Evolutionary Multi-Agent Optimization for Resilient Financial Risk Management

Abstract

Financial risk management is increasingly challenged by interconnected markets, heterogeneous actors, rapid regime shifts, and regulatory demands that extend beyond single-institution optimization. This paper examines evolutionary multi-agent optimization as a system-level architecture for resilient financial risk management. It argues that resilience emerges not merely from better prediction but from adaptive populations of risk agents that can explore, diversify, coordinate, and recover under uncertainty. The discussion integrates agent-based computational finance, evolutionary computation, multi-agent systems, systemic risk research, and governance studies. The paper develops a conceptual architecture in which risk agents represent portfolios, counterparties, liquidity pools, supervisory monitors, and market infrastructures, while evolutionary mechanisms shape their strategies through selection, variation, and coevolution. Particular attention is given to structural trade-offs among robustness, efficiency, fairness, transparency, and sustainability. The analysis shows that evolutionary multi-agent optimization can improve stress testing, contagion detection, collateral management, and dynamic capital allocation, but it also introduces governance risks related to emergent behavior, opacity, reward hacking, and unequal distribution of adaptation capacity. The paper concludes that resilient deployment requires hybrid human-machine oversight, auditable simulation infrastructures, fair access to computational resources, and policy frameworks that treat algorithmic risk management as critical socio-technical infrastructure rather than a private technical tool.