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Using the Artificial Neural Network (ANN) to assess bank credit risk: a case study of Indonesia
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 m...
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| Main Authors: | , , , |
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| Format: | Default Preprint |
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2008
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| Online Access: | https://hdl.handle.net/2134/4180 |
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| _version_ | 1818175176913715200 |
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| author | Maximilian Hall Dadang Muljawan Suprayogi Lolita Moorena |
| author_facet | Maximilian Hall Dadang Muljawan Suprayogi Lolita Moorena |
| author_sort | Maximilian Hall (1247469) |
| collection | Figshare |
| description | 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. |
| format | Default Preprint |
| id | rr-article-9493226 |
| institution | Loughborough University |
| publishDate | 2008 |
| record_format | Figshare |
| spelling | rr-article-94932262008-01-01T00:00:00Z Using the Artificial Neural Network (ANN) to assess bank credit risk: a case study of Indonesia Maximilian Hall (1247469) Dadang Muljawan (7195571) Suprayogi (7195748) Lolita Moorena (7195751) Other economics not elsewhere classified Default risk Artificial neural network Bayesian regularization Transition matrix Economics not elsewhere classified 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. 2008-01-01T00:00:00Z Text Preprint 2134/4180 https://figshare.com/articles/preprint/Using_the_Artificial_Neural_Network_ANN_to_assess_bank_credit_risk_a_case_study_of_Indonesia/9493226 CC BY-NC-ND 4.0 |
| spellingShingle | Other economics not elsewhere classified Default risk Artificial neural network Bayesian regularization Transition matrix Economics not elsewhere classified Maximilian Hall Dadang Muljawan Suprayogi Lolita Moorena Using the Artificial Neural Network (ANN) to assess bank credit risk: a case study of Indonesia |
| title | Using the Artificial Neural Network (ANN) to assess bank credit risk: a case study of Indonesia |
| title_full | Using the Artificial Neural Network (ANN) to assess bank credit risk: a case study of Indonesia |
| title_fullStr | Using the Artificial Neural Network (ANN) to assess bank credit risk: a case study of Indonesia |
| title_full_unstemmed | Using the Artificial Neural Network (ANN) to assess bank credit risk: a case study of Indonesia |
| title_short | Using the Artificial Neural Network (ANN) to assess bank credit risk: a case study of Indonesia |
| title_sort | using the artificial neural network (ann) to assess bank credit risk: a case study of indonesia |
| topic | Other economics not elsewhere classified Default risk Artificial neural network Bayesian regularization Transition matrix Economics not elsewhere classified |
| url | https://hdl.handle.net/2134/4180 |