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mind of the user. This concept will be applied in all the sections of this book – taking statistical analyses and overlaying business knowledge on it to create better results.

      ● Building scorecards is a business process – as much as we use statistical algorithms, simple or complex, to build models, at the end of the day it is a business exercise. The purpose of the exercise is to enable a better business decision and not merely the creation of a great formula. As such, each process – whether selecting a “bad” definition, deciding appropriate segmentations, best bins for attributes, or the best scorecard – will be viewed through the lens of a business decision.

      ● Collaborative scorecard development, in which end users, subject matter experts, implementers, modelers, validators, decision makers and other stakeholders work in a cohesive and coherent manner to get better results and avoid costly setbacks and potential disasters during the process.

      ● The concept of building a risk profile – this means building scorecards that contain predictive variables representing major information categories, usually between 8 and 15 variables. 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? In statistics, parsimonious models are usually preferred. However, in this case, where the modeler is attempting to more fully capture the business reality, more variables are preferred in order to construct a proper and representative risk profile. The point of the exercise is to make the best decision-making tool possible, not just a statistical one.

      ● Anticipating impacts of decisions and preparing for them. 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.

      ● View of scorecards 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 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 – for example, amount and type of data available, knowledge level, staff, and regulatory limitations. This methodology should therefore be viewed as a set of “best-practice” guidelines rather than as a set of definitive rules that must be followed. Many processes and calculations described in this book can be changed and customized by individual users once they understand what is going on. 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.5 Users should be aware of the regulations that govern models in their jurisdictions, and change the process accordingly.

      Scorecards: General Overview

      Credit risk scoring, as with other predictive models, is a tool used to evaluate the level of credit 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.” These probabilities or scores, along with other business 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.

Exhibit 1.1 Sample Scorecard (Partial)

      Scorecard characteristics may be selected from any of the sources of data available to the lender at the time of the application. Examples of such characteristics are demographics (e.g., age, time at residence, time at job, postal code), existing relationship (e.g., time at bank, number and types of products, payment performance, previous claims), credit bureau (e.g., inquiries, trades, delinquency, public records), real estate data, and so forth. The selection of such variables and creation of scorecards will be covered in later chapters in much more detail.

      Each attribute (“age” is a characteristic and “23–25” 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 1.2 is an example of the gains chart, one of the management reports produced during scorecard development.

      The gains chart, which will be covered in more detail in later chapters, tells us the expected performance of the scorecard. Several things can be observed from this exhibit:

      ● The score bands have been arranged so that there are approximately 10 percent of accounts in each bucket. Some analysts prefer to arrange them in equal score bands.

      ● The marginal bad rate, shown in the column “marginal event rate,” rank orders from a minimum of 0.2 percent to a maximum of about 15.7 percent. There is some variability between the bad rate based on counts and the predicted bad rate from the model (average predicted probability) due to low counts.

      ● For the score range 163 to 172, for example, the expected marginal bad rate is 5.31 percent. This means 5.31 percent of the accounts that score in that range are expected to be bad.

      ● For all accounts above 163, the cumulative bad rate, shown in the column “cumulative event rate,” is 2.45 percent. This would be the total expected bad rate of all applicants above 163.

      ● If we use 163 as a cutoff for an application scorecard, the acceptance will be about 70 percent, meaning 70 percent of all applicants score above 163.

Exhibit 1.2 Gains Chart

      Based on factors outlined above, as well as other decision metrics to be discussed in the chapter on scorecard implementation, a company can then decide, for example, to decline all applicants who score below 163, or to charge them higher pricing in view of the greater risk they present. “Bad” is generally defined using negative performance indicators such as bankruptcy, fraud, delinquency, write-off/charge-off, and negative net present value (NPV).

      Risk score information, combined with other factors such as expected approval rate and revenue/profit potential at each risk level, can be used to develop new application strategies that will maximize revenue and minimize bad debt. Some of the strategies for high-risk applicants are:

      ● Declining credit/services if the risk level is too high.

      ● Assigning a lower starting credit limit on a credit card or line of credit.

      ● Asking the applicant to provide a higher down payment or deposit for mortgages or car loans.

      ● Charging a higher interest rate on a loan.

      ● Charging a higher

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