SOCIAL MEDIA DATA TO DETERMINE LOAN DEFAULT PREDICTING METHOD IN AN ISLAMIC ONLINE PEER TO PEER LENDING
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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Bank Indonesia
2020-05-01
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| Series: | Journal of Islamic Monetary Economics and Finance |
| Subjects: | |
| Online Access: | https://jimf-bi.org/index.php/JIMF/article/view/1184 |
| _version_ | 1818427068050833408 |
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| author | Hasna Nabila Laila Khilfah Taufik Faturohman |
| author_facet | Hasna Nabila Laila Khilfah Taufik Faturohman |
| author_sort | Hasna Nabila Laila Khilfah |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-12-14T14:39:51Z |
| format | Article |
| id | doaj.art-cdb08481b4f5448f9e97adc92b55e578 |
| institution | Directory Open Access Journal |
| issn | 2460-6146 2460-6618 |
| language | English |
| last_indexed | 2024-12-14T14:39:51Z |
| publishDate | 2020-05-01 |
| publisher | Bank Indonesia |
| record_format | Article |
| series | Journal of Islamic Monetary Economics and Finance |
| spelling | doaj.art-cdb08481b4f5448f9e97adc92b55e5782022-12-21T22:57:27ZengBank IndonesiaJournal of Islamic Monetary Economics and Finance2460-61462460-66182020-05-016210.21098/jimf.v6i2.11841184SOCIAL MEDIA DATA TO DETERMINE LOAN DEFAULT PREDICTING METHOD IN AN ISLAMIC ONLINE PEER TO PEER LENDINGHasna Nabila Laila Khilfah0Taufik Faturohman1SBM Institut Teknologi Bandung, IndonesiaSBM Institut Teknologi Bandung, IndonesiaCurrently, 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%.https://jimf-bi.org/index.php/JIMF/article/view/1184credit scoringislamic online peer to peer lendingfintechindonesia |
| spellingShingle | Hasna Nabila Laila Khilfah Taufik Faturohman SOCIAL MEDIA DATA TO DETERMINE LOAN DEFAULT PREDICTING METHOD IN AN ISLAMIC ONLINE PEER TO PEER LENDING Journal of Islamic Monetary Economics and Finance credit scoring islamic online peer to peer lending fintech indonesia |
| title | SOCIAL MEDIA DATA TO DETERMINE LOAN DEFAULT PREDICTING METHOD IN AN ISLAMIC ONLINE PEER TO PEER LENDING |
| title_full | SOCIAL MEDIA DATA TO DETERMINE LOAN DEFAULT PREDICTING METHOD IN AN ISLAMIC ONLINE PEER TO PEER LENDING |
| title_fullStr | SOCIAL MEDIA DATA TO DETERMINE LOAN DEFAULT PREDICTING METHOD IN AN ISLAMIC ONLINE PEER TO PEER LENDING |
| title_full_unstemmed | SOCIAL MEDIA DATA TO DETERMINE LOAN DEFAULT PREDICTING METHOD IN AN ISLAMIC ONLINE PEER TO PEER LENDING |
| title_short | SOCIAL MEDIA DATA TO DETERMINE LOAN DEFAULT PREDICTING METHOD IN AN ISLAMIC ONLINE PEER TO PEER LENDING |
| title_sort | social media data to determine loan default predicting method in an islamic online peer to peer lending |
| topic | credit scoring islamic online peer to peer lending fintech indonesia |
| url | https://jimf-bi.org/index.php/JIMF/article/view/1184 |
| work_keys_str_mv | AT hasnanabilalailakhilfah socialmediadatatodetermineloandefaultpredictingmethodinanislamiconlinepeertopeerlending AT taufikfaturohman socialmediadatatodetermineloandefaultpredictingmethodinanislamiconlinepeertopeerlending |