Learning an Interpretable Risk Scoring System for Maximizing Decision Net Benefit
arXiv:2604.04241
The paper introduces an interpretable risk scoring system that directly maximizes decision net benefit by formulating the problem as a sparse integer linear program, and shows it maintains good discrimination and calibration on various datasets.
Abstract
Risk scoring systems are widely used in high-stakes domains to assist decision-making. However, existing approaches often focus on optimizing predictive accuracy or likelihood-based criteria, which may not align with the main goal of maximizing utility. In this paper, we propose a novel risk scoring system that directly optimizes net benefit over a range of decision thresholds. The model is formulated as a sparse integer linear programming problem which enables the construction of a transparent scoring system with integer coefficients, and hence, facilitates interpretation and practical application. We also establish fundamental relationships among net benefit, discrimination, and calibration. Our analysis proves that optimizing net benefit also guarantees conventional performance measures. We evaluated our method on multiple public datasets as well as on a large-scale credit risk dataset. This computational study demonstrated that our interpretable method can effectively achieve high net benefit while maintaining competitive discrimination and calibration performance.
50 pages, 9 figures, 17 tables, and 6 algorithm