<![CDATA[Financial risk management plays a crucial role in ensuring the stability and resilience of financial markets. Traditional models, such as Value at Risk (VaR) and Monte Carlo simulations, have been widely used to assess risks; however, they often fail to account for complex, dynamic interactions between market participants. This paper explores the application of Agent-Based Models (ABMs) as an innovative approach to financial risk assessment. ABMs simulate the interactions between heterogeneous agents, such as investors, banks, and regulators, providing a more realistic representation of financial systems. The study highlights the strengths of ABMs in capturing systemic risks, non-linear dynamics, and emergent phenomena like market crashes, herd behavior, and contagion. The results demonstrate that ABMs can enhance the understanding of financial risk by modeling individual behaviors and their impact on market stability. Through simulation experiments, the paper shows how ABMs can complement traditional risk management tools by providing deeper insights into the systemic nature of financial crises. The findings suggest that ABMs offer valuable advantages over conventional models, particularly in assessing market volatility and the resilience of financial institutions. This research contributes to the growing body of literature advocating for the integration of ABMs into financial risk management frameworks.]]>
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