信贷风险评分卡：智能信用评分的开发与实施

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"Credit Risk Scorecards：Developing and Implementing Intelligent Credit Scoring" 信用评分卡是一种在金融行业中广泛应用的风险管理工具，特别是在信贷决策过程中。这本书由SAS公司的信用评分专家Naeem Siddiqi撰写，是关于信用风险评估的权威指南。它不仅适合初学者作为入门教材，也对有经验的专业人士提供了深入的实践见解。信用评分模型的开发流程是书中的核心内容，涵盖了从数据收集、预处理、特征选择、模型构建、验证到实施的全过程。这个生命周期对于理解信贷风险管理至关重要，因为它确保了模型的准确性和可靠性。以下是模型开发的一些关键步骤： 1. 数据准备：首先，需要收集包含客户信用历史、财务状况和个人信息的数据。数据质量是建模的基础，因此数据清洗和缺失值处理是必不可少的。 2. 特征工程：识别能够预测违约概率的变量，如收入、负债、信用历史等，并进行适当的转换，如二值化、标准化或归一化。 3. 模型选择：常见的信用评分模型包括逻辑回归、决策树、随机森林和机器学习算法（如支持向量机、神经网络）。每种方法都有其优缺点，需要根据具体业务需求和数据特性选择合适的方法。 4. 模型训练与优化：通过划分训练集和测试集，使用交叉验证等方法训练模型并调整参数，以提高预测性能。 5. 验证与评估：模型的性能通常通过AUC-ROC曲线、准确率、召回率、F1分数等指标进行评估。同时，还需要进行模型稳定性测试，以确保模型在未来数据上的表现。 6. 模型部署：在满足监管要求和内部审批后，将模型嵌入信贷审批系统，用于自动化决策过程。 7. 持续监控与更新：随着时间推移，市场环境和客户行为可能发生变化，因此模型需要定期回顾和更新，以保持其预测能力。本书还强调了在实际操作中，信用风险评分卡的开发不仅要考虑技术层面，还需关注法规遵从性、模型解释性以及业务流程的整合。Naeem Siddiqi的实践经验使读者能更深入地理解如何在现实世界中有效地运用这些理论知识。总结起来，《Credit Risk Scorecards：Developing and Implementing Intelligent Credit Scoring》是理解信用评分模型开发和实施的宝贵资源，它将理论知识与实践经验相结合，对于提升金融机构的风险管理水平具有重要意义。

same expenditure. Scorecards could also be developed faster by inter-

nal resources using the right software—which meant that custom

scorecards could be implemented faster, leading to lower losses.

In addition, companies realized that their superior knowledge of

internal data and business insights led them to develop better-performing

scorecards. Defining the population performance definitions is a critical

part of scoring system construction, and the ability to vary definitions for

different purposes is key. For example, a probability of default score

designed for capital planning purposes may exclude moderately delin-

quent accounts (60 days past due twice during the past 24 months) that

are normally included in “bad behavior” and go by the Basel definition

for loans considered likely to default (associated with write-off, reposses-

sion, foreclosure, judgments, or bankruptcy). This will vary by type of

loan or trade line—for example, revolving, installment, mortgage, and so

forth. On sample construction, some Scorecard Developers eliminate

large numbers of accounts associated with inactivity, indeterminate

behavior, and so forth, and this is another area where some empirical

investigation and control is warranted.

Better-performing scorecards also came about from having the flexi-

bility to experiment with segmentation, and from following through by

developing the optimum number and configuration of scorecards.

Internal scorecard development also increases the knowledge base

within organizations. The analyses done reveal hidden treasures of infor-

mation that allow for better understanding of customers’ risk behavior,

and lead to better strategy development.

In summary, leaving key modeling and sampling decisions to “exter-

nal experts” can prove to be a suboptimal route at best, and can also be

quite costly. A perfect example that comes to mind is a finance com-

pany that outsourced scorecard development and found upon system

implementation that the “updated scorecards” turned down 65% of

their current and repeat customers, even though they developed specific

individual scorecards for present versus former borrowers. Ultimately,

the problem was traced back to the good/bad performance definitions

and the fact that their average “good” paying customer had delinquency

characteristics that would normally be categorized as bad behavior, or

indeterminate at the very least! Unfortunately, there were five regional

introduction 3

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scorecards for each of the two groups, so that ultimately ten scorecards

were shelved at an average cost of $27,000. There was also fallout with

customers who were initially turned down after 20 years of doing busi-

ness with the company.

This book presents a business-focused process for the development

and implementation of risk prediction scorecards, one that builds upon

a solid foundation of statistics and data mining principles. Statistical and

data mining techniques and methodologies have been discussed in detail

in various publications, and will not be covered in depth here. The key

concepts that will be covered are:

•The application ofbusiness intelligence to the scorecard devel-

opment process, so that the development and implementation of

scorecards is seen as an intelligent business solution to a business

problem. Good scorecards are not built by passing data solely

through a series of programs or algorithms—they are built when

the data is passed through the analytical and business-trained

mind of the user.

•Collaborative scorecard development, in which end users, subject

matter experts, implementers, modelers, and other stakeholders

work in a cohesive and coherent manner to get better results.

•The concept ofbuilding a risk profile—building scorecards that

contain predictive variables representing major information cate-

gories. This mimics the thought processes of good risk adjudicators,

who analyze information from credit applications, or customer

behavior, and create a profile based on the different types of infor-

mation available. They would not make a decision using four or five

pieces of information only—so why should anyone build a score-

card that is narrow-based?

•Anticipating impacts ofdecisions and preparing for them. Each

decision made—whether on the definition of the target variable,

segmentation, choice of variables, transformations, choice of cut-

offs, or other strategies—starts a chain of events that impacts other

areas of the company, as well as future performance. By tapping

4 introduction

c01.qxd 9/6/05 12:20 PM Page 4

into corporate intelligence, and working in collaboration with

others, the user will learn to anticipate the impact of each deci-

sion and prepare accordingly to minimize disruption and unpleas-

ant surprises.

•View ofscorecards as decision support tools. Scorecards should be

viewed as a tool to be used for better decision making, and should

be created with this view. This means they must be understood

and controlled; scorecard development should not result in a com-

plex model that cannot be understood enough to make decisions

or perform diagnostics.

Individual scorecard development projects may need to be dealt with

differently, depending on each company’s unique situation. This

methodology should therefore be viewed as a set of guidelines rather

than as a set of definitive rules that must be followed. Finally, it is worth

noting that regulatory compliance plays an important part in ensuring

that scorecards used for granting consumer credit are statistically sound,

empirically derived, and capable of separating creditworthy from non-

creditworthy applicants at a statistically significant rate.

An excellent,

but somewhat dated, article on credit scoring and the Equal Credit

Opportunity Act is cited in the Bibliography (Hsia, 1978).

Scorecards: General Overview

Risk scoring, as with other predictive models, is a tool used to evaluate

the level of risk associated with applicants or customers. While it does

not identify “good” (no negative behavior expected) or “bad” (negative

behavior expected) applications on an individual basis, it provides statis-

tical odds, or probability, that an applicant with any given score will be

“good” or “bad.” These probabilities or scores, along with other busi-

ness considerations such as expected approval rates, profit, churn, and

losses, are then used as a basis for decision making.

In its simplest form, a scorecard consists of a group of characteristics,

statistically determined to be predictive in separating good and bad

accounts. For reference, Exhibit 1.1 shows a part of a scorecard.

scorecards: general overview 5

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