An Explainable Decision Intelligence Architecture for Secure Data Sharing and Risk-Aware Resource Governance

Abstract

Healthcare providers, financial institutions, retailers, logistics operators, and workforce systems generate large amounts of information for planning and risk related decisions. Much of this information cannot be freely shared because it may contain sensitive organizational or consumer data. Resource decisions also become difficult when demand changes, fraud risks increase, capacity becomes limited, or operational disruptions occur. This study develops an explainable decision intelligence architecture for these conditions. The proposed framework combines hierarchical federated learning with multimodal data analysis, Self-Sovereign Identity, group signatures, differential privacy, SHAP based explanations, Siamese fine-tuning, and Pareto-based optimization. Data remain within participating organizations, while selected model information supports collaborative analysis. The decision model considers resource cost, unmet demand, risk exposure, privacy expenditure, and service vulnerability at the same time. Stochastic programming, fuzzy decision models, scenario analysis, and sensitivity analysis are used to represent uncertain operating conditions. Five scenarios are considered: normal operations, demand surges, fraud escalation, supplier disruption, and compound disruption. Public and synthetic datasets provide the basis for comparison with centralized learning, standard federated learning, and optimization approaches without integrated explainability and privacy governance. The study develops a common decision framework for resource planning across heterogeneous service networks.