<![CDATA[The application of artificial intelligence and machine learning in the banking industry has gained significant attention in recent years. This paper aims to conduct a systematic review of the existing literature on the application of AI-based risk management in the banking sector especially in Indonesia. Thi research utilized desk methodology approach to summarize the development and implementation of AI in financial risk management. The review follows a structured approach to identify, analyze, and synthesize the key findings from relevant studies. The review covers the benefits, challenges, and potential future research directions in this domain. We found that AI-based risk management can provide significant improvements in areas such as credit risk assessment, fraud detection, and regulatory compliance. However, the adoption of AI in banking also comes with its own set of challenges, such as data availability, model interpretability, and ethical considerations. Findings from this review can guide both academics and practitioners in understanding the current state of AI-based risk management in banking and inform future research and implementation efforts.]]>
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