Modeling credit scoring using neural network ensembles

Chih Fong Tsai, Chihli Hung

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

Purpose – Credit scoring is important for financial institutions in order to accurately predict the likelihood of business failure. Related studies have shown that machine learning techniques, such as neural networks, outperform many statistical approaches to solving this type of problem, and advanced machine learning techniques, such as classifier ensembles and hybrid classifiers, provide better prediction performance than single machine learning based classification techniques. However, it is not known which type of advanced classification technique performs better in terms of financial distress prediction. The paper aims to discuss these issues. Design/methodology/approach – This paper compares neural network ensembles and hybrid neural networks over three benchmarking credit scoring related data sets, which are Australian, German, and Japanese data sets. Findings – The experimental results show that hybrid neural networks and neural network ensembles outperform the single neural network. Although hybrid neural networks perform slightly better than neural network ensembles in terms of predication accuracy and errors with two of the data sets, there is no significant difference between the two types of prediction models. Originality/value – The originality of this paper is in comparing two types of advanced classification techniques, i.e. hybrid and ensemble learning techniques, in terms of financial distress prediction.

Original languageEnglish
Pages (from-to)1114-1123
Number of pages10
JournalKybernetes
Volume43
Issue number7
DOIs
StatePublished - 29 Jul 2014

Keywords

  • Bankruptcy prediction
  • Classifier ensemble
  • Credit scoring
  • Hybrid classifier
  • Machine learning

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