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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 methodology 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 debugging complicated programs. The availability of powerful "point and click"-based Extract-Transform-Load ETL software enabled efficient extraction and preparation of data for scorecard development and other data mining.
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 development can be done faster, cheaper, and with far more flexibility than before. Development was cheaper, since the cost of maintaining an inhouse credit scoring capability was less than the cost of purchased scorecards. Internal development capability also allowed companies to develop far more scorecards with enhanced segmentation for the same expenditure.
Scorecards could also be developed faster by internal 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 delinquent 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, repossession, 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 flexibility 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 information that allow for better understanding of customers' risk behavior, and lead to better strategy development.
In summary, leaving key modeling and sampling decisions to "external experts" can prove to be a suboptimal route at best, and can also be quite costly. There was also fallout with customers who were initially turned down after 20 years of doing business 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 information available. They would not make a decision using four or five pieces of information only-so why should anyone build a scorecard that is narrow-based? Each decision made-whether on the definition of the target variable, segmentation, choice of variables, transformations, choice of cutoffs, or other strategies-starts a chain of events that impacts other areas of the company, as well as future performance.
By tapping into corporate intelligence, and working in collaboration with others, the user will learn to anticipate the impact of each decision and prepare accordingly to minimize disruption and unpleasant surprises.
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 complex 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 noncreditworthy applicants at a statistically significant rate.
Scorecards: General OverviewRisk 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 statistical odds, or probability, that an applicant with any given score will be "good" or "bad.
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. Each attribute "Age" is a characteristic and "" is an attribute is assigned points based on statistical analyses, taking into consideration various factors such as the predictive strength of the characteristics, correlation between characteristics, and operational factors.
The total score of an applicant is the sum of the scores for each attribute present in the scorecard for that applicant. Exhibit 1. The circled line in the exhibit tells us the following: to develop new application strategies that will maximize revenue and minimize bad debt. Application scores can also help in setting "due diligence" policies.
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 application stage.
Risk scoring is similarly used with existing clients on an ongoing basis. In this context, the client's behavioral data with the company is used to predict the probability of negative behavior. Based on similar business considerations as previously mentioned e. Custom scorecards are those developed using data for customers of one organization 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.
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.
In particular, changes may be required in the way "bad" is defined, and in the way the target prediction is connected to "Probability of Default," "Exposure at Default," and "Loss Given Default. 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.
Scorecard development is not a "black box" process, and should not be treated as such. Experience has shown that developing scorecards in isolation can lead to problems such as inclusion of characteristics that are no longer collected, legally suspect, or difficult to collect operationally, and devising of strategies that result in "surprises" or are unimplementable.
The level of involvement of staff members varies, and different staff members 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 business and operational considerations that go into such projects.
Scorecard Development RolesAt a minimum, the following main participants are required: Scorecard DeveloperThe Scorecard Developer is the person who performs the statistical analyses needed to develop scorecards.
He or she would also contribute heavily to the development of strategies and to gauging possible impacts of those strategies on customers and the various areas of the organization. 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. Segmentation offers the opportunity to assess risk for increasingly specific populations-the involvement of marketing in this effort can ensure that scorecard segmentation is in line with the organization's intended target markets.
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 departments such as Collections, Application Processing, Adjudication when separate from Risk Management , and Claims.
Any strategy developed scorecard development roles using scorecards, such as changes to cutoff levels, will impact these departments. 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.
A good question to ask is, "What characteristics do you see in bad accounts, and have they changed over the last few years? 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 adjudication staff credit analysts. This would be especially helpful for those developing scorecards for the first time. These interviews will enable them to tap into existing experience to identify generally predictive characteristics. Project ManagerThe Project Manager is responsible for the overall management of the project, including creation of the project plan and timelines, integration of the development and implementation processes, and management of other project resources.
They sometimes have added responsibilities for corporate data warehouses. They must be notified of changes to maintain timelines for implementation. In particular, where scorecards are being developed using complex transformations or calculations, and they need to be implemented on real-time software, the IT department may be able to advise if these calculations are beyond the capabilities of the software.
Siddiqi N. 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 of business intelligence to the scorecard development 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 of building a risk profile—building scorecards that contain predictive variables representing major information categories.
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. Siddiqi Published Engineering. Chapter 1: Introduction.
Wiley Online Library Probekapitel. Intelligent Credit Scoring presents a business-oriented process for the development and implementation of risk prediction scorecards.
Sign in. That all-important number that has been around since the s and determines our creditworthiness. I suppose we all also have a basic intuition of how a credit score is calculated, or which factors affect it. Refer to my previous article for some further details on what a credit score is.
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Praise for Credit Risk Scorecards Scorecard development is important to Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring.