AI-DRIVEN OVERSIGHT OF RELIGIOUS PHILANTHROPY: A PREVENTIVE APPROACH TO TERRORISM FINANCING IN INDONESIA - Rabitha, Daniel; Nugroho, Novi Dwi; Ismail, Ismail; Sugiyarto, Wakhid (Proceedings of the International Conference of Islamic Economics and Business (ICONIES); Vol 11 No 1 (2025): The 11th International Conference of Islamic Economics and Business (ICONIES 2025), 2025)
Terrorism financing through religious philanthropic institutions poses a serious challenge in Indonesia, as evidenced by Jemaah Islamiyah's (JI) exploitation of Baitul Maal Abdurrahman Bin Auf (BM ABA) and Syam Organizer (SO) as "funding machines" (Nugroho et al., 2025). Globally, faith-based charities are vulnerable to misuse as a cover for terrorist activities, with terrorist groups adaptively shifting to more sophisticated funding methods, including the exploitation of unregistered non-profit organizations (NPOs) (Mardiansyah, 2022; Wardhana, 2023). Artificial Intelligence (AI) holds transformative potential to assist governments and oversight bodies in detecting anomalous donation patterns, flagging high-risk transactions, and disrupting funding chains before they can be used for violent acts (Bank for International Settlements, n.d.; Infosys BPM, n.d.). This research has examined the patterns of religious philanthropy misuse in terrorism financing in Indonesia, analyzed how AI can be implemented to detect and prevent such misuse at the fundraising stage, and identified the ethical, technical, and legal challenges in its application. Using case studies of BM ABA and SO, this study analyzed financial transactions, donation reports, and relevant open-source intelligence (OSINT) (Nugroho et al., 2025). The potential AI applications discussed include Natural Language Processing (NLP) for analyzing donation campaign content, anomaly detection for fund flows, and network analysis to identify donor-operator relationships (Irwanto et al., 2025). The research findings indicate that both institutions, despite having similar charitable goals, employed different methods (charity boxes vs. Tabligh Akbar) and were both exploited for terrorism financing (Nugroho et al., 2025). Recurring transaction patterns and donor clustering were identified as potential predictive features for AI models, while regulatory oversight in Indonesia was found to have minimal utilization of data-driven tools (Indonesia Business Post, 2022; Mardiansyah, 2022). This paper contributes academically by providing an interdisciplinary framework spanning security studies, philanthropy, and AI technology; practically by proposing an AI-based monitoring system; and ethically by recommending human-rights-compliant AI implementation to avoid discrimination against religious communities.