Build a Scorecard in Oracle Using Logistic Regression Model

One of the popular methods in assessing credit risk is a scorecard. To build a scorecard manually, analysts first identify a number of relevant variables, e.g. having bankruptcies in the past, number of open accounts, etc. Each variable is divided into meaningful segments based on its value. Then a point is assigned to each segment. The credit score is the sum of the points for all the variables. For example, a scorecard may look like the following:

Variable | Segment | Point
Having Bankruptcy | NO | 95
Having Bankruptcy | YES | 15
Number of Open Accounts | from 1 to 3 | 30
Number of Open Accounts | from 4 to 8 | 50
Number of Open Accounts | 9 or more | 40
...................................................

Assume a person's credit history shows past bankruptcy, 4 open accounts and other information. His credit score will be calculated as 15+50.....

To determine the points manually is subjective. A much better way is to build a logistic regression model and use the model coefficients as the "points". A scorecard created this way is accurate and yet still provides the transparency of a scorecard. One of the top cell phone service provider actually used our model-derived scorecard to screen the new customers.

In example 2 of the post Build Predictive Models Using PL/SQL, we show how to build a logistic regression model in Oracle. The extract the model coefficients, we simply use the following query:

select attribute_name, attribute_value, coefficient from table(select dbms_data_mining.get_model_details_glm('GLM_MODEL') from dual);
(Replacing 'GLM_MODEL' with real model name)

We may want to perform some transformation of the coefficients so that the score will be within the desired range.