OSIRA, an AI Early Warning System Using Open Source Intelligence for Digital Fraud Prevention

                2026-11-06
              

Published

                177-191
              

Pages

OPEN

Access

Nafila Kurnia Sari

Universitas Gadjah Mada, Yogyakarta, Indonesia

Alya Zaskya

Universitas Gadjah Mada, Yogyakarta, Indonesia

Muhammad Zakiyyuddin Abdul Adhiim

Universitas Gadjah Mada, Yogyakarta, Indonesia

Abstract

Purpose: This study designs and evaluates OSIRA (Open Source Intelligence Risk Analyzer), an early warning system based on artificial intelligence that integrates Open Source Intelligence (OSINT) and Natural Language Processing (NLP) to identify digital fraud indications before they enter formal reporting mechanisms in a cashless society.
Research Methodology: The study uses a design science approach consisting of problem analysis, system design, risk score formulation, and a limited scale proof of concept simulation. The simulation used 540 synthetic texts from 50 template groups, divided into 394 training and 146 testing texts through a group based split, and classified them with Term Frequency–Inverse Document Frequency (TF IDF) features and logistic regression.
Results: OSIRA integrates web crawling, text preprocessing, fraud classification, entity extraction, a risk engine, and an early warning module. At a threshold of 0.50, the model achieved 82.88% accuracy, 79.66% precision, 98.95% recall, an F1 score of 88.26%, and a ROC AUC of 0.8528, but its false positive rate reached 47.06%, mainly for normal texts discussing fraud related topics.
Conclusions: OSIRA shows initial feasibility as an additional intelligence layer that can shift fraud mitigation from reactive response toward proactive fraud intelligence.
Limitations: The evaluation used synthetic rather than real OSINT data, a baseline classifier with limited contextual understanding, and risk score weights and thresholds that have not been empirically calibrated.
Contributions: The study offers a conceptual and technical framework for using pre reporting public signals in fraud risk management and provides guidance for banks, digital wallet providers, and regulators.

Early Warning System Fraud Prevention Natural Language Processing Open Source Intelligence Risk Scoring

How to Cite

Sari, N. K., Zaskya, A., & Adhiim, M. Z. A. (2026). OSIRA, an AI Early Warning System Using Open Source Intelligence for Digital Fraud Prevention. Jurnal Teknika Dan Informatika, 6(2), 177-191. https://doi.org/10.52909/jti.v6i2.470

