Article2014

Modeling credit scoring using neural network ensembles

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Financial Distress and Bankruptcy Prediction Forecasting Techniques and Applications Imbalanced Data Classification Techniques

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.

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References

1997A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting 1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY 1984Methodological Issues Related to the Estimation of Financial Distress Prediction Models 2006Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review 1994Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)2003Credit rating analysis with support vector machines and neural networks: a market comparative study 1990A neural network model for bankruptcy prediction 2004An application of support vector machines in bankruptcy prediction model

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