<![CDATA[The development of Artificial Intelligence (AI) technology has brought significant changes in the global financial sector, especially in the context of systemic risk detection and mitigation. The complexity of financial market integration and the experience of previous global crises demonstrate the urgency of leveraging AI to strengthen the resilience of the financial system. This study aims to analyze the role of AI in systemic risk management by comparing its implementation in developed and developing countries. The research method uses a systematic literature review (SLR) approach enriched with bibliometric analysis to identify global research patterns, as well as comparative analysis to compare practices between the two groups of countries. Secondary data is obtained from academic articles, reports of international institutions, and financial risk indicators such as the Volatility Index (VIX), Capital Adequacy Ratio (CAR), and Non-Performing Loan Ratio (NPL). The results show that AI consistently improves the accuracy of systemic risk detection by up to 40% compared to traditional models. Developed countries are emphasizing the use of AI in the framework of macroprudential supervision, supported by adaptive regulations and mature data infrastructure. In contrast, developing countries are leveraging AI primarily for micro-risk management, such as credit risk and liquidity, but still face regulatory limitations, data infrastructure, and human resources. The main findings of this study confirm the gap in AI implementation between developed and developing countries, while demonstrating the urgency of international collaboration for regulatory harmonization and cross-border data exchange. This research contributes to the literature by presenting a cross-border comparative perspective, as well as providing policy recommendations that emphasize AI transparency, strengthening data infrastructure, and global cooperation to strengthen financial stability in the digital age.]]>
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