Stanislav Letkovsky

Applied Meters a.s.,

Prešov, Slovakia

E-mail: letkovsky@appliedmeters.sk

ORCID 0000-0001-9111-4940 

Predicting bankruptcy using artificial intelligence: The case of the engineering industry

Sylvia Jencova

University of Prešov,

Prešov, Slovakia 

E-mail: sylvia.jencova@unipo.sk 

ORCID 0000-0002-0736-0880

Petra Vasanicova

University of Prešov,

Prešov, Slovakia

E-mail: petra.vasanicova@unipo.sk

ORCID 0000-0001-7353-2057

Stefan Gavura

Technical University of Košice,

Košice, Slovakia

E-mail: stefan.gavura@tuke.sk

ORCID 0000-0001-5969-5597

Radovan Bacik

University of Prešov,

Prešov, Slovakia 

E-mail: radovan.bacik@unipo.sk 

ORCID 0000-0002-5780-3838

Abstract. 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.

Received: October, 2022

1st Revision: October, 2023

Accepted: December, 2023

DOI: 10.14254/2071-789X.2023/16-4/8

JEL Classification: C45, G33

Keywords: bankruptcy prediction, artificial neural network, support vector machine, logistic regression, engineering industry