Artificial neural network to develop loan default predicting model using social media data: a case study of online peer to peer lending

Taufik Faturohman, Muhammad Abdullah Hamzah Syaiful Mukminin, Sudarso Kaderi Wiryono, Gun Gun Indrayana, Raden Aswin Rahadi and Kurnia Fajar Afgani

International Journal of Monetary Economics and Finance, 2023, vol. 16, issue 3/4, 252-260

Abstract: Online Peer-to-Peer Lending has been growing rapidly in Indonesia. A system called credit scoring, delineated by a high non-performing financing (NPF) indicator, is considered an important tool used by financial institutions to address concerns and evaluate loan applicants. To enhance credit scoring models, social media data is added to increase the predictability rate on credit scoring models. This paper evaluates credit scoring models using artificial neural networks (ANNs) method with multilayer perceptron (MLP) approach. Inclusion of social media data resulted in increases in the predictability rate by 15.8%, to 98.3%. Our results suggest that social media data addition improves efficiency and healthier portfolios in the alternative financing industry.

Keywords: ANN; artificial neural network; loan default predicting model; social media; online peer-to-peer lending. (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=131898 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijmefi:v:16:y:2023:i:3/4:p:252-260

Access Statistics for this article

More articles in International Journal of Monetary Economics and Finance from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().