Credit scoring models often fail not due to weak algorithms but because variables weren't properly prepared. Categorization transforms raw data into meaningful risk classes, addressing issues like non-linear relationships, excessive modalities, outliers, and missing values. This preprocessing step creates variables that are statistically meaningful, economically interpretable, and stable over time.

For categorical variables, categorization reduces dimensionality by grouping similar categories together. Instead of creating numerous dummy variables, the model estimates fewer parameters, resulting in more stable coefficients. For continuous variables, categorization helps capture non-linear risk patterns that logistic regression models might otherwise miss, while also reducing the impact of extreme values.

The Weight of Evidence transformation is particularly valuable when discretizing continuous variables. This approach helps prepare variables for interpretable credit scoring models by creating ordered risk classes. Missing values can be handled by creating dedicated categories, as missingness itself may contain risk information that improves model performance. Proper categorization ultimately leads to more robust and interpretable credit scoring systems.