Abstract
The financing of terrorism in Indonesia, has become increasingly complex due to the involvement of various domestic terrorist organizations. These groups obtain funds through diverse sources such as direct donations, membership fees, self-funding, and the misuse of non-profit organizations (NPOs). This study aims to explore and analyze the potential and challenges of utilizing advanced technologies, particularly Artificial Intelligence (AI) and Machine Learning (ML), in preventing terrorist financing in Indonesia. The research employs a qualitative descriptive approach, utilizing secondary data from FATF reports and related literature. The findings indicate that AI and ML can significantly enhance the detection and investigation of suspicious financial activities, provide real-time transaction monitoring, and facilitate inter-agency collaboration. However, challenges such as data limitations, regulatory complexity, high implementation costs, and data security and privacy issues must be addressed to fully leverage these technologies. This study provides recommendations for developing a supportive regulatory framework, enhancing inter-agency cooperation, and investing in better data infrastructure to effectively utilize AI and ML in combating terrorist financing.
Published Version
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