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Big data and SME financing in China

Big Data is becoming more and more prevalent in recent years, and it attracts lots of attention from various perspectives of the world such as academia, industry, and even government. Big Data can be seen as the next-generation source of power for the economy. Today, Big Data represents a new way to...

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Main Authors: Sheikh Hassan, Ahmad Fahmi, Ab Razak, Nazrul Hisyam, Tian, Z.
Format: Article
Language:English
Published: IOP Publishing 2018
Online Access:http://psasir.upm.edu.my/id/eprint/73267/1/SME.pdf
http://psasir.upm.edu.my/id/eprint/73267/
https://iopscience.iop.org/article/10.1088/1742-6596/1018/1/012002/pdf
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spelling my.upm.eprints.732672020-11-30T07:56:52Z http://psasir.upm.edu.my/id/eprint/73267/ Big data and SME financing in China Sheikh Hassan, Ahmad Fahmi Ab Razak, Nazrul Hisyam Tian, Z. Big Data is becoming more and more prevalent in recent years, and it attracts lots of attention from various perspectives of the world such as academia, industry, and even government. Big Data