Comparing Facebook Prophet and ARIMA for Forecasting Online Gambling Transactions in Indonesian E-Wallets

Fadiyah Nur Ayu Ning  Tyas; Mohammad Reza Fahlevi; Muhammad Irwin Hentriansa

doi:10.21532/apfjournal.v11i1.428

Authors

Fadiyah Nur Ayu Ning Tyas Fraud Management, LinkAja
Mohammad Reza Fahlevi Fraud Management, LinkAja
Muhammad Irwin Hentriansa Fraud Management, LinkAja

DOI:

https://doi.org/10.21532/apfjournal.v11i1.428

Keywords:

Online Gambling, E-Wallet, Fraud Detection, Time Series Forecasting, Facebook Prophet

Abstract

The rapid expansion of digital financial services in Indonesia has driven widespread adoption of e-wallet platforms, while also enabling the growth of illegal online gambling activities. This study presents a predictive framework for detecting gambling-related behavior on Indonesian e-wallet platforms using the Facebook Prophet time series forecasting model, with ARIMA employed as a comparative benchmark. By analyzing historical transaction data flagged as suspicious or potentially linked to gambling, both models are used to capture seasonal trends and detect anomalies that signal future high-risk activity. The results show that Prophet effectively anticipates spikes in gambling-related transactions, often outperforming ARIMA in capturing complex seasonal patterns. These findings highlight the potential of time series forecasting to enhance fraud detection systems by enabling proactive interventions during predicted high-risk periods. This research contributes to the field of financial fraud prevention by demonstrating the value of integrating predictive analytics particularly Prophet into efforts to combat illicit behavior within Indonesia’s evolving digital finance ecosystem.

References

Adekoya, A. A., Olaoye, S. A., & Lawal, B. A. (2023). Internal Audit Values and Fraud Detection: An Empirical Analysis. Asian Journal of Economics, Business and Accounting, 23(17), 162–172. https://doi.org/10.9734/ajeba/2023/v23i171051.

Alfian, A. (2023). Fraud Analytics Practices in Public-Sector Transactions: A Systematic Review. Journal of Public Budgeting, Accounting & Financial Management, 35(5), 685–710. https://doi.org/10.1108/jpbafm-11-2022-0175.

Association of Certified Fraud Examiners (ACFE). (2024). Occupational Fraud 2024: A Report to the Nations (13th ed.). Association of Certified Fraud Examiners. https://legacy.acfe.com/report-to-the-nations/2024/.

Betti, N., & Sarens, G. (2020). Understanding the Internal Audit Function in a Digitalised Business Environment. Journal of Accounting & Organizational Change, 17(2), 197–216. https://doi.org/10.1108/jaoc-11-2019-0114.

Kontopoulou, V. I., Panagopoulos, A. D., Kakkos, I., & Matsopoulos, G. K. (2023). A Review of ARIMA vs. Machine Learning Approaches for Time Series Forecasting in Data Driven Networks. Future Internet, 15(8), 255. https://doi.org/10.3390/fi15080255.

Dal Pozzolo, A., Caelen, O., Le Borgne, Y. A., Waterschoot, S., & Bontempi, G. (2014). Learned Lessons in Credit Card Fraud Detection from a Practitioner Perspective. Expert Systems with Applications, 41(10), 4915–4928. https://doi.org/10.1016/j.eswa.2014.02.026.

Gupta, R. (2019). Data Mining for Fraud Detection: An Overview of Techniques and Applications. Turkish Journal of Computer and Mathematics Education, 10(1), 561–567.

Hasan, K. (2024). Adaptive Fraud Detection: Challenges of Traditional Rule-Based Systems in Digital Finance. Journal of Financial Crime Analytics, 17(2), 45–58.

Aros, L. H., Molano, L. X. B., Gutierrez-Portela, F., Moreno Hernandez, J. J., & Barrero, M. S. R. (2024). Financial Fraud Detection through the Application of Machine Learning Techniques: A Literature Review. Humanities and Social Sciences Communications, 11(1), 1130. https://doi.org/10.1057/s41599-024-03606-0.

