Human-in-the-Loop Controls for Autonomous Financial Management: Governance, Accountability, and Auditability
Keywords:
Human-In-The-Loop, Autonomous Financial Management, AI Governance, Algorithmic Accountability, Financial Auditing, Human Override, Decision ProvenanceAbstract
Artificial intelligence is moving financial management from decision support toward systems that can recommend, authorize, and execute actions across budgeting, treasury, credit, fraud response, portfolio management, payments, and compliance. This autonomy creates a governance problem when human actors lack timely visibility, effective intervention rights, or evidence sufficient to reconstruct decisions. This paper conducts a structured, literature-based review of peer-reviewed open-access research on human oversight, human–AI interaction, algorithmic accountability, explainability, auditing, decision provenance, and financial AI. It develops a four-stage taxonomy of ex ante, concurrent, ex post, and continuous governance controls and maps preventive, suspensive, corrective, reversal, system-level, and policy overrides across the financial-decision lifecycle. The resulting Governable Financial Autonomy Model connects five layers: autonomy boundaries, decision controls, intervention mechanisms, auditability, and accountability and learning. The model argues that meaningful human control depends jointly on information, competence, authority, response time, and technical intervention capability, while auditability requires linked data, model, policy, decision, human-action, and outcome records. The paper contributes a risk-sensitive governance architecture, accountability-role structure, and testable propositions for future research. The framework is conceptual and requires empirical validation across financial applications, organizations, and regulatory settings.
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