Acta Informatica Pragensia 2019, 8(1), 18-37 | DOI: 10.18267/j.aip.1232938

Dolovanie dát z bankového sektora

Anna Biceková, Ľudmila Pusztová
Department of Cybernetics and Artificial Intelligence, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letná 9, 042 00 Košice, Slovak Republic

Predkladaný príspevok sa zaoberá problematikou bankrotov podnikov a definuje spôsoby akými je možné tomuto nežiadúcemu stavu predísť. V súčasnosti medzi tieto spôsoby patria hlavne moderné prístupy z oblasti získavania znalostí a dolovania v dátach, ktoré podnikom dokážu pomôcť v mnohých smeroch. V rámci praktickej aplikácie metód dolovania v dátach s cieľom predikovať budúci stav podniku, boli použité dáta finančných ukazovateľov poľských spoločností. V predkladanom článku sme využili algoritmy vhodné na predikciu bankrotov – rozhodovacie stromy, ktoré poskytujú jednoduchú interpretáciu výsledkov. V niektorých experimentoch sme využili aj metódy výberu atribútov, LASSO alebo PCA metódu. Postup práce sa riadi metodológiou CRISP-DM, ktorá ponúka popis dôležitých krokov potrebných pri rôznych analytických úlohách. Súčasťou článku je aj analýza súčasného stavu, ktorá predstavuje riešenia danej problematiky inými autormi. Po vyhodnotení všetkých modelov sme dospeli k záveru, že algoritmus C5.0 je na 97,07 % schopný predikovať zbankrotovanie respektíve nezbankrotovanie podniku, pričom použitie metód výberu atribútov nebolo potrebné.

Keywords: Predikcia bankrotov, dolovanie v dátach, CRIPS-DM metodológia, rozhodovacie stromy

Data Mining from the Banking Sector´s Data

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

Received: May 20, 2019; Accepted: June 28, 2019; Prepublished online: July 2, 2019; Published: July 10, 2019  Show citation

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Biceková, A., & Pusztová, Ľ. (2019). Data Mining from the Banking Sector´s Data. Acta Informatica Pragensia, 8(1), 18-37. doi: 10.18267/j.aip.123
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