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Using The Artificial Neural Network (ANN) to Assess Bank Credit Risk: A Case Study of Indonesia

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Author Info

  • Maximilian J. B. Hall

    ()
    (Dept of Economics, Loughborough University)

  • Dadang Muljawan

    ()
    (Central Bank of Indonesia)

  • Suprayogi

    ()
    (Industrial Engineering Program, Bandung Institute of Technology, Indonesia)

  • Lolita Moorena

    ()
    (Central Bank of Indonesia Internship program, Bandung Institute of Technology, Indonesia)

Abstract

Ever since the Asian Financial Crisis, concerns have risen over whether policy-makers have sufficient tools to maintain financial stability. The ability to predict financial disturbances enables the authorities to take precautionary action to minimize their impact. In this context, the authorities may use any financial indicators which may accurately predict shifts in the quality of bank exposures. This paper uses key macro-economic variables (i.e. GDP growth, the inflation rate, stock prices, the exchange rates, and money in circulation) to predict the default rate of the Indonesian Islamic banks’ exposures. The default rates are forecasted using the Artificial Neural Network (ANN) methodology, which incorporates the Bayesian Regularization technique. From the sensitivity analysis, it is shown that stock prices could be used as a leading indicator of future problem.

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File URL: http://www.lboro.ac.uk/departments/ec/RePEc/lbo/lbowps/CreditRisk-Using-ANN.pdf
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Bibliographic Info

Paper provided by Department of Economics, Loughborough University in its series Discussion Paper Series with number 2008_06.

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Date of creation: Jul 2008
Date of revision: Jul 2008
Handle: RePEc:lbo:lbowps:2008_06

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Keywords: default risk; artificial neural network; Bayesian regularization; transition matrix.;

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  1. Linda Allen & Anthony Saunders, 2003. "A survey of cyclical effects in credit risk measurement model," BIS Working Papers 126, Bank for International Settlements.
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