Human-AI Collaborative Decision Intelligence for Financial Risk Management
Keywords:
human-AI collaboration, decision intelligence, financial risk management, trust calibration, model governance, systemic risk, explainability, operational resilienceAbstract
Financial risk management is increasingly mediated by computational intelligence, yet the most consequential decisions in banks, insurers, asset managers, and public financial institutions remain irreducibly human, institutional, and political. This paper develops a system-level account of human-AI collaborative decision intelligence for financial risk management. Rather than treating artificial intelligence as a replacement for expert judgment, the analysis frames collaboration as an architectural and governance problem in which human and machine capabilities are combined under conditions of uncertainty, regulatory scrutiny, and distributed accountability. The paper examines conceptual foundations drawn from behavioral decision theory, interpretable machine learning, and human-centered automation, then proposes a layered architecture spanning data aggregation, model orchestration, regime detection, explanation services, decision arbitration, and audit infrastructure. It considers structural trade-offs among predictive accuracy, robustness, transparency, fairness, latency, and institutional sustainability. Particular attention is given to market regime shifts, model drift, adversarial conditions, and the limits of historical calibration. The discussion then turns to governance, fairness, and accountability, arguing that collaborative decision intelligence requires calibrated trust, contestability, and clear allocation of responsibility across human agents, model providers, and institutional leaders. Deployment and organizational change are analyzed as long-horizon socio-technical processes rather than mere technology integration. Finally, the paper considers policy and cross-domain implications, including regulatory expectations for explainability, model risk management, and operational resilience. The central claim is that human-AI collaboration in financial risk management should be designed as a governed decision system that preserves human authority where ambiguity, legitimacy, and systemic consequence demand it.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
