Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

Pereira, José Manuel; Basto, Mário; Silva, Amélia Ferreira da

doi:10.5539/jms.v4n1p114

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…It is imperative to recognize that each of the discussed models has advantages and drawbacks. Consequently, there is no universally superior technique, and selecting an appropriate model hinges on individual circumstances and one's conception of business failure, aligning with Pereira et al [33].…”

Section: Financial Distress Prediction Modelsmentioning

confidence: 98%

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

Silva,

Brito,

Lourenço

et al. 2023

Sustainability

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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.

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Section: Financial Distress Prediction Modelsmentioning

confidence: 98%

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

Silva,

Brito,

Lourenço

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…Project failures are a common issue faced by many companies. Several factors contribute to these failures, including inadequate planning, deficient project management, and unmet business requirements [15]. Another prevalent issue is the generation of inaccurate or irrelevant information by the systems [16].…”

Section: Introductionmentioning

confidence: 99%

Information Quality of Business Intelligence Systems: A Maturity-based Assessment

Ait Touil,

Jabraoui

2023

J. Inf. Syst. Eng. Bus. Intell.

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Background: The primary role of a Business Intelligence (BI) system is to provide information to decision-makers within an organization. Moreover, it is crucial to acknowledge that the quality of this information is of greatest significance. Several studies have extensively discussed the importance of information quality in information systems, including BI. However, there is relatively little discussion on the factors influencing 'Information quality”. Objective: This study aimed to address this literature gap by investigating the determinants of BI maturity that impacted information quality. Methods: A maturity model comprising three dimensions was introduced, namely Data quality, BI infrastructure, and Data-driven culture. Data were collected from 84 companies and were analyzed using the SEM-PLS approach. Results: The analysis showed that maturity had a highly positive influence on Information Quality, validating the relevance of the three proposed determinant factors. Conclusion: This study suggested and strongly supported the importance and relevance of Data quality, BI infrastructure, and Data-driven culture as key dimensions of BI maturity. The robust statistical relationship between maturity and information quality showed the effectiveness of approaching the systems from a maturity perspective. This investigation paved the way for exploring additional dimensions that impact Information quality. Keywords: BI infrastructure, BI maturity, Data-driven culture, Data quality, Information quality.

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Association Rules as a Decision Making Model in the Textile Industry

Istrat

Lalic

2017

Fibres and Textiles in Eastern Europe

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Sales process disfunctions in the textile industry are problems that cause loss of customers, incomplete market supply, etc. The objective of the research is to analyse transactions from the textile industry database in order to find patterns in buyers’ behavior and improve the model of decision-making. Association rules, one of the most noticeable data mining techniques, is used as methodology to learn rules and market patterns that occur in sales in the textile industry, which will enhance the decision-making process, by making it more effective and efficient. The Apriori algorithm was applied and open source software Orange was used. It has been shown using a real-life dataset containing 2000 transactions from the textile industry of the South East Europe region that the approach proposed is useful in discovering effective knowledge in data associated with sales. The study reports new interesting rules and the dependence of the following parameters: Support, Confidence, Lift and Leverage on making more customized offers in the textile industry.

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Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

Cited by 3 publications

References 29 publications

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

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

Information Quality of Business Intelligence Systems: A Maturity-based Assessment

Association Rules as a Decision Making Model in the Textile Industry

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