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References

Abdallah, A., Maarof, M. A., & Zainal, A. (2016). Fraud detection system: A survey. Journal of Network and Computer Applications, 68, 90–113. https://doi.org/10.1016/j.jnca.2016.04.007
Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138–52160. https://doi.org/10.1109/ACCESS.2018.2870052
Ali, A., Abd Razak, S., Othman, S. H., Eisa, T. A. E., Al-Dhaqm, A., Nasser, M., Elhassan, T., Elshafie, H., & Saif, A. (2022). Financial fraud detection based on machine learning: A systematic literature review. Applied Sciences, 12(19). https://doi.org/10.3390/app12199637
Bank Indonesia. (2026). Statistik sistem pembayaran dan infrastruktur pasar keuangan (SPIP). Retrieved from https://www.bi.go.id/id/statistik/ekonomi-keuangan/spip/Default1.aspx
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
Boulieris, P., Pavlopoulos, J., Xenos, A., & Vassalos, V. (2024). Fraud detection with natural language processing. Machine Learning, 113, 5087–5108. https://doi.org/10.1007/s10994-023-06354-5
Carcillo, F., Le Borgne, Y.-A., Caelen, O., Kessaci, Y., Oblé, F., & Bontempi, G. (2021). Combining unsupervised and supervised learning in credit card fraud detection. Information Sciences, 557, 317–331. https://doi.org/10.1016/j.ins.2019.05.042
Chen, L., Li, H., Chua, Y. P., & Chen, J. (2026). When awareness is not enough: Online fraud susceptibility, threat awareness, anti-fraud self-efficacy and online scam prevention behavior among Chinese university students. Frontiers in Psychology, 17, Article 1857855. https://doi.org/10.3389/fpsyg.2026.1857855
Committee of Sponsoring Organizations of the Treadway Commission & Association of Certified Fraud Examiners. (2023). Fraud risk management guide (2nd ed.).
Correa Bahnsen, A., Aouada, D., Stojanovic, A., & Ottersten, B. (2016). Feature engineering strategies for credit card fraud detection. Expert Systems with Applications, 51, 134–142. https://doi.org/10.1016/j.eswa.2015.12.030
Dal Pozzolo, A., Boracchi, G., Caelen, O., Alippi, C., & Bontempi, G. (2018). Credit card fraud detection: A realistic modeling and a novel learning strategy. IEEE Transactions on Neural Networks and Learning Systems, 29(8), 3784-3797. https://doi.org/10.1109/TNNLS.2017.2736643
Duflos, E., Izaguirre, J. C., Datwani, L., Mishra, A., Collins, D., Bathula, P. R., Mathias, L., & Sawant, D. (2023). Social media monitoring to assess consumer risks in digital credit apps: Guidance for supervisors from an India pilot. Consultative Group to Assist the Poor.
Erbuğa, G. S., & Ünal, C. (2026). Explainable artificial intelligence-driven fraud detection: Behavioral proxies of digital financial literacy in online payment models. Borsa Istanbul Review, 26, Article 100840. https://doi.org/10.1016/j.bir.2026.100840
Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627-660. https://doi.org/10.5465/annals.2018.0057
Gomber, P., Kauffman, R. J., Parker, C., & Weber, B. W. (2018). On the fintech revolution: Interpreting the forces of innovation, disruption, and transformation in financial services. Journal of Management Information Systems, 35(1), 220-265. https://doi.org/10.1080/07421222.2018.1440766
Hilal, W., Gadsden, S. A., & Yawney, J. (2022). Financial fraud: A review of anomaly detection techniques and recent advances. Expert Systems with Applications, 193, Article 116429. https://doi.org/10.1016/j.eswa.2021.116429
Kim, E., Lee, J., Shin, H., Yang, H., Cho, S., Nam, S., Song, Y., Yoon, J., & Kim, J. (2019). Champion-challenger analysis for credit card fraud detection: Hybrid ensemble and deep learning. Expert Systems with Applications, 128, 214–224. https://doi.org/10.1016/j.eswa.2019.03.042
Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., & Zhang, G. (2019). Learning under concept drift: A review. IEEE Transactions on Knowledge and Data Engineering, 31(12), 2346-2363. https://doi.org/10.1109/TKDE.2018.2876857
Marsella, M., Wijaya, C. S., Wijaya, I., Shidqi, M. T., & Novita, D. (2023). Analisis implementasi artificial intelligence untuk bisnis: Systematic literature review. DEVICE: Journal of Information System, Computer Science and Information Technology, 4(2). https://doi.org/10.46576/device.v4i2.4037
