## 1 Introduction
### Literature Review
[6] developed models for enterprises in V4 countries, achieving over 88% accuracy using multiple discriminant analysis. The study identified total indebtedness ratios as the most significant predictor, providing valuable insights into the post-pandemic economic environment. [PERSON] and [PERSON] [24] created the logit and probit models in their study. The study's results indicate that the model based on logit functions slightly outperforms the classification ability of the probit model in predicting bankruptcies in the Slovak Republic. [PERSON] and [PERSON] [25] created a prediction model for Slovak companies based on multiple discriminant analysis with a classification accuracy of over 82%. In the Czech Republic, the Neumaier and Neumaierova family of financial distress prediction models--IN95, IN99, IN01, and IN05--are the most well-known Czech predictive models and significantly contribute to financial health assessment in the Czech Republic. These models were designed to adapt to local economic conditions and provide robust predictions of financial distress by evaluating various financial indicators [26; 27]. These models are also often used for predicting the financial state of the companies in Slovakia. [PERSON] and [PERSON] [28] developed a prediction model for the Czech construction industry, addressing the sector's unique financial characteristics. Their study critiques traditional models like the Altman Z-score, highlighting their limited applicability to construction firms. [PERSON] et al. [29] compared multivariate discriminant analysis (MDA), artificial neural networks (ANNs), and support vector machines (SVMs) for bankruptcy prediction of Czech industrial companies. Their findings indicated that ANNs and SVMs performed better than MDA. However, the authors emphasised the continued use of classical methods like MDA in certain practical applications due to their simplicity and ease of interpretation. [PERSON] et al. [30] analysed the performance of various bankruptcy prediction models over five years, finding that [PERSON]'s model achieved the highest overall success rate. Their study highlighted significant variations in model accuracy across industries, recommending sector-specific adjustments to improve predictions. [PERSON] and [PERSON] [31] focused their research on comparing the performance of financial distress prediction models under different economic conditions, including periods of growth and recession. The authors emphasise how changing economic environments influence model accuracy and highlight the importance of incorporating industry specifics in the prediction models. Recent studies from Polish authors have introduced innovative approaches to financial distress prediction, leveraging both traditional and advanced methodologies. Machine learning models, enhanced with oversampling techniques, achieved up to 99% predictive accuracy, highlighting the utility of ensemble learning for addressing imbalanced datasets in financial forecasting [32; 33]. Ensemble classifier models, including boosting and bagging, outperformed traditional single-classifier approaches when tested on data on Polish firms, providing robust early warnings of financial distress over a two-year horizon [34]. Another study focused on logit and discriminant models tailored to the Polish industrial sector, emphasising the need for locally adapted prediction methods over unadjusted global models [35]. Hybrid machine learning techniques, such as those combining XGBoost and artificial neural networks, further improved predictive accuracy by dynamically integrating advanced algorithms and addressing imbalances in Polish financial datasets [33]. Multivariable models also outperformed univariate approaches for Polish manufacturing companies, confirming that combining multiple financial indicators yields better predictions of financial distress [36]. In Hungary, the first Hungarian model was constructed by [PERSON] and [PERSON] [37] based on the data on Hungarian enterprises covering the period 1990-1991.