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To browse Academia. Skip to main content. By using our site, you agree to our collection of information through the use of cookies. To learn more, view our Privacy Policy. Log In Sign Up. Written by an experienced credit scoring practitioner, it provides a unique and comprehensive insight into the scorecard development process. All rights reserved. Published simultaneously in Canada. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section or of the United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc.

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Some content that appears in print may not be available in electronic books. Includes bibliographical references and index. Credit scoring systems. Risk management. S53 I would like to express my gratitude to a few people who have supported me in this endeavor.

I want to thank my family—Saleha, Zainab, and Noor—for tolerat- ing my frequent absences from home, and the hours spent in my office working on the book. Finally I want to acknowledge my parents for encouraging me to seek knowledge, and for their constant prayers and blessings, without which there would be no success. Aggressive marketing efforts have resulted in deeper penetration of the risk pool of potential customers, and the need to process them rapidly and effectively has led to growing automation of the credit and insurance application and adjudication processes.

The Risk Manager is now challenged to produce risk adjudication solutions that can not only satisfactorily assess creditworthiness, but also keep the per-unit processing cost low, while reducing turnaround times for cus- tomers. In addition, customer service excellence demands that this auto- mated process be able to minimize denial of credit to creditworthy customers, while keeping out as many potentially delinquent ones as possible.

In the insurance sector, the ability to keep the prices of policies commensurate with claims risk becomes more critical as underwriting losses increase across the industry. At the customer management level, companies are striving ever harder to keep their existing clients by offering them additional prod- ucts and enhanced services. Conversely, for customers who exhibit negative behavior non- payment, fraud , Risk Managers need to devise strategies to not only identify them, but also deal with them effectively to minimize further loss and recoup any monies owed, as quickly as possible.

Risk scorecards have been used by a variety of industries for uses including predicting delinquency nonpayment—that is, bankruptcy—fraud, claims for insurance , and recovery of amounts owed for accounts in collections. Scoring method- ology offers an objective way to assess risk, and also a consistent approach, provided that system overrides are kept to a minimum. In the past, financial institutions acquired credit risk scorecards from a handful of credit risk vendors. This involved the financial institution providing their data to the vendors, and the vendors then developing a predictive scorecard for delivery.

While some advanced companies have had internal modeling and scorecard development functions for a long time, the trend toward developing scorecards in-house has become far more widespread in the last few years. This happened for various reasons. First, application software became available that allowed users to develop scorecards without investing heavily in advanced programmers and infrastructure.

Complex data mining functions became available at the click of a mouse, allowing the user to spend more time applying business and data mining expertise to the problem, rather than debug- ging complicated programs. Second, advances in intelligent and easy to access data storage have removed much of the burden of gathering the required data and putting it into a form that is amenable to analysis.

Once the tools became available, in-house development became a viable option for many smaller and medium-sized institutions. The industry could now realize the significant Return on Investment ROI that in-house scorecard development could deliver for the right players.

Experience has shown that in-house credit scorecard develop- ment can be done faster, cheaper, and with far more flexibility than before. Development was cheaper, since the cost of maintaining an in- house credit scoring capability was less than the cost of purchased scorecards.

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.

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. 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. 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.

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?

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.

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. 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.

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. 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. Examples of such characteristics are demographics e. The total score of an applicant is the sum of the scores for each attribute present in the scorecard for that applicant. Exhibit 1. That is, 1. Based on factors outlined above, a company can then decide, for example, to decline all applicants who score below , or to charge them higher pricing in view of the greater risk they present. For example, an applicant scoring very high or very low can be declined or approved outright without obtaining further information on real estate, income verification, or valuation of underlying security.

The previous examples specifically dealt with risk scoring at the appli- cation stage. Risk scoring is similarly used with existing clients on an ongoing basis.

Based on similar business considerations as previously mentioned e. Custom scorecards are those developed using data for customers of one organi- zation exclusively. For example, ABC Bank uses the performance data of its own customers to build a scorecard to predict bankruptcy. It may use internal data or data obtained from a credit bureau for this purpose, but the data is only for its own customers. Generic or pooled data scorecards are those built using data from multiple lenders.

For example, four small banks, none of which has enough data to build its own custom scorecards, decide to pool their data for auto loans. They then build a scorecard with this data and share it, or customize the scorecards based on unique characteristics of their portfolios.

Scorecards built using industry bureau data, and marketed by credit bureaus, are a type of generic scorecards. This can be done in branches, credit adjudication centers, and collections departments. Combined with business knowledge, predictive modeling technologies provide risk managers with added efficiency and control over the risk management process. In the future, credit scoring is expected to play an enhanced role in large banking organizations, due to the requirements of the new Basel Capital Accord Basel II.

This will also lead to a reevaluation of methodologies and strategy development for scorecards, based on the recommendations of the final accord.

Regulation B, Section This not only creates better scorecards, it ensures that the solutions are consistent with business direction, and enables education and knowledge transfer during the development process. The level of involvement of staff members varies, and different staff mem- bers are required at various key stages of the process. By understanding the types of resources required for a successful scorecard development and implementation project, one will also start to appreciate the busi- ness and operational considerations that go into such projects.

Scorecard Development Roles At a minimum, the following main participants are required: Scorecard Developer The Scorecard Developer is the person who performs the statistical analyses needed to develop scorecards.

Risk Managers may also be able to use some of their experience to point Scorecard Developers in a particular direction, or to give special consideration to certain data elements. Experienced Risk Managers are also aware of historical changes in the market, and will be able to adjust expected performance numbers if required.

Scorecards are developed to help in decision making—and anticipating change is key. They also coordinate design of new application forms where new information is to be collected. This approach produces the best results for the most valued segments and harmonizes marketing and risk directions. Operational Manager s The Operational Manager is responsible for the management of depart- ments such as Collections, Application Processing, Adjudication when separate from Risk Management , and Claims.

Staff from Adjudication, Collections, and Fraud departments can also offer experience-based insight into factors that are predictive of negative behavior, which helps greatly when selecting characteristics for analysis. Application scorecards are usually developed on data that may be two years old, and collections staff may be able to identify any trends or changes that need to be incorporated into analyses.

This exercise also provides an opportunity to test and validate experience within the organization. The same can be done with adju- dication staff credit analysts. This would be especially helpful for those developing scorecards for the first time.


Credit Risk Scorecards : Developing and Implementing Intelligent Credit Scoring

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. Chapter 1: Introduction. Scorecards: General Overview. Scorecard Development Roles. Intelligent Scorecard Development. Scorecard Development and Implementation Process: Overview.

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Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring

To browse Academia. Skip to main content. By using our site, you agree to our collection of information through the use of cookies. To learn more, view our Privacy Policy. Log In Sign Up. Written by an experienced credit scoring practitioner, it provides a unique and comprehensive insight into the scorecard development process. All rights reserved.


You are currently using the site but have requested a page in the site. Would you like to change to the site? Naeem Siddiqi. Credit Risk Scorecards provides insight into professional practices in different stages of credit scorecard development, such as model building, validation, and implementation.

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