In banking, combining Principal Component Analysis (PCA) and Decision Trees (DT) can optimize real-time governance systems under regulatory constraints by reducing dimensionality and enhancing interpretability. These models must align with fairness, auditability, and explainability guidelines, ensuring compliance while maintaining predictive power in fraud detection or credit scoring.

Thanks Chuck A Arize . I completely agree—PCA-DT models are well-suited for regulated banking systems due to their interpretability and compliance alignment. In my work, I’ve implemented governance-driven feedback loops that recalibrate PCA thresholds and DT splits dynamically in response to data drifts and policy changes. This approach helps ensure ongoing auditability and regulatory compliance under frameworks like GDPR and Basel III, while preserving model performance in areas like fraud detection.

For those interested, I’ve detailed this approach in my recent paper on regulatory-aligned PCA-DT optimization in banking systems:

Conference Paper Efficacy of Data Governance a Cutting Edge Approach to Ensur...