Hilal, W., Gadsden, S. A., & Yawney, J. (2022). Financial Fraud: A Review of Anomaly Detection Techniques and Recent Advances. Expert Systems with Applications, 193, 116429. https://doi.org/10.1016/j.eswa.2021.116429.

Husainah, N., Paulina, J., Misrofingah, M., Pradipta, I. A., Maulana, A. E., & Fahlevi, M. (2023). Determining Factors of Digital Wallet Actual Usage: A New Model to Identify Changes in Consumer Behavior. International Journal of Data and Network Science, 7(2), 933–940. https://doi.org/10.5267/j.ijdns.2022.12.017.

Indonesia Business Post. (2024). Ministry Cracks Down on E-Wallets Used for Online Gambling. Indonesia Business Post. https://indonesiabusinesspost.com/insider/ministry-cracks-down-on-e-wallets-used-for-online-gambling/.

Islam, S., & Stafford, T. (2022). Factors Associated with the Adoption of Data Analytics by Internal Audit Function. Managerial Auditing Journal, 37(2), 193–223. https://doi.org/10.1108/maj-04-2021-3090.

Jurgovsky, J., Granitzer, M., Ziegler, K., Calabretto, S., Portier, P. E., He-Guelton, L., & Caelen, O. (2018). Sequence Classification for Credit-Card Fraud Detection. Expert Systems with Applications,100, 234-245.

Komdigi. (2024). Laporan Tahunan Kominfo dan Digitalisasi Indonesia. Kementerian Komunikasi dan Informatika Republik Indonesia.

Kwarteng, S. B. (2024). Comparative Analysis of ARIMA, SARIMA and Prophet Model in Forecasting. Research & Development, 5(4), 110–120.

Moschini, G., Houssou, R., Bovay, J., & Robert-Nicoud, S. (2021). Anomaly and Fraud Detection in Credit Card Transactions using the ARIMA Model. Engineering Proceedings, 5(1), 56. https://doi.org/10.3390/engproc2021005056.

Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011). The Application of Data Mining Techniques in Financial Fraud Detection: A Classification Framework and an Academic Review of Literature. Decision Support Systems, 50(3), 559–569. https://doi.org/10.1016/j.dss.2010.08.006.

Novita, N., & Anissa, A. (2022). The Role of Data Analytics for Detecting Indications of Fraud in the Public Sector. International Journal of Research in Business and Social Science, 11(7), 218–225. https://doi.org/10.20525/ijrbs.v11i7.2113.

PPATK. (2025). Analisis Transaksi Perjudian Online 2024–2025. PPATK. https://www.ppatk.go.id/.

Sidauruk, D. L. (2024). Data Analytics in Fraud Prevention and Detection by Government Internal Supervisory Apparatuses at Ministries/Institutions/Local Governments: A Mixed-Method Study. Asia Pacific Fraud Journal, 9(2), 241–260. https://doi.org/10.21532/apfjournal.v9i2.340.

Taylor, S. J., & Letham, B. (2018). Forecasting at Scale. The American Statistician, 72(1), 37–45. https://doi.org/10.1080/00031305.2017.1380080.

Thimonier, H., Popineau, F., Rimmel, A., Doan, B. L., & Daniel, F. (2024). Comparative Evaluation of Anomaly Detection Methods for Fraud Detection in Online Credit Card Payments. In Proceedings of the Ninth International Congress on Information and Communication Technology (ICICT 2024), 1011, 37–48. Springer. https://doi.org/10.1007/978-981-97-4581-4_4.

Undang-undang (UU). (2024). Undang-undang (UU) Nomor 1 Tahun 2024 tentang Perubahan Kedua atas Undang-Undang Nomor 11 Tahun 2008 tentang Informasi dan Transaksi Elektronik. Undang-undang (UU).

Yadav, S. (2022). A Comparative Study of ARIMA, Prophet and LSTM for Time Series Prediction. Journal of Artificial Intelligence Machine Learning and Data Science, 1(1), 1813–1816.