Sustainability of Transport Sector Companies: Bankruptcy Prediction Based on Artificial Intelligence

Abstract

Understanding business failure within the transport industry is crucial for formulating an effective competitive policy. Acknowledging the pivotal role of financial stability as a cornerstone of sustainability, this study undertakes a comparative investigation between statistical models forecasting business failure and artificial intelligence-based models within the context of the transport sector. The analysis spans the temporal period from 2014 to 2021 and encompasses a dataset of 4866 companies from four South European countries: Portugal, Spain, France, and Italy. The models created were linear support vector machines (L-SVMs), kernel support vector machines (K-SVMs), k-nearest neighbors (k-NNs), logistic regression (LR), decision trees (DTs), random forests (RFs), extremely random forests (ERFs), AdaBoost, and neural networks (NNs). The models were implemented in Python using the scikit-learn package. The results revealed that most models exhibited high precision and accuracy, ranging from 71% to 73%, with the ERF model outperforming others in both predictive capacity and accuracy. It was also observed that artificial intelligence-based models outperformed statistical models in predicting business failure, with particular emphasis on the AdaBoost and ERF models. Thus, we conclude that the results confirm the hypothesis that the artificial intelligence models were superior in all metrics compared to the results obtained by logistic regression.