Credit Score Modeling
Author: Dr. Aric LaBarr
Introduction
Credit scoring is best summed up by this quote from David Edelman who at the time was the Credit Director of the Royal Bank of Scotland:
Credit scoring is “one of the oldest applications of data mining, because it is one of the earliest uses of data to predict consumer behavior.”
Example Scorecard
Let’s examine a simple example of a scorecard to see these benefits. Below is a simple scorecard built off of a three variable logistic regression model trying to predict default on a loan. The three variables are late, which represents the number of cumulative days late over the past year on previous loans, home, which represents whether an applicant owns or rents their home, and cycle number which is the number of previous loans given to the applicant.
Imagine we had an applicant who had last had 13 late days of payments over the past year, who owned their home, and had 2 previous loans. They would have a score of 590 (185 + 225 + 180). Let’s assume our cut-off for giving a loan was a score of 500. If this was the case, the applicant would be given the loan. Now imagine we had another applicant who had 33 days late over the past year, owned their home, but never had a loan previously from us. They would have a score of 445 (100 + 225 + 120). They would not be given a loan.
Credit Scorecards
Credit scoring is a statistical model that assigns a risk value to prospective or existing credit accounts. Typically, we think of credit scoring in the context of loans. We are trying to determine the likelihood of an individual to default on a loan.
Scorecards are a common way of displaying the patterns found in a binary response model. Typically, the models that underlie scorecards are logistic regression models. The main benefits of scorecards are their clear and intuitive way of presenting the regression coefficients from a model. These scorecards are typically thought of in a credit modeling framework, but are not limited there as they are used in fraud detection, healthcare, and marketing fields as well.
Credit scorecards, much like your FICO score, are a statistical risk model that was put into a special format designed for ease of interpretation. These are used to make strategic decisions such as accepting/rejecting applicants and deciding when to raise a credit line, as well as other decisions. The credit scorecard format is very popular and successful in the consumer credit world for three primary reasons:
People at all levels within an organization generally find it easy to understand and use.
Regulatory agencies are accustomed to credit risk models presented in this fashion.
Credit scorecards are straightforward to implement and monitor over time.
As seen by the previous example, scorecards are extremely ease for anyone to use and implement in any computing system or database. This way the person making the loan decision has easy cut-offs and variable groupings to bucket an applicant in. They also have the ability to let an applicant know why they were rejected for a loan much easier. From our second individual, they had an income level that was in the lowest point bin for that variable. This is also true for their months since last last payment variable. These are the reasons they were rejected for a loan.
This ease of interpretation protects the consumer as it is their right to ask why they were rejected for a loan. This is why regulators appreciate the format of scorecards so much in the credit world.