Development of fuzzified neural network for enterprise bankruptcy risk estimation

Artem Sinkovskyi () and Volodymyr Shulakov
Additional contact information
Artem Sinkovskyi: Cherkasy State Technological University
Volodymyr Shulakov: Cherkasy State Technological University

Technology audit and production reserves, 2024, vol. 3, issue 2(77), 19-22

Abstract: The object of this study is the assessment of the level of enterprise bankruptcy risk. It is a critical component in assessing the financial condition of an enterprise, and also serves as an indicator that allows the management team to reduce potential risks and develop their own strategies to strengthen the financial condition of the enterprise. One of the most challenging aspects of bankruptcy forecasting is the complex financial situations of bankrupt companies. By accurately predicting the risk of bankruptcy, businesses can take preventive measures to mitigate financial difficulties and ensure long-term sustainability. Previous methods, such as Altman's Z-score, are not accurate enough, as presented in the study. The paper investigates a modern approach to bankruptcy prediction based on a neural network with complex neural elements, namely neural arithmetic logic units (NALUs) and a custom phasing layer. This layer can process complex raw numerical values, such as financial indicators relevant to the analysis of a company's bankruptcy. Compared to Altman's Z-score, the developed method demonstrates a better F1 score in bankruptcy classification (48 %). On the raw data, the neural network demonstrates an improvement in the F1 score by about 40 % compared to the classical multilayer perceptron (MLP) with linear layers and nonlinear activation functions. A modern replacement for ReLU called Mish was used, which achieves better generalization. It was also assumed that the addition of new neural elements, which provide the neural network with arithmetic capabilities, contributes to the performance of processing non-normalized input data. This work highlights the importance of using advanced neural network architectures to improve the accuracy and reliability of forecasting in financial risk assessment. Using the parameters presented in the study, managers of enterprises will be able to more accurately assess the risk of bankruptcy.

Keywords: statistical model; bankruptcy risk assessment; neural arithmetic; machine learning (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://journals.uran.ua/tarp/article/download/306873/298399 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:baq:taprar:v:3:y:2024:i:2:p:19-22

DOI: 10.15587/2706-5448.2024.306873

Access Statistics for this article

More articles in Technology audit and production reserves from PC TECHNOLOGY CENTER
Bibliographic data for series maintained by Iryna Prudius ().