Classification scorecards are a great way to predict things because the techniques used in the banking industry specialize in interpretability, predictive power, and ease of deployment. The banking industry has long used credit scoring to determine credit risk—the likelihood a particular loan will be paid back. A scorecard is a common way of displaying the patterns found in a classification model—typically a logistic regression model. However, to be useful the results of the scorecard must be easy to interpret. The main goal of a credit score and scorecard is to provide a clear and intuitive way of presenting regression model results. This article briefly discusses what scorecard analysis is and how it can be applied to score almost anything.

Scorecards are extremely successful in the consumer credit world because they:

Are very generalizable – anyone in an organization can understand and use them

Are accepted by regulatory agencies as a standard method for presenting credit risk

Are straightforward to implement and monitor over time

Can be quickly programmed and deployed in a mass way

Strict credit industry regulations protect consumers from loan rejections based on uninterpretable “black box” models. There are often also laws against using particular variables, such as race or zip code, in the credit decision. This has driven the banking industry to develop models with results that can be easily interpreted. However, the goal of interpretable model results goes well beyond just banking.

Let’s look at a credit scorecard model example that employs three variables: age, income, and home ownership. Each variable has a value, or level, that contributes scorecard points, as shown in Figure 1. The points are summed together and if they exceed the threshold the applicant is approved for a loan:

Figure 1. Scorecard example

Credit Approval Threshold ≥ 500

Example 1

AGE = 32 => 120 score

OWNERSHIP = OWN => 225 score

INCOME = $30,000 => 180 score

Credit Score = 120+225+180 = 525 => Loan Approved

Example 2

AGE = 22 => 100 score

OWNERSHIP = OWN => 225 score

INCOME = $8,000 => 120 score

Credit Score = 100+225+120 = 445 => Loan Rejected

These simple variables provide clear guidelines for decision makers, making loan approval decisions transparent and easy to interpret and discuss or defend.

Data Scientists often build models where the client must interpret the results or understand the factors driving the model results. In these cases scorecard modeling provides an easy solution. With scorecards, there are no continuous variables – every variable is categorized. This is the most important step. The key to scorecard models is to categorize or “bin” the variables in a way that summarizes as much information as possible, and then build models that eliminate weak variables or those that do not conform to good business logic.

Binning simplifies many analysis issues that are complex for linear models, since:

There is a direct relationship between group membership and points, instead of an indirect relationship between model coefficients and variable values.

Groups can reflect nonlinear relationships; there is no need to worry about linearity assumptions – one of the biggest problems with logistic regression.

Grouping handles outliers well because the outliers can be contained in the smallest or largest group, whereas in linear models the outliers affect the estimates everywhere.

Missing values are easily handled by being assigned to their own group.

As a starting point for binning, select a continuous variable and divide it into groups that are most alike. This can be done with decision trees, Gini statistics, Chi-square tests, random statistical buzzwords, etc. – whatever method you’d like to use to categorize individual continuous variables. Imagine starting with many bins for a continuous variable. Then look to see which bins can be combined statistically. Having a continuous variable assumes that all levels are different when predicting a target variable. Most of the time this is not true. Is there a difference between someone with an income of $38,000 and someone with $39,000? Most likely not, but treating income as a continuous variable makes this assumption. By categorizing we can let the computer decide if there is a statistical difference; if there isn’t, they can be combined in the same category.