Explainable Large Language Models for High-Stakes Decision Support in Healthcare and Finance

Abstract

Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding, reasoning, and context-aware prediction across diverse domains. However, their deployment in high-stakes environments such as healthcare and finance raises significant concerns regarding transparency, reliability, accountability, and ethical risk. Decision-making in these sectors requires models that not only generate accurate outputs but also provide interpretable, audit-ready explanations that support human verification and regulatory compliance. This paper proposes a comprehensive framework for Explainable Large Language Models (X-LLMs) tailored for high-stakes decision support in clinical diagnostics, treatment recommendation, risk scoring, fraud detection, and financial forecasting. The framework integrates intrinsic interpretability techniques—such as attention visualizations, causal reasoning modules, knowledge-grounded decoding, and rule-constrained generation—with post-hoc explainability methods including counterfactual reasoning, feature attribution, rationale extraction, and trust calibration metrics. Additionally, X-LLMs employ domain-specific knowledge graphs, medical and financial ontologies, and uncertainty quantification modules to provide transparent, evidence-backed decision pathways. Empirical evaluation on real-world healthcare and financial datasets demonstrates that X-LLMs significantly outperform standard LLMs in explanation fidelity, user trust, and decision reliability, while maintaining comparable predictive accuracy. The findings highlight the transformative potential of explainable LLMs as trustworthy AI partners capable of supporting clinicians, financial analysts, and regulatory stakeholders in making transparent and accountable high-stakes decisions.