IEEE - Institute of Electrical and Electronics Engineers, Inc. - Modeling consumer loan default prediction using ensemble neural networks

2013 International Conference on Computing, Electrical and Electronics Engineering (ICCEEE)

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In this paper, a loan default prediction model is constricted using three different training algorithms, to train a supervised two-layer feed-forward network to produce the prediction model. But first, two attribute filtering functions were used, resulting in two data sets with reduced attributes and the original data-set. Back propagation based learning algorithms was used for training the network. The neural networks are trained using real world credit application cases from a German bank datasets which has 1000 cases; each case with 24 numerical attributes; upon, which the decision is based. The aim of this paper was to compare between the resulting models produced from using different training algorithms, scaled conjugate gradient backpropagation, Levenberg-Marquardt algorithm, One-step secant backpropagation (SCG, LM and OSS) and an ensemble of SCG, LM and OSS. Empirical results indicate that training algorithms improve the design of a loan default prediction model and ensemble model works better than the individual models.

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Author(s):	Amira Kamil Ibrahim Hassan ; Ajith Abraham
Publisher:	IEEE - Institute of Electrical and Electronics Engineers, Inc.
Publication Date:	1 August 2013
Conference Location:	Khartoum, Sudan
Conference Date:	26 August 2013
Page(s):	719 - 724
ISBN (CD):	978-1-4673-6231-3
ISBN (Electronic):	978-1-4673-6232-0
DOI:	10.1109/ICCEEE.2013.6634029