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

Maximilian Hall, Dadang Muljawan (), Suprayogi () and Lolita Moorena ()
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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

Discussion Paper Series from Department of Economics, Loughborough University

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.

Keywords: default risk; artificial neural network; Bayesian regularization; transition matrix. (search for similar items in EconPapers)
JEL-codes: C11 C63 E25 E27 G32 (search for similar items in EconPapers)
Date: 2008-07, Revised 2008-07
New Economics Papers: this item is included in nep-cmp, nep-fmk, nep-mac, nep-rmg and nep-sea
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