Estimating probability of banking crises using random forest

Hartini, Sri; Rustam, Zuherman; Saragih, Glori Stephani; Segovia Vargas, María Jesús

doi:10.11591/ijai.v10.i2.pp407-413

Akses terbuka Jurnal SINTA 1

Estimating probability of banking crises using random forest

IAES International Journal of Artificial Intelligence · Vol. 10 No. 2 (2021) · hlm. 407–413

DOI: 10.11591/ijai.v10.i2.pp407-413
ISSN: 2252-8938, 2089-4872
Terbit: 2021-06-01
Penerbit: Institute of Advanced Engineering and Science
Bahasa: eng
Lisensi: creativecommons.org/licenses/by-sa/4.0

Abstrak

Banks have a crucial role in the financial system. When many banks suffer from the crisis, it can lead to financial instability. According to the impact of the crises, the banking crisis can be divided into two categories, namely systemic and non-systemic crisis. When systemic crises happen, it may cause even stable banks bankrupt. Hence, this paper proposed a random forest for estimating the probability of banking crises as prevention action. Random forest is well-known as a robust technique both in classification and regression, which is far from the intervention of outliers and overfitting. The experiments were then constructed using the financial crisis database, containing a sample of 79 countries in the period 1981-1999 (annual data). This dataset has 521 samples consisting of 164 crisis samples and 357 non-crisis cases. From the experiments, it was concluded that utilizing 90 percent of training data would deliver 0.98 accuracy, 0.92 sensitivity, 1.00 precision, and 0.96 F1-Score as the highest score than other percentages of training data. These results are also better than state-of-the-art methods used in the same dataset. Therefore, the proposed method is shown promising results to predict the probability of banking crises.

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11 penulis · 29 tautan kepenulisan bersama · 7 lintas organisasi

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University of Indonesia
Universidad Complutense de Madrid
Rumah Sakit Umum Pusat Nasional Dr. Cipto Mangunkusumo

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Rekan penulis	Organisasi	Artikel bersama
Ilsya Wirasati	University of Indonesia	8
Jane Eva Aurelia	University of Indonesia	6
Yasirly Amalia	University of Indonesia	3
Aldi Purwanto	University of Indonesia	3
Fildzah Zhafarina	University of Indonesia	3
Ajeng Maharani Putri	University of Indonesia	2
Jacub Pandelaki	Rumah Sakit Umum Pusat Nasional Dr. Cipto Mangunkusumo	2

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Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.

Metadata asli (oai_dc)

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	<dc:title xml:lang="en-US">Estimating probability of banking crises using random forest</dc:title>
	<dc:creator>Hartini, Sri</dc:creator>
	<dc:creator>Rustam, Zuherman</dc:creator>
	<dc:creator>Saragih, Glori Stephani</dc:creator>
	<dc:creator>Segovia Vargas, María Jesús</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Banking crises; Machine learning; Prediction of banking crises; Probability of banking crises; Random forest; Random forest regression</dc:subject>
	<dc:description xml:lang="en-US">Banks have a crucial role in the financial system. When many banks suffer from the crisis, it can lead to financial instability. According to the impact of the crises, the banking crisis can be divided into two categories, namely systemic and non-systemic crisis. When systemic crises happen, it may cause even stable banks bankrupt. Hence, this paper proposed a random forest for estimating the probability of banking crises as prevention action. Random forest is well-known as a robust technique both in classification and regression, which is far from the intervention of outliers and overfitting. The experiments were then constructed using the financial crisis database, containing a sample of 79 countries in the period 1981-1999 (annual data). This dataset has 521 samples consisting of 164 crisis samples and 357 non-crisis cases. From the experiments, it was concluded that utilizing 90 percent of training data would deliver 0.98 accuracy, 0.92 sensitivity, 1.00 precision, and 0.96 F1-Score as the highest score than other percentages of training data. These results are also better than state-of-the-art methods used in the same dataset. Therefore, the proposed method is shown promising results to predict the probability of banking crises.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2021-06-01</dc:date>
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	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/20880</dc:identifier>
	<dc:identifier>10.11591/ijai.v10.i2.pp407-413</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 10, No 2: June 2021; 407-413</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v10.i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/20880/13121</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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