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

Author

Listed:
  • 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.

Suggested Citation

  • Maximilian J. B. Hall & Dadang Muljawan & Suprayogi & Lolita Moorena, 2008. "Using The Artificial Neural Network (ANN) to Assess Bank Credit Risk: A Case Study of Indonesia," Discussion Paper Series 2008_06, Department of Economics, Loughborough University, revised Jul 2008.
    • Handle: RePEc:lbo:lbowps:2008_06
    as

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    File URL: http://www.lboro.ac.uk/departments/ec/RePEc/lbo/lbowps/CreditRisk-Using-ANN.pdf
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    References listed on IDEAS

    as
    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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    More about this item

    Keywords

    default risk; artificial neural network; Bayesian regularization; transition matrix.;
    All these keywords.

    JEL classification:

    • E25 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Aggregate Factor Income Distribution
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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