The last thing a modeler wants to do is deliver a very predictive model that can’t be implemented. There are several issues that might eliminate a variable from use:
- Unavailability – A variable might be very predictive, but it may not be available in the implementation environment. This is often an issue when the scoring model is executed in real time, as in instant approvals for credit or insurance. Sometimes, this is a situation where the data exists, but the means to deliver it do not exist or are limited. An example of this might be a specialized credit bureau variable (such as an indicator of transactor or revolver of credit cards) that is available in batch mode, but not in real time feeds.
- Expense – Some data can be predictive, but its predictive value may not be able to justify the cost. Modelers should always know not only that a variable is available for implementation, but also if there is an additional cost involved.
- Legal – Many variables are expressly prohibited in certain types of models that fall under either state or federal governance. For example, any variable that is indicative of gender or ethnicity. Also, many variables could be used to discriminate other protected classes based on the coefficient of the variable in the model.
- Customer Service – Many models
are legally required to generate “reason codes.” These codes are
intended to help the consumer understand why they were either
rejected or not given the best offer. These codes are supposed to be
related to the variables most responsible for the decision. If these
codes are confusing or counterintuitive then customer service may
have a difficult time explaining them to consumers who call in
asking for clarification.
For example, if a model coefficient for “Time on Books” indicates that higher values on this variable increases risk and then the consumer reason code could state something like
“too much credit history with this company.”
This could well generate a lot of consumer calls asking not only to explain this, but encouraging them to cancel their account since it implies that is one way to increase their credit rating.
Previewing predictor variables for acceptability is always best practice. Certainly eliminating variables that are obviously problematic is good practice, but identifying potential issues before presenting a final model is prudent.