Perkembangan dan Efektivitas Early Warning System Berbasis Artificial Intelligence dalam Prediksi Financial Distress Perusahaan: Systematic Literature Review
DOI:
https://doi.org/10.59725/de.v33i1.390Keywords:
Artificial Intelligence, Deep Learning, Early Warning, Financial distress, Machine LearningAbstract
Financial distress is a condition of declining financial health of a company that can develop gradually and lead to business failure if not detected early. With the increasing complexity of the business environment and the limitations of conventional statistical methods, Artificial Intelligence/AI is increasingly being adopted in the development of early warning systems (EWS) to predict financial distress. This study aims to examine the development of AI-based EWS research, identify the most widely used algorithms, and evaluate the effectiveness of AI models compared to conventional methods in predicting financial distress. The method used is a comprehensive systematic literature review of 15 relevant scientific articles. The results show that the paradigm has shifted from statistical models to machine learning and deep learning. Random Forest and Artificial Neural Network are the most widely used algorithms and have better predictive performance. This study offers a conceptual synthesis of the progress, effectiveness, and challenges of applying AI in predicting financial distress and opens opportunities for further research on the development of contextual and interpretative EWS.
References
Arefin, S. M. S., Sultana, N., Rafi, M. A., & Rahman, M. M. (2025). AI-powered early warning systems for emerging market crises: Enhancing U.S. foreign. Journal of Economics, Finance and Accounting Studies, 7(5), 17–29. https://doi.org/10.32996/jefas.2025.7.5.3
Awalia, R., & Kristanti, F. T. (2023). Prediksi financial distress menggunakan artificial neural network (ANN) pada perusahaan perbankan yang terdaftar di Bursa Efek Indonesia (BEI) periode 2017–202. Jurnal Kajian Pendidikan Ekonomi dan Ilmu Ekonomi, 7(2).
Ayuni, N. W. D., Utthavi, W. H., & Lasmini, N. N. (2024). Artificial neural networks: A deep learning approach in financial distress prediction. In A. A. N. G. Sapteka, I. G. L. M. Parwita, I. K. Wiratama, F. Moi, K. W. Widantha, E. Septevany, D. A. I. C. Dewi, W. E. Mariani, & R. N. Fakhrurozi (Eds.), Proceedings of the International Conference on Sustainable Green Tourism Applied Science—Engineering Applied Science 2024 (ICoSTAS-EAS 2024) (Vol. 249, pp. 99–108). Atlantis Press International BV. https://doi.org/10.2991/978-94-6463-587-4_12
Barboza, F. L. D. M., Duarte, D. L., & Cunha, M. A. (2022). Anticipating corporate’s distresses. Exacta, 20(2), 470–496. https://doi.org/10.5585/exactaep.2021.17494
Ha, H. H., Dang, N. H., & Tran, M. D. (2023). Financial distress forecasting with a machine learning approach. Corporate Governance and Organizational Behavior Review, 7(3), 90–104. https://doi.org/10.22495/cgobrv7i3p8
Irvan, F. M. (2024). Comparative analysis of machine learning and deep learning models integrated with Altman Z-score for financial distress prediction in companies listed on the Indonesia Stock Exchange (IDX). EKOMBIS REVIEW: Jurnal Ilmiah Ekonomi Dan Bisnis, 12(2). https://doi.org/10.37676/ekombis.v12i2.5478
Judijanto, L., Rizani, A., & Devita, H. (2025). Implementation of artificial intelligence in financial crisis early warning system. 3(6).
Ke, J., & Wang, Q. (2023). Research on financial distress prediction models of Chinese listed companies in pharmaceutical manufactures based on machine learning. In K. Hemachandran, R. S. K. Boddu, & W. Alhasan (Eds.), Proceedings of the 2023 2nd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2023) (Vol. 9, pp. 544–552). Atlantis Press International BV. https://doi.org/10.2991/978-94-6463-222-4_59
Kitchenham, B., Brereton, O. P., Budgen, D., Turner, M., Bailey, J., & Linkman, S. (2009). Systematic literature reviews in software engineering—A systematic literature review. Information and Software Technology, 51(1), 7–15. https://doi.org/10.1016/j.infsof.2008.09.009
Kristanti, F. T., Febrianta, M. Y., Salim, D. F., Riyadh, H. A., & Beshr, B. A. H. (2024). Predicting financial distress in Indonesian companies using machine learning. Engineering, Technology & Applied Science Research, 14(6), 17644–17649. https://doi.org/10.48084/etasr.8520
Lokanan, M. E., & Ramzan, S. (2024). Predicting financial distress in TSX-listed firms using machine learning algorithms. Frontiers in Artificial Intelligence, 7, 1466321. https://doi.org/10.3389/frai.2024.1466321
Lorens, R. R., Prayogo, D., & Budiman, J. (2022). Prediksi kebangkrutan dengan metode ANN, SVM, dan CART pada perusahaan properti, konstruksi, dan industri sejenis yang terdaftar di BEI. Dimensi Utama Teknik Sipil, 9(2). https://doi.org/10.9744/duts.9.2.136-155
Nyabera, A. M., Ikamari, C., & Mocheche, G. (2024). Prediction of financial distress using dynamic artificial neural network for early warning system. Asian Journal of Probability and Statistics, 26(9), 151–159. https://doi.org/10.9734/ajpas/2024/v26i9651
Peralungal, A., & Natchimuthu, N. (2024). Machine learning in financial distress: A scoping review. International Research Journal of Multidisciplinary Scope, 5(3), 457–474. https://doi.org/10.47857/irjms.2024.v05i03.0779
Ruan, L., & Liu, H. (2021). Financial distress prediction using GA-BP neural network model. International Journal of Economics and Finance, 13(3), 1. https://doi.org/10.5539/ijef.v13n3p1
Samara, K., & Shinde, A. (2025). Bankruptcy prediction using machine learning and data preprocessing techniques. Analytics, 4(3), 22. https://doi.org/10.3390/analytics4030022
Sharif, S., & Saleem, H. M. N. (2024). A review on the determinants of financial distress. 8(4), 473–484. https://doi.org/10.35484/pssr.2024(8-IV)37
Sumarni, I. (2022). Analisis financial distress perusahaan di Indonesia dimasa pandemic COVID-19. PubBis: Jurnal Penelitian dan Pemikiran Administrasi Publik dan Administrasi Bisnis, 6(1). https://doi.org/10.35722/pubbis.v6i1.584
Surnayanti, & Erlina, R. (n.d.). Dinamika ekonomi internasional: Perubahan, ketidakpastian, dan peluang di era Society 5.0. Penerbit Adab.
Urrochman, M. Y., Setyati, E., & Kristian, Y. (2023). Prediksi timing financial distress pada Bank Perkreditan Rakyat di Indonesia menggunakan machine learning. Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi, 12(2), 576. https://doi.org/10.35889/jutisi.v12i2.1219
Yousaf, U. B., Jebran, K., & Wang, M. (2022). A comparison of static, dynamic and machine learning models in predicting the financial distress of Chinese firms. Romanian Journal of Economic Forecasting.
Zhong, J., & Wang, Z. (2022). Artificial intelligence techniques for financial distress prediction. AIMS Mathematics, 7(12), 20891–20908. https://doi.org/10.3934/math.20221145
