From Raw Data to Risk Classes

Categorization transforms raw data variables into a smaller number of meaningful groups to make their relationship with default risk clearer and more stable for credit scoring models. For categorical variables, this process reduces complexity by grouping similar items, while for continuous variables like income, it captures non-linear risk patterns that a simple linear model would otherwise miss. This technique also makes models more robust by managing the influence of outliers and provides a strategic way to handle missing values by assigning them to their own informative category. Ultimately, grouping variables improves model interpretability, allowing risk to be explained in intuitive business terms, and enhances stability in production environments.