Predicting financial failure in health sector balance sheets

This thesis investigates sector-specific financial failure risks within the Turkish healthcare industry and evaluates the efficacy of early warning mechanisms tailored for institutional sustainability. Employing the Altman Z-Score model as a predictive framework, the study analyzes longitudinal financial data across 12 healthcare sub-sectors from 2015 to 2023. The dataset was sourced from publicly accessible financial statistics published by the Central Bank of the Republic of Türkiye and compiled industry-level balance sheets and income statements. Z-Score components (X1–X5) were calculated for each sub-sector and then statistically correlated with key performance indicators, including net profit, total assets, and return on assets. Findings reveal that many sub-sectors have persistently remained below the financial distress threshold, with liquidity and operating profitability ratios exerting downward pressure on overall financial resilience. Post-pandemic recovery trends are present in selected domains; however, structural vulnerabilities largely persist across the system. Correlation analysis shows a strong positive relationship between asset efficiency and Z-Score values, whereas organizational scale exhibits a more complex and negatively skewed interaction with financial stability. Ultimately, the Altman model is validated as a viable early warning tool in healthcare financial risk estimation. The study concludes by advocating for the integration of domain-specific prediction models, data mining techniques, and AI-powered decision support systems to mitigate future vulnerabilities. Furthermore, public sector interventions—such as targeted subsidies and structural policy designs—are recommended to reinforce financial durability and ensure uninterrupted service delivery across healthcare providers.