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Predicting Business Bankruptcy: A Comprehensive Case Study

Predicting Business Bankruptcy: A Comprehensive Case Study

Rui Sarmento, Luís Trigo, Liliana Fonseca
Copyright: © 2016 |Volume: 5 |Issue: 2 |Pages: 18
ISSN: 2155-6334|EISSN: 2155-6342|EISBN13: 9781466693012|DOI: 10.4018/IJSODIT.2016070105
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MLA

Sarmento, Rui, et al. "Predicting Business Bankruptcy: A Comprehensive Case Study." IJSODIT vol.5, no.2 2016: pp.48-65. http://doi.org/10.4018/IJSODIT.2016070105

APA

Sarmento, R., Trigo, L., & Fonseca, L. (2016). Predicting Business Bankruptcy: A Comprehensive Case Study. International Journal of Social and Organizational Dynamics in IT (IJSODIT), 5(2), 48-65. http://doi.org/10.4018/IJSODIT.2016070105

Chicago

Sarmento, Rui, Luís Trigo, and Liliana Fonseca. "Predicting Business Bankruptcy: A Comprehensive Case Study," International Journal of Social and Organizational Dynamics in IT (IJSODIT) 5, no.2: 48-65. http://doi.org/10.4018/IJSODIT.2016070105

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Abstract

Managers, investors, financial institutions and government agencies have a major concern on forecasting enterprise bankruptcy. It enables the sustainability assessment of critical suppliers and clients, as well as competitors and the business environment. Throughout the 20th and the 21st century, advances in statistics and computer science fields enabled the development of different trends in financial distress assessment that co-exist today. However, recent Data Mining (DM) techniques are regarded as being the most precise. IT expertise requirements in the constantly evolving DM field may have been a major obstacle to the adoption of these techniques by decision makers. Furthermore, DM software tools that are now widespread offer a broad spectrum of Artificial Intelligence algorithms and the most difficult task may be the decision of selecting the appropriate algorithm. Hence, the adoption of a good workflow method for data processing and analysis is critical for having fast and reliable results. This work presents an overview of the available bankruptcy techniques and provides a comprehensive case study exploring the latest Data Mining techniques.

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