From Credit Scoring to Governed Decision Systems: Integrating Predictive Performance, Uncertainty, and Governance
TL;DR
The Calibrated, Adaptive, Knowledge-aware Ensemble for Credit Scoring (CAKE-CS), a structure-aware architecture integrating fold-local dependency representation, heterogeneous experts, sparse routing, cross-fitted stacking, probability calibration, and cross-conformal decisions within a fold-disciplined pipeline, is presented.
Abstract
Credit-scoring systems must translate predictive risk into calibrated, uncertainty-aware, and governable decisions rather than ranking applicants alone. This study aims to develop and evaluate a governed credit-scoring architecture integrating predictive performance, probability quality, uncertainty, and governance within a unified decision-oriented framework. Existing studies typically evaluate these functions separately, leaving uncertainty and governance outside the core modeling pipeline. We present the Calibrated, Adaptive, Knowledge-aware Ensemble for Credit Scoring (CAKE-CS), a structure-aware architecture integrating fold-local dependency representation, heterogeneous experts, sparse routing, cross-fitted stacking, probability calibration, and cross-conformal decisions within a fold-disciplined pipeline. Across eleven public credit and default portfolios containing 70,518 observations, CAKE-CS achieves a macro-averaged Area Under Curve (AUC) of 0.869 versus 0.862 for the strongest gradient-boosting competitor, together with the lowest macro-averaged expected misclassification cost, Brier score, and log loss among nineteen competitors under the primary protocol. Bidirectional ablation identifies stacking as the clearest positive contributor to AUC, while routing and structure serve primarily architectural roles. Empirical cross-conformal coverage ranges from 0.882 to 0.920. The originality is architectural rather than algorithmic. CAKE-CS reconfigures credit scoring from a model-centric prediction problem into a governed decision-system problem by linking prediction, probability quality, uncertainty, action, monitoring, and audit evidence. Theoretically, this extends credit-scoring research by treating these dimensions as interconnected components of decision-system performance. Practically, the architecture provides a deployment-oriented template in which calibrated predictions and uncertainty support decision thresholds, human review, monitoring, and audit.