Financial statement fraud research has progressed from rule-based and statistical screening toward machine learning and explainable artificial intelligence (XAI). Recent finance and auditing literature has also begun to frame AI as part of human–AI decision and governance systems rather than as a stand-alone predictive tool. Yet a narrower audit-specific problem remains unresolved: how a model-generated fraud-risk signal should be evaluated, challenged, and translated into a proportionate audit response when explanations may be unstable, error consequences are asymmetric, and audit resources are constrained. This conceptual article develops a Human–AI Decision Governance Framework for financial statement fraud risk assessment. Using theory synthesis and conceptual model development, the framework integrates six layers: fraud-risk signaling, explanation assurance, professional judgment, decision utility, audit response, and governance with feedback. It treats AI outputs as decision inputs rather than fraud conclusions and distinguishes model performance from explanation reliability, professional validity, and decision utility. Six propositions specify testable relationships among predictive performance, explanation quality, professional skepticism, resource-sensitive thresholds, human override, and lifecycle governance. The contribution is deliberately domain-specific: it connects fraud analytics and XAI to the auditor’s assessment and response process, including the risks of material misstatement due to fraud under ISA 240 (Revised), rather than claiming a generic theory of human–AI governance. The framework provides a structured research agenda for behavioral, archival, simulation, and field validation.