Predicting loan default in peer‐to‐peer lending using narrative data

Online loans are an instant method to get loans with technology basis and under control of the Financial Services Authority. Startups organizer have failed to protect consumers personal information thus creates problem in collecting debts."This study aims to explain and analyze" the”Legal Position of the Debt Collector in the administration of fintech and the legal consequences of the act of suppressing payments to consumers who fail to pay unlawfully.”This type of research used is normative juridical conducted with the approach of existing laws and regulations in Indonesia. Based on the research results obtained, there is a relationship or position of a third party with an online loan provider as a debt collector in a loan default, and this is explicitly explained in the P2P Lending fintech service delivery guidelines. "The legal consequences of the act of suppressing payments in the form of distribution" consumer personal data from the debt collector of the party organizing P2P Lending where "the consumer has the right to get legal protection through the filing of a claim of loss" arising as well as the organizer may be subject to administrative sanctions for his negligence. Pinjaman online ialah pinjaman cepat berbasis teknologi yang diawasi oleh OJK, beberapa penyelenggara telah lalai dalam menjaga data pribadi konsumen sehingga menimbulkan permasalahan dalam penagihan hutang kepada konsumen. Penelitian ini bertujuan untuk menjelaskan dan menganalisis Kedudukan Hukum Debt collector dalam penyelenggaraan fintech dan akibat hukum terhadap tindakan menekan pembayaran kepada konsumen gagal bayar dengan cara melawan hukum”. Jenis Penelitian yang dipergunakan ialah yuridis normatif yang dilakukan dengan pendekatan peraturan perundang-undangan yang ada di Indonesia. Berdasarkan hasil penelitian yang didapat yakni adapun hubungan atau kedudukan pihak ketiga dengan penyelenggara pinjaman online adalah sebagai penagih hutang dalam pinjaman gagal bayar dan hal tersebut dijelaskan secara eksplisit dalam pedoman perilaku pemberian layanan fintech Peer to Peer Lending (P2P Lending).”Adapun akibat hukum terhadap tindakan menekan pembayaran berupa penyebaran data pribadi konsumen dari debt collector pihak penyelenggara P2P Lending dimana konsumen berhak mendapat perlindungan hukum melalui pengajuan tuntutan kerugian yang timbul serta pihak penyelenggara dapat dikenakan sanksi administratif atas tindakan kelalaiannya.

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SOCIAL MEDIA DATA TO DETERMINE LOAN DEFAULT PREDICTING METHOD IN AN ISLAMIC ONLINE PEER TO PEER LENDING

Journal of Islamic Monetary Economics and Finance ◽

10.21098/jimf.v6i2.1184 ◽

2020 ◽

Vol 6 (2) ◽

Author(s):

Hasna Nabila Laila Khilfah ◽

Taufik Faturohman

Keyword(s):

Social Media ◽

Peer To Peer ◽

Default Probability ◽

Major Type ◽

Significant Variable ◽

Loan Default ◽

Social Media Data ◽

Peer Lending ◽

Media Data ◽

Financial Technology

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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