Implementation of Artificial Intelligence in Financial Crisis Early Warning System

Loso Judijanto; Ahmad Rizani; Hety Devita

doi:10.54443/sj.v3i6.512

Authors

Loso Judijanto IPOSS Jakarta, Indonesia
Ahmad Rizani Universitas Palangka Raya, Indonesia
Hety Devita Universitas Mulia Balikpapan, Indonesia

DOI:

https://doi.org/10.54443/sj.v3i6.512

Keywords:

artificial intelligence, early warning system, financial crisis, machine learning, risk detection

Abstract

Financial crisis is a serious threat to economic stability, so an effective early warning system is needed to detect and anticipate potential risks early on. The implementation of artificial intelligence (AI) in financial crisis early warning systems offers significant advantages through big data analysis capabilities, non-linear pattern detection, and more accurate and faster risk prediction than conventional methods. Studies have shown that machine learning and deep learning algorithms can improve crisis prediction accuracy, expand the scope of risk monitoring, and support more responsive decision-making by regulators and financial industry players. However, challenges such as data quality, security, model transparency, and human resource readiness still need to be addressed for optimal AI implementation. This study concludes that with good governance, investment in infrastructure and human resources, and adaptive regulations, AI can be a strategic tool in strengthening early warning systems and maintaining financial sector resilience in the digital era.

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References

Bank of England. (2025). Financial Stability in Focus: Artificial intelligence in the financial system.

Bluwstein, K. & et al. (2021). Comparing Early Warning Models for Financial Crises: The Role of Machine Learning. Journal of Financial Stability, 53. https://doi.org/10.1016/j.jfs.2021.100819

Chan-Lau, J. A. (2023). Interpreting Large-scale Machine Learning Crisis Prediction Models. International Monetary Fund. https://doi.org/10.5089/9798400254332.001

Eichengreen, B., Rose, A., & Wyplosz, C. (1996). Contagious Currency Crises. Scandinavian Journal of Economics, 98(4). https://doi.org/10.2307/3440877

Eliyah, E., & Aslan, A. (2025). STAKE’S EVALUATION MODEL: METODE PENELITIAN. Prosiding Seminar Nasional Indonesia, 3(2), Article 2.

Fouliard, J. & et al. (2021). Predicting Systemic Financial Stress Episodes with Machine Learning. Journal of Financial Data Science, 3(1). https://doi.org/10.3905/jfds.2021.1.037

Frankel, J., & Rose, A. (1996). Currency Crashes in Emerging Markets: An Empirical Treatment. Journal of International Economics, 41(3–4). https://doi.org/10.1016/S0022-1996(96)01441-9

Hacibedel, B., & Qu, S. (2022). Systemic Non-Financial Corporate Sector Distress: An Ensemble Machine Learning Approach. Journal of Risk and Financial Management, 15(5). https://doi.org/10.3390/jrfm15050198

Hellwig, M. (2021). Fiscal Crisis Prediction: Machine Learning vs. Traditional Econometrics. Economic Modelling, 104. https://doi.org/10.1016/j.econmod.2021.105657

Holopainen, J., & Sarlin, P. (2017). Machine learning methods for early warning systems of banking crises. Journal of Financial Stability, 32, 25–39. https://doi.org/10.1016/j.jfs.2017.08.001

Huang, Z. & et al. (2021). XGBoost Model for Financial Risk Early Warning. Applied Soft Computing, 100. https://doi.org/10.1016/j.asoc.2020.106943

Iskandar, R. & dkk. (2024). Peran Kecerdasan Buatan Dalam Meningkatkan Efisiensi dan Transparansi Pasar Keuangan. Ecoducation, E-Jurnal UIBU.

Janosov, M., & Szabo, G. (2020). Machine Learning for Market Analysis and Financial Risk Management. Journal of Computational Finance, 24(2). https://doi.org/10.2139/ssrn.3524567

Joseph, A. (2020). Shapley Regressions: A Framework for Statistical Inference on Machine Learning Models. Journal of Machine Learning Research, 21(1). https://doi.org/10.5555/3455716.3455721

Kaminsky, G., & Reinhart, C. (1999). The Twin Crises: The Causes of Banking and Balance-of-Payments Problems. American Economic Review, 89(3). https://doi.org/10.1257/aer.89.3.473

Li, X. & et al. (2020). Financial Crisis Early Warning Systems Based on Deep Learning. IEEE Access, 8. https://doi.org/10.1109/ACCESS.2020.2990816

Liu, H. & et al. (2021). Integrating ESG Factors into Financial Risk Early Warning Systems. Sustainability, 13(18). https://doi.org/10.3390/su131810053

Pemerintah Republik Indonesia. (2020). Strategi Nasional untuk Kecerdasan Artifisial Republik Indonesia 2020-2045.

Sarlin, P. (2021). AI and Big Data for Early Warning of Financial Crises. Big Data Research, 25. https://doi.org/10.1016/j.bdr.2021.100253

Sun, Y., & Li, J. (2022). Deep Learning for Intelligent Assessment of Financial Investment Risk Prediction. Journal of Risk and Financial Management, 15(10). https://doi.org/10.1155/2022/3062566

Suparman, A. (2024). Penerapan Kecerdasan Buatan Dalam Sistem Informasi Untuk Meningkatkan Keamanan Keuangan. E-Jurnal Ekonomi Dan Keuangan, 11(3). https://doi.org/10.22487/Ejk.V11i3.1366

Tim BINUS University. (2025). Revolusi Digital dalam Akuntansi Melalui Kecerdasan Buatan. BINUS University.

Tim Moneta. (2024). Penerapan Artificial Intelligence sebagai Inovasi di Sektor Keuangan. Moneta: Jurnal Ekonomi Dan Keuangan Syariah.

Torraco, R. J. (2020). Writing Integrative Literature Reviews: Guidelines and Examples. Human Resource Development Review, 19(4), 434–446. https://doi.org/10.1177/1534484320951055

Vasarhelyi, M. A., & Alles, M. (2018). The Impact of Artificial Intelligence on Fraud Detection in Financial Auditing. Journal of Accounting Literature, 42. https://doi.org/10.1016/j.acclit.2018.03.002

Wang, Y. & et al. (2021). Attention Mechanisms in Financial Crisis Prediction. Expert Systems with Applications, 183. https://doi.org/10.1016/j.eswa.2021.115340

Zhang, T., & Anhui, Z. (2025). AI-Driven Financial Risk Early-Warning Using TD-DS-RF: A Decision Support Model Integrating Multi-Source Data. Decision Making: Applications in Management and Engineering, 8(1), 478–496. https://doi.org/10.31181/dmame8120251391