A Conceptual Framework for Post-COVID Financial Distress Prediction Using Fuzzy Inference Systems in Emerging Markets

Financial distress prediction has traditionally been based on linear, ratio-driven models that assume stable economic conditions and clear financial signals. However, the COVID-19 pandemic introduced significant uncertainty and uneven recovery patterns, particularly in emerging markets, which challenge these assumptions. In response, this study proposes a conceptual framework for post-COVID financial distress prediction using a Fuzzy Inference System (FIS). Drawing on financial distress theory, decision science, and fuzzy logic, this paper reinterprets key financial ratios—such as profitability, liquidity, leverage, and efficiency—not as precise values, but as flexible and linguistically meaningful indicators. These indicators are then integrated into a rule-based fuzzy inference system to generate a more nuanced assessment of financial distress risk. From a theoretical perspective, the proposed framework shows how fuzzy inference can better capture non-linearity, uncertainty, and the complex interactions between financial variables—areas where traditional linear models often fall short. This study contributes to the financial distress literature by introducing an uncertainty-aware framework that connects corporate finance with intelligent decision-making systems. It also lays a strong foundation for future empirical testing and offers practical insights for regulators, investors, and managers navigating post-crisis environments.

Financial distress; Fuzzy inference system; Conceptual framework; post-COVID recovery; Emerging markets

1. Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589–609. [Google Scholar] [Crossref]

2. Fahmi, I. (2020). Analisis Laporan Keuangan. Alfabeta. [Google Scholar] [Crossref]

3. Gunawan, B., & Putra, H. (2021). Empirical Study of Manufacturing Companies Listed on the Indonesia Stock Exchange and Malaysia Stock Exchange Period 2017-2018. Atlantis Press, 176, 113-120. [Google Scholar] [Crossref]

4. Hashim, M., Muhammad, K., Ghani, E. K., & Azis, M. A. A. (2024). Financial Distress Analysis of Top 100 Malaysian Public Listed Companies during COVID-19 Pandemic using Altman Z-Score Analysis. International Journal of Economics and Financial Issues, 14(4), 200–205. [Google Scholar] [Crossref]

5. Hashim, H. N. M. (2021). Why financially distressed companies PN17 of Bursa Malaysia regularisation plan are delayed? [Google Scholar] [Crossref]

6. Ismail, N. A., & Mahussin, N. (2021). BURSA MALAYSIA SECTORAL INDICES RETURN CORRELATION BEFORE AND DURING THE COVID-19 PANDEMIC. [Google Scholar] [Crossref]

7. Judijanto, L., & Riandari, F. (2024). Fuzzy logic framework for financial distress prediction: Enhancing corporate decision-making under uncertainty. International Journal of Basic and Applied Science, 13(1), 1-13. [Google Scholar] [Crossref]

8. Marzuki, N. M., Foziah, H., & Daud, W. M. N. W. (2025). Enhancing Financial Distress Prediction Using Logistic Regression and Managerial Literacy: A Conceptual Framework for the Malaysian Construction Industry. The Journal of Management Theory and Practice (JMTP), 6(1), 6-11. [Google Scholar] [Crossref]

9. Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of accounting research, 109-131. [Google Scholar] [Crossref]

10. Sahu, A., Sinha, S., & Banka, H. (2024). Fuzzy inference system using genetic algorithm and pattern search for predicting roof fall rate inunderground coal mines. International Journal of Coal Science and Technology, 11(1). https://doi.org/10.1007/s40789-023-00630-4 [Google Scholar] [Crossref]

11. Saleem, Q., & Rehman, R. U. (2011). Impacts of liquidity ratios on profitability. Interdisciplinary journal of research in business, 1(7), 95-98. [Google Scholar] [Crossref]

12. Shinde-Pawar, M., Patil, J., Shah, A., & Rasal, P. (2022). Development Of Fuzzy Inference System For COVID-19 Data Analysis. Journal of Pharmaceutical Negative Results, 13(4), 693–699. https://doi.org/10.47750/pnr.2022.13.04.093 [Google Scholar] [Crossref]

13. Tang, W. L., Tsai, J. T., & Chen, Y. M. (2021). Fuzzy logic and Gagné learning hierarchy for assessing mathematics skills. Science Progress,104(2). https://doi.org/10.1177/00368504211014346 [Google Scholar] [Crossref]

14. Yunus, S. Z. S. A., Puteh, S. E. W., Ali, A. M., & Daud, F. (2021a). The Covid Impact to Public Healthcare Utilization Among Urban Low-Income Subsidized Community in Klang Valley Malaysia. Health Services Research and Managerial Epidemiology, 8. https://doi.org/10.1177/23333928211002407 [Google Scholar] [Crossref]

15. Yazdanfar, D., & Öhman, P. (2020). Financial distress determinants among SMEs: empirical evidence from Sweden. Journal of Economic Studies, 47(3), 547-560. [Google Scholar] [Crossref]

16. Yunus, S. Z. S. A., Puteh, S. E. W., Ali, A. M., & Daud, F. (2021). The Covid impact to public healthcare utilization among urban low-income subsidized community in Klang Valley Malaysia. Health services research and managerial epidemiology, 8, 23333928211002407. [Google Scholar] [Crossref]

17. Zuhud, D. A. Z., Musa, M. H., Ismail, M., Bahaludin, H., & Razak, F. A. (2022). The causality and uncertainty of the COVID-19 pandemic to Bursa Malaysia financial services index’s constituents. Entropy, 24(8), 1100. [Google Scholar] [Crossref]

18. Zulkiflee, Z. N., & Idris, A. (2024). Fuzzy inference system model for air quality index prediction. Proc. Sci. Math, 23, 133-143. [Google Scholar] [Crossref]