African Journal of
Agricultural Research

  • Abbreviation: Afr. J. Agric. Res.
  • Language: English
  • ISSN: 1991-637X
  • DOI: 10.5897/AJAR
  • Start Year: 2006
  • Published Articles: 6848

Full Length Research Paper

Evaluation of insolvency in mutual credit unions by application of the data mining using decision trees approach

Isabel Cristina Gozer
  • Isabel Cristina Gozer
  • Paranaense University ? UNIPAR, Umuarama, Parana, Brazil.
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Antonio Roberto Pereira Leite de Albuquerque
  • Antonio Roberto Pereira Leite de Albuquerque
  • Departamento de informatica em saude, Universidade Federal de Sao Paulo - Escola Paulista de Medicina ? UNIFESP-EPM, Sao Paulo, Brazil.
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Sadao Isotani
  • Sadao Isotani
  • Instituto de Fisica da Universidade Estadual de Sao Paulo ? IFUSP, Sao Paulo, Brazil.
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Regio Marcio Toesca Gimenes
  • Regio Marcio Toesca Gimenes
  • Paranaense University ? UNIPAR, Umuarama, Parana, Brazil.
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Willian Heitor Moreira
  • Willian Heitor Moreira
  • MBA em Controladoria Gestao Empresarial e Financeira ? Unipar, Umuarama, Parana, Brazil.
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Odair Alberton
  • Odair Alberton
  • Postgraduate Program in Biotechnology Applied to Agriculture; Paranaense University ? UNIPAR, Umuarama, Parana, Brazil.
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Emilio Araujo Menezes6
  • Emilio Araujo Menezes6
  • Universidade Federal de Santa Catarina, Santa Catarina, Brazil.
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  •  Received: 06 May 2015
  •  Published: 23 July 2015

Abstract

This study aimed to present an evaluation of the insolvency of mutual credit unions in the Paraná State (Brazil) by application of the data mining using decision trees approach. The information required to build the models were obtained from indicators applied to a sample of 62 mutual credit unions from which 31 are solvent and 31 are insolvent. The selection of indicators was made based on the PEARLS system, whose efficacy refers to the World Council of Credit Unions (WOCCU). The decision trees were built by training the J48, ADTree and LADTree algorithms. After the analysis of results, the best performance was observed for the ADTree algorithm. According to the Kappa statistics, its acceptance level was excellent. In addition to the evaluation of performance of the decision trees, the paths with the highest confidence levels for assessing insolvency was identified by the A3 indicator (Net Institutional and Transitory Capital + Non-Interest-bearing Liabilities/ Non-earning Assets) (> 0.052), this value indicate that the cooperative is solvent. The confidence level was set at 1.953 and the path is represented on the second node of the tree.
 
Key words: Insolvency, credit unions, data mining, decision trees.