Implementation Strategy for the Real-Time Financial Crisis Early Warning System for Profit Optimization at Permata Bunda Syariah Clinic Cirebon
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Abstract
Background. Financial uncertainty and cash flow dynamics in healthcare facilities require an early warning system that can detect potential financial distress quickly and accurately. PBSC Clinic, as a healthcare institution, faces financial risks stemming from fluctuations in revenue, operating expenses, and short-term and long-term liabilities. Therefore, the development of the Real Time Clinic's Financial Crisis Early Warning System (RT, CFC, EWS) is important as a managerial decision-making tool based on actual financial data.
Purpose. This study aims to analyze the financial condition of PBSC Clinic and to develop a CFC EWS RT based on profitability, liquidity, leverage, and distance-to-default indicators, presented in a real-time dashboard.
Method. This type of research is an evaluative-descriptive study with a case study approach. The data used are the financial statements of the PBSC Clinic for the current period, which are analyzed using financial ratios and risk zone mapping (green, yellow, red) as early warning signals.
Results. The results of the study show that the CFC EWS RT dashboard can represent the clinic's financial condition in an accurate and contextual manner. This system is effective at providing an early signal of declining profitability, liquidity pressures, and increased leverage risk, allowing management to take corrective action in a relatively short time.
Conclusion. RT CFC EWS plays a strategic role in supporting the financial stability and sustainability of the PBSC Clinic.
Implications. Practically, this research provides implementable guidance that can be directly used by the management of PBSC Clinics and similar health facilities. The CFC EWS RT Dashboard is capable of being a simple but effective daily monitoring tool.
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References
Abubakar, A., Astuti, R. I., & Oktapiani, R. (2018). Selection of early warning indica-tors to identify distress in the corporate sector: Crisis prevention strengthening efforts. Bulletin of Monetary Economics and Banking, 20(3), 251–281.
Aebi, V., Sabato, G., & Schmid, M. (2020). Risk management, corporate governance, and bank performance in the financial crisis. Journal of Banking & Finance, 118, 105874.
Ali, S., & Anwar, S. (2023). Liquidity risk, operational risk, and profitability nexus: Evidence from Asian financial markets. Finance Research Letters, 55, 103857.
Alkomsan, A. A., & Yacoub, M. M. (2025). A hybrid early warning system for corpo-rate financial distress: Integrating Altman Z-score with machine learning tech-niques on companies listed on the Egyptian Exchange. Journal of Managerial Sciences, 49(1), 424–470.
Altman, E. I., Iwanicz-Drozdowska, M., Laitinen, E. K., & Suvas, A. (2020). Finan-cial distress prediction in an international context: A review and empirical analysis of Altman's Z-score model. Journal of International Financial Man-agement & Accounting, 31(2), 137–171.
Ammann, M., & Verhofen, M. (2022). Capital structure decisions: Empirical evidence based on trade-off and pecking-order predictions. Journal of Corporate Fi-nance, 72, 102150.
Breia, C., & da Costa, J. (2021). The dynamic relationship between cash flow, invest-ment, and financial constraints. Finance Research Letters, 39, 101587.
Chao, Y. (2024). Technologies on intelligent financial risk early warning in higher ed-ucation institutions: A systematic review. International Journal on Informatics Visualization, 8(3).
Chen, Y., & Young, M. (2020). Resource-based view and strategic financial manage-ment capability: Evidence from emerging markets. International Review of Fi-nancial Analysis, 72, 101593.
Creswell, J. W. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Sage Publications.
Farooq, U., & Salem, R. (2022). Corporate financial distress and firm performance: Evidence from emerging markets. Finance Research Letters, 48, 102887.
Firdaus, N. T., & Santoso, N. (2025). Early warning systems for financial crisis predic-tion: A systematic literature review of econometrics, machine learning, and un-certainty indices. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 5(4), 1415–1422.
Florio, C., & Leoni, G. (2021). Enterprise risk management and firm performance: The Italian case. Journal of Risk and Financial Management, 14(1), 35.
Gao, L. (2024). Construction and evaluation of financial distress early warning model based on machine learning. Journal of Electrical Systems, 20(3s).
González-Torres, T., & Naranjo-Gil, D. (2022). Risk management systems and finan-cial performance: The moderating effect of environmental uncertainty. Interna-tional Journal of Accounting Information Systems, 46, 100583.
Hamdat, A., Ceskakusumadewi, B., & Samalam, A. G. (2024). The impact of man-agement information systems on decision-making efficiency via real-time data. Vifada Management and Digital Business, 1(2), 56–74.
Jiang, W., Wu, X., & Wang, X. (2022). Construction and application of the financial early warning model based on the BP neural network. Computational Intelli-gence and Neuroscience, 2022, Article 5108677.
Kim, S., Kang, T., & Kim, Y. (2021). Early detection of corporate financial distress using ensemble learning techniques. Journal of Risk and Financial Manage-ment, 14(11), 543.
Lechner, P., & Gatzert, N. (2019). Determinants and value of enterprise risk manage-ment: Empirical evidence from the literature. Risk Management and Insurance Review, 22(3), 225–247.
Li, S., & Chen, X. (2024). An effective financial crisis early warning model based on an IFOA-BP neural network. Journal of Internet Technology, 25(3), 435–446.
Li, W., & Wang, Z. (2023). Predicting financial distress using machine learning mod-els: A comparative study. Expert Systems with Applications, 218, 119600.
Mustafa, F., Ausat, A. M. A., & Kraugusteeliana, K. (2024). The role of business in-formation systems in strategic decision-making: Evidence from ERP, CRM, and BI. JMP (Journal of Management & Marketing).
Outecheva, N. (2021). Corporate financial distress: A survey of the literature and emerging research trends. International Journal of Financial Studies, 9(4), 65.
Powell, R. J., Dinh, D. V., Vu, N. T., & Vo, D. H. (2024). Accounting-based variables as an early warning indicator of financial distress in crisis and non-crisis peri-ods. International Journal of Finance & Economics, 29(4), 4105–4124.
Setiawan, A. W., & Cahya, W. (2024). The strategic role of AI in enhancing MIS per-formance and innovation. Data: Journal of Information Systems and Manage-ment, 2(4), 239–252.
Sihombing, D. J. C., et al. (2023). Development of financial management information system with real-time interface for transaction recording and reporting. In-foScience, 5(2), 104–115.
Tanaka, K. (2024). A multi-stage financial distress early warning system: Analyzing corporate insolvency with random forest. Journal of Financial Studies, 18(4).
Thomas, R., & George, A. (2022). Real-time data systems and organizational decision-making: A cloud computing perspective. International Journal of Information Management, 66, 102511.
Wang, W., & Liang, Z. (2024). Financial distress early warning for Chinese enterprises from a systemic risk perspective: Based on the adaptive weighted XGBoost-bagging model. Systems, 12(2), 6