Minaee, S., Kalchbrenner, N., Cambria, E., Nikzad, N., Chenaghlu, M., & Gao, J. (2021). Deep learning-based text classification: A comprehensive review. ACM Computing Surveys, 54(3). https://doi.org/10.1145/3439726
Nurqamarani, A. S., Fadilla, S., & Juliana, A. (2024). Revolutionizing payment systems: The integration of TRAM and trust in QRIS adoption for micro, small, and medium enterprises in Indonesia. Journal of Information Systems Engineering and Business Intelligence, 10(3), 314-327. https://doi.org/10.20473/jisebi.10.3.314-327
Otoritas Jasa Keuangan. (2026). Indonesia Anti-Scam Centre berhasil kembalikan Rp161 miliar dana masyarakat korban scam. Retrieved from https://ojk.go.id/id/berita-dan-kegiatan/siaran-pers/Pages/IASC-Berhasil-Kembalikan-Rp161-Miliar-Dana-Masyarakat-Korban-Scam.aspx
Ozili, P. K. (2018). Impact of digital finance on financial inclusion and stability. Borsa Istanbul Review, 18(4), 329-340. https://doi.org/10.1016/j.bir.2017.12.003
Pastor-Galindo, J., Nespoli, P., Gómez Mármol, F., & Martínez Pérez, G. (2020). The not yet exploited goldmine of OSINT: Opportunities, open challenges and future trends. IEEE Access, 8, 10282–10304. https://doi.org/10.1109/ACCESS.2020.2965257
Patil, P., Tamilmani, K., Rana, N. P., & Raghavan, V. (2020). Understanding consumer adoption of mobile payment in India: Extending Meta-UTAUT model with personal innovativeness, anxiety, trust, and grievance redressal. International Journal of Information Management, 54, Article 102144. https://doi.org/10.1016/j.ijinfomgt.2020.102144
Pehlivanlı, D., Aykaç Alp, E., & Katanalp, B. (2024). Introducing the overall risk scoring as an early warning system. Expert Systems with Applications, 246, Article 123232. https://doi.org/10.1016/j.eswa.2024.123232
Randhawa, K., Loo, C. K., Seera, M., Lim, C. P., & Nandi, A. K. (2018). Credit card fraud detection using AdaBoost and majority voting. IEEE Access, 6, 14277–14284. https://doi.org/10.1109/ACCESS.2018.2806420
Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206-215. https://doi.org/10.1038/s42256-019-0048-x
Safira, S., Sembiring, A. M. C., Firdaus, M. Z., Tsabitah, N. D., Longdong, G. A. C., & Solihin, I. (2026). Peran open-source intelligence (OSINT) sebagai early warning Ukraina menjelang invasi Rusia Februari tahun 2022 [The role of open source intelligence (OSINT) as an early warning for Ukraine before the Russian invasion of February 2022]. Jurnal Wacana Sosial Nusantara. Retrieved from https://ejournal.lembagarisetmandirinusantara.or.id/index.php/jwsn/article/view/122/153
Salloum, S., Gaber, T., Vadera, S., & Shaalan, K. (2022). A systematic literature review on phishing email detection using natural language processing techniques. IEEE Access, 10, 65703–65727. https://doi.org/10.1109/ACCESS.2022.3183083
Shin, D. (2021). The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human-Computer Studies, 146, Article 102551. https://doi.org/10.1016/j.ijhcs.2020.102551
Sudarmanto, E. (2020). Manajemen risiko: Deteksi dini upaya pencegahan fraud. Jurnal Ilmu Manajemen, 9(2), 107-121. https://doi.org/10.32502/jimn.v9i2.2506
West, J., & Bhattacharya, M. (2016). Intelligent financial fraud detection: A comprehensive review. Computers & Security, 57, 47–66. https://doi.org/10.1016/j.cose.2015.09.005
Whitty, M. T. (2019). Predicting susceptibility to cyber-fraud victimhood. Journal of Financial Crime, 26(1), 277-292. https://doi.org/10.1108/JFC-10-2017-0095
Yadav, A., Kumar, A., & Singh, V. (2023). Open-source intelligence: A comprehensive review of the current state, applications and future perspectives in cyber security. Artificial Intelligence Review, 56, 12407–12438. https://doi.org/10.1007/s10462-023-10454-y
Yang, H., Shukur, Z., & Sahran, S. (2026). A review of artificial intelligence for financial fraud detection. Applied Sciences, 16(4). https://doi.org/10.3390/app16041931
Yuspin, W., Putri, A. O., Fauzie, A., & Pitaksantayothin, J. (2024). Digital banking security: Internet phishing attacks, analysis and prevention of fraudulent activities. International Journal of Safety and Security Engineering, 14(6), 1699-1706. https://doi.org/10.18280/ijsse.140605
Zhang, W. (2024). Dynamic monitoring of financial security risks: A novel China financial risk index and an early warning system. Economics Letters, 234, Article 111445. https://doi.org/10.1016/j.econlet.2023.111445