Human–AI hybrid finance: from AI tools to decision systems

Abstract Artificial intelligence (AI) in finance is commonly reviewed by method, data type, or application domain. These perspectives are essential, but they understate a deeper shift: AI is moving from a predictive tool to a component of human–AI hybrid financial decision systems. This integrative and conceptual review synthesizes literature across finance, management, human–computer interaction (HCI), and AI to examine how humans and AI jointly participate in information acquisition, prediction, recommendation, approval, execution, monitoring, and learning. We argue that the central question is moving from model performance to decision architecture: how authority, oversight, and accountability should be allocated across financial workflows. We show that human–AI complementarity in finance is conditional rather than automatic, depending on task structure, private information, feedback quality, incentives, explanation design, and governance. We also argue that AI-mediated financial decisions are reflexive: they reshape organizational workflows, prices, liquidity, credit allocation, and the future data on which subsequent decisions rely. The review integrates evidence on methods, data, scenarios, explainability, trust, governance, financial large language models (FinLLMs), and agentic finance, and organizes the field around an integrated decision-system framework consisting of five connected constructs—delegation frontier, reliance wedge, decision-useful explainable artificial intelligence (XAI), meaningful oversight, and reflexive AI loop—to support cumulative research on investment, trading, credit, asset management, risk, compliance, and financial regulation.