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Using the artificial neural network to assess bank credit risk: a case study of Indonesia

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  • Maximilian Hall
  • Dadang Muljawan
  • Lolita Moorena

Abstract

Ever since the Asian Financial Crisis, concerns have arisen 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 article uses key macro-economic variables (i.e. Gross Domestic Product (GDP) growth, the inflation rate, stock prices, 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 problems.

Suggested Citation

  • Maximilian Hall & Dadang Muljawan & Lolita Moorena, 2009. "Using the artificial neural network to assess bank credit risk: a case study of Indonesia," Applied Financial Economics, Taylor & Francis Journals, vol. 19(22), pages 1825-1846.
    • Handle: RePEc:taf:apfiec:v:19:y:2009:i:22:p:1825-1846
    DOI: 10.1080/09603100903018760
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    References listed on IDEAS

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    1. repec:zbw:bofrdp:2004_018 is not listed on IDEAS
    2. Til Schuermann & Yusuf Jafry, 2003. "Measurement and Estimation of Credit Migration Matrices," Center for Financial Institutions Working Papers 03-08, Wharton School Center for Financial Institutions, University of Pennsylvania.
    3. Linda Allen & Anthony Saunders, 2003. "A survey of cyclical effects in credit risk measurement model," BIS Working Papers 126, Bank for International Settlements.
    4. Philip Lowe, 2002. "Credit risk measurement and procyclicality," BIS Working Papers 116, Bank for International Settlements.
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    Cited by:

    1. Muhammad Nadim Hanif & Khurrum S. Mughal & Javed Iqbal, 2018. "A Thick ANN Model for Forecasting Inflation," SBP Working Paper Series 99, State Bank of Pakistan, Research Department.
    2. Asrin Karimi, 2014. "Credit Risk Modeling for Commercial Banks," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 4(3), pages 187-192, July.
    3. Asrin Karimi, 2014. "Evaluation of the Credit Risk with Statistical analysis," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 4(3), pages 206-211, July.
    4. Aditya Saxena & Dr Parizad Dungore, 2024. "Credit Risk Assessment Model for UAE Commercial Banks: A Machine Learning Approach," Papers 2407.12044, arXiv.org.
    5. repec:idn:journl:v:20:y:2018:i:3:p:1-18 is not listed on IDEAS

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