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Title:	Smart Microfinance Platform Service for Migrant Workers
Authors:	蔡瑞煌 Tsaih, Rua-Huan;Tsao, Li-Ling
Contributors:	資管系
Keywords:	Microfinance;Artificial neural network;P2P lending platform
Date:	2023-06
Issue Date:	2024-02-16 15:02:39 (UTC+8)
Abstract:	The disruptive technology-enabled innovation in financial services has made banking no longer only in the hands of banks. Everyone should be included in the financial management framework, especially those who are not eligible for traditional financial services, according to the inclusive finance concept. The rise of emergent Fintech enables the Peer-to-Peer (P2P) microfinance platform, which has changed the traditional banking business model. In 2019, there were over 169 million migrant workers worldwide. They go to work in other countries, making a significant economic contribution to both their mother and working countries. However, when migrant workers have extra financial needs, like other economically underprivileged people, they do not easy to get access to credit loans from local financial institutions, but borrow money from other friendly sources (e.g., relatives, acquaintances, or unlicensed lenders) or from unfriendly underground channels with a very high-interest rate. We establish a legal and user-friendly smart microfinance platform for Taiwan's migrant workers to meet their needs in a timely and costsaving manner. In this platform, we use Single hidden layer feed-forward neural network (SLFN) to develop predictive models for assessing the credit score and default risks of migrant workers in their application for micro-credit loans.
Relation:	2023 IEEE/ACIS 23rd International Conference on Computer and Information Science (ICIS), IEEE, pp.103-108
Data Type:	conference
DOI link:	https://doi.org/10.1109/ICIS57766.2023.10210228
DOI:	10.1109/ICIS57766.2023.10210228
Appears in Collections:	[Department of MIS] Proceedings

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