Scalable Multi-Agent Architectures for Financial Risk Analysis and Adaptive Investment Decision-Making

Abstract

Financial markets are increasingly characterized by interdependence, heterogeneous information, rapid technological change, and systemic risk that cannot be understood adequately through isolated forecasting models. This paper examines scalable multi-agent architectures for financial risk analysis and adaptive investment decision-making from a systems-oriented perspective. It argues that the central challenge is not merely the construction of more capable artificial intelligence agents, but the design of socio-technical infrastructures that coordinate specialized agents under conditions of uncertainty, latency, adversarial pressure, regulatory constraint, and contested accountability. The discussion synthesizes research on agent-based computational finance, multi-agent systems, reinforcement learning, large-scale representation learning, and financial stability policy. It considers architectural trade-offs among centralization, federation, hierarchy, and market-like coordination, and it evaluates how these choices affect robustness, fairness, sustainability, and governance. Particular attention is given to deployment realities such as data provenance, model drift, explainability, liquidity feedback, and operational resilience. The paper also explores adaptive investment decision-making as a problem of constrained learning in non-stationary environments, where agents must balance exploration, risk exposure, and collective stability. It concludes that scalable multi-agent finance requires layered governance, heterogeneous oversight, and infrastructure designed for graceful degradation rather than optimal performance alone. Such architectures should be evaluated not only by predictive accuracy or portfolio returns, but also by their capacity to preserve market integrity, distribute risk transparently, and remain accountable to public policy objectives.