How to Build a Credit Scoring Grid From a Logistic Regression Model

A logistic regression model's coefficients can be transformed into a 0-1000 credit score for assessing default risk. This process involves calculating a score for each category of a variable, with a client's final score being the sum of the scores for their specific attributes. The model's effectiveness is validated by its ability to separate defaulters from non-defaulters, with defaulters clustering at lower scores. Finally, a risk grid is constructed by grouping scores into distinct classes, such as "High Risk" or "Low Risk," and checking the stability of these classes over time.