but to identify potential problems that may arise
from ineffective policies, unfavorable trends, lending concentrations,
or non-adherence to policies. Examiners normally review items such as:

The structure of the underwriting department and the expertise of its staff.
Applicable board and/or committee minutes (in coordination with the examiner-in-charge).
Underwriting policies and procedures.
Underwriting chronology logs or similar documents summarizing changes in the underwriting and loan approval process.
Planned underwriting and loan approval changes.
Management reporting, tracking, and monitoring, including
department statistics, portfolio statistics, and other segmentation
statistics.
Automated underwriting systems.
Controls over judgmental underwriting processes.
Management's identification of and response to adverse trends in the underwriting and loan approval area.
Audits or other reviews of the underwriting and loan approval function.

The following items might signal current or future elevated risk and, thus, might warrant follow-up:

Excessive or rapidly rising approval rates.
Frequent or substantial changes in underwriting criteria.
High employee turnover in the department.
High or increasing exception volumes.
Extremely low or non-existent exception volumes.
High or increasing volume of accounts closed shortly after booking.
Adverse performance of multiple account holders compared to cardholders with only one account.
Few or ineffective management reports.
Trends in the credit score distribution toward higher-risk accounts.
High or increasing volume of consumer complaints.
Credit lines inconsistent with products offered or with the target market's risk profile.
Trends showing marked changes in average assigned lines.

These lists are not exhaustive, and examiners must exercise
discretion in determining the expanse and depth of examination
procedures to apply. If examiners identify significant concerns, they
should expand procedures accordingly.

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