Research background: In a modern economy, full of complexities, ensuring a business' financial stability, and increasing its financial performance and competitiveness, has become especially difficult. Then, monitoring the company's financial situation and predicting its future development becomes important. Assessing the financial health of business entities using various models is an important area in not only scientific research, but also business practice. Purpose of the article: This study aims to predict the bankruptcy of companies in the engineering and automotive industries of the Slovak Republic using a multilayer neural network and logistic regression. Importantly, we develop a novel an early warning model for the Slovak engineering and automotive industries, which can be applied in countries with undeveloped capital markets. Methods: Data on the financial ratios of 2,384 companies were used. We used a logistic regression to analyse the data for the year 2019 and designed a logistic model. Meanwhile, the data for the years 2018 and 2019 were analysed using the neural network. In the prediction model, we analysed the predictive performance of several combinations of factors based on the industry sector, use of the scaling technique, activation function, and ratio of the sample distribution to the test and training parts. Findings & value added: The financial indicators ROS, QR, NWC/A, and PC/S reduce the likelihood of bankruptcy. Regarding the value of this work, we constructed an optimal network for the automotive and engineering industries using nine financial indicators on the input layer in combination with one hidden layer. Moreover, we developed a novel prediction model for bankruptcy using six of these indicators. Almost all sampled industries are privatised, and most companies are foreign owned. Hence, international companies as well as researchers can apply our models to understand their financial health and sustainability. Moreover, they can conduct comparative analyses of their own model with ours to reveal areas of model improvements.

Bankruptcy prediction is a powerful early-warning tool and plays a crucial role in various aspects of financial and business management. It is vital for safeguarding investments, maintaining financial stability, making informed credit decisions, and contributing to the overall health of the economy. This paper aims to develop bankruptcy prediction models for the Slovak engineering industry and to compare their effectiveness. Predictions are generated using the classical logistic regression (LR) method as well as artificial intelligence (AI) techniques (artificial neural networks (ANN) and support vector machines (SVM)). Research sample consists of 825 businesses operating in the engineering industry (Manufacture of machinery and equipment n.e.c.; Manufacture of motor vehicles, trailers and semi-trailers; Manufacture of other transport equipment). The selection of eight financial indicators is grounded in prior research and existing literature. The results show high accuracy for all used methods. The SVM outcomes indicate a level of accuracy on the test set that is nearly indistinguishable from that of the ANN model. The use of AI techniques demonstrates their effective predictive capabilities and holds a significant position within the realm of tools for forecasting bankruptcy.

Predicting bankruptcy within selected industries is crucial because of the potential ripple effects and unique characteristics of those industries. It serves as a risk management tool, guiding various stakeholders in making decisions. While artificial intelligence (AI) has shown high success rates in classification tasks, it remains uncertain whether its use significantly enhances the potential for early warning of impending problems. The following question arises: will classical methods eventually replace the effectiveness of these advanced techniques? This paper sheds light on the fact that even classical methods continue to achieve results that are not far behind, highlighting their enduring importance in financial analysis. This paper aims to develop bankruptcy prediction models for the chemical industry in Slovakia and to compare their effectiveness. Predictions are generated using the classical logistic regression (LR) method as well as AI techniques, artificial neural networks (ANNs), support vector machines (SVMs), and decision trees (DTs). The analysis aims to determine which of the employed methods is the most efficient. The research sample consists of circa 600 enterprises operating in the Slovak chemical industry. The selection of eleven financial indicators used for bankruptcy prediction was grounded in prior research and existing literature. The results show that all of the explored methods yielded highly similar outcomes. Therefore, determining the clear superiority of any single method is a difficult task. This might be partially due to the potentially reduced quality of the input data. In addition to classical statistical methods employed in econometrics, there is an ongoing development of AI-based models and their hybrid forms. The following question arises: to what extent can these newer approaches enhance accuracy and effectiveness?

Bankruptcy prediction is currently a widely researched topic, as it typically results from a chain of negative events. Logistic Regression (LR) is one of the standard prediction tools; however, with advances in technology, machine learning (ML) methods are gaining prominence and demonstrating improvements in performance and accuracy. It remains inconclusive whether ML methods significantly outperform traditional approaches such as LR in bankruptcy prediction. In this study, we identified the most commonly applied basic ML techniques—namely, Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), and Decision Trees (DTs)—which are frequently used in the literature for classification tasks. These methods were selected for empirical comparison with LR to evaluate their relative predictive performance and potential advantages in bankruptcy forecasting. In the EU, small and medium-sized enterprises (SMEs) constitute more than 99% of the economy; however, only a few survive beyond five years. This study examines bankruptcy prediction in the specific context of the Slovak Republic, using a sample of 2754 SME manufacturing enterprises from 2020 to 2021 and 3158 from 2022 to 2023. All models show good predictive performance; however, the small statistical difference between the results does not conclusively demonstrate the superiority of ML methods over LR.

Background: The fundamental foundation of financial and economic analysis is the implementation of financial and statistical models in business entities. Financial indicators help assess the company's financial situation and, at the same time, represent the most used tool in the detection of bankruptcy. A still significant method is the logistic regression, which ranks among classic statistical methods. Aims: The aim of the paper is to propose a scoring model for non-financial corporations in the electrotechnical industry and to estimate the probability of bankruptcy of enterprises in the electrotechnical industry of the Slovak Republic. Sample: The research sample consists of 1,241 companies operating in the electrotechnical industry of the Slovak Republic, according to SK NACE 26, 27 in the year 2020. Methods: We used logistic regression and designed a logistic model. Financial data were sourced from The Register of Financial Statements of the Slovak Republic. Results: In the bankruptcy model for the electrotechnical industry, the estimated odds ratios showed that the chances of bankruptcy significantly reduce the financial indicators EBITDA/T, ROA, L2, and NWC/A. When these indicators increase, the company's probability (or chance) of bankruptcy decreases. Conclusions: The estimate of a risky company or the probability of its bankruptcy is always very important. Therefore, it seems appropriate to look for applicable models. Assessing the financial health of business entities using various models is an important area not only in scientific research but also in the practice of business entities. Implications: The benefit of the paper is the construction of an early warning model for the Slovak electrotechnical industry. We constructed an appropriate model and evaluated the financial situation in the selected branch of industry.