Social Media Data to Determine Loan Default Predicting Method in an Islamic Online P2P Lending

Khilfah, Hasna Nabila Laila; Faturohman, Taufik

doi:10.21098/jimf.v6i2.1184

Jurnal SINTA 1

Social Media Data to Determine Loan Default Predicting Method in an Islamic Online P2P Lending

Journal of Islamic Monetary Economics and Finance · Vol. 6 No. 2 (2020) · hlm. 243–274

DOI: 10.21098/jimf.v6i2.1184
ISSN: 2460-6618, 2460-6146
Terbit: 2020-05-21
Penerbit: Bank Indonesia
Bahasa: eng

Abstrak

Currently, financial technology is growing rapidly in Indonesia. One of financial technology major type is online peer to peer lending platform. Islamic online peer to peer lending is also emerging. However, credit risk still a major concern for this platform. In order to address this issue, social media assessment is developed. Therefore, in this paper, authors aimed to identify social media variables that could be used as default probability predictors and to determine predictability level by added social media data to the model. Six independent variables consist of social media data and seven control variables from historical payment and demographic data are used to construct credit scorecard and logistic. The result identifies five variables that could be considered and used as default probability predictor which are Posting Frequency in Midnight, Followers, Following, Employment, and Tenor. Interestingly, number of religion accounts followed in Instagram is not a significant variable. Furthermore, the model with selected variables through the combination of demographic, historical payment, and social media data could increase the predictability level by 6.6%.

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Bandung Institute of Technology

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Rekan penulis	Organisasi	Artikel bersama
Yulianti Yulianti	Bandung Institute of Technology	2
Salma Fadhilah Widityani	Bandung Institute of Technology	1
Raden Aswin Rahadi	Bandung Institute of Technology	1
Yulianti Yulianti	Bandung Institute of Technology	1
Irfan Hassandi	Bandung Institute of Technology	1
Teguh Nugraha	Bandung Institute of Technology	1
Muhamad Anindya Hiroshi Purbayanto	Bandung Institute of Technology	1
Arson Aliludin	Bandung Institute of Technology	1

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Sumber metadata: OAI-PMH (metadata jurnal) http://www.jimf-bi.org/index.php/JIMF/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.

Metadata asli (oai_dc)

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	<dc:title xml:lang="en">Social Media Data to Determine Loan Default Predicting Method in an Islamic Online P2P Lending</dc:title>
	<dc:creator xml:lang="en">Khilfah, Hasna Nabila Laila</dc:creator>
	<dc:creator xml:lang="en">Faturohman, Taufik</dc:creator>
	<dc:subject xml:lang="en">Credit scoring</dc:subject>
	<dc:subject xml:lang="en">Islamic online peer to peer lending</dc:subject>
	<dc:subject xml:lang="en">Fintech</dc:subject>
	<dc:subject xml:lang="en">Indonesia</dc:subject>
	<dc:description xml:lang="en">Currently, financial technology is growing rapidly in Indonesia. One of financial technology major type is online peer to peer lending platform. Islamic online peer to peer lending is also emerging. However, credit risk still a major concern for this platform. In order to address this issue, social media assessment is developed. Therefore, in this paper, authors aimed to identify social media variables that could be used as default probability predictors and to determine predictability level by added social media data to the model. Six independent variables consist of social media data and seven control variables from historical payment and demographic data are used to construct credit scorecard and logistic. The result identifies five variables that could be considered and used as default probability predictor which are Posting Frequency in Midnight, Followers, Following, Employment, and Tenor. Interestingly, number of religion accounts followed in Instagram is not a significant variable. Furthermore, the model with selected variables through the combination of demographic, historical payment, and social media data could increase the predictability level by 6.6%.</dc:description>
	<dc:publisher xml:lang="en">Bank Indonesia</dc:publisher>
	<dc:date>2020-05-21</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://jimf-bi.org/JIMF/article/view/1184</dc:identifier>
	<dc:identifier>10.21098/jimf.v6i2.1184</dc:identifier>
	<dc:source xml:lang="en">Journal of Islamic Monetary Economics and Finance; Vol. 6 No. 2 (2020); 243 - 274</dc:source>
	<dc:source>2460-6618</dc:source>
	<dc:source>2460-6146</dc:source>
	<dc:source>10.21098/jimf.v6i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jimf-bi.org/JIMF/article/view/1184/812</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2020 Taufik Faturohman, Hasna Nabila Laila Khilfah</dc:rights>
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