ScorecardBuilder: an automated framework for creating self-organizing risk models

Interpretable risk models remain difficult to construct at scale, especially for large datasets common across diverse analytical settings. This work introduces an automated scorecard-building system that transforms structured data into interpretable, bounded integer scores for workflow-integrated applications or decision support tools. The pipeline combines elastic-net logistic regression, monotonic binning, weight-of-evidence encoding, and mixed-integer optimization to produce transparent, statistically constrained models without requiring specialized expertise. It handles missingness, enforces reproducible preprocessing, and generates audit-friendly artifacts that support regulatory review. Using retrospective clinical data, the system demonstrates a generalizable framework for interpretable predictive modeling and advances trustworthy, infrastructure-aware deployment for multipurpose use.