Data Mining from the Banking Sector´s Data

Dolovanie dát z bankového sektora

Anna Biceková and Ľudmila Pusztová

Acta Informatica Pragensia, 2019, vol. 2019, issue 1, 18-37

Abstract: This paper deals with the prediction of company bankruptcies and defines how this undesirable state can be prevented. Currently, these methods include modern approaches from the area of data mining that can help companies in many ways. In a practical application of data mining methods for predicting the future state of a company, financial indicators of Polish companies were used. In the analyses, we used algorithms suitable for bankruptcy prediction - decision trees that provide a simple interpretation of results. In some experiments, we also used attribute selection methods, LASSO, or the PCA method. The workflow is governed by the CRISP-DM methodology, which describes the important steps needed for different analytical tasks. Part of the article is an analysis of the current state, which presents solutions to this problem suggested by other authors. After evaluating all models, we concluded that the C5.0 algorithm is capable of predicting a company's bankruptcy or non-bankruptcy with 97.07 % accuracy, without the use of attribute selection methods.

Keywords: Bankruptcy prediction; Data mining; CRISP-DM methodology; Decision trees; Predikcia bankrotov; dolovanie v dátach; CRIPS-DM metodológia; rozhodovacie stromy (search for similar items in EconPapers)
Date: 2019
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DOI: 10.18267/j.aip.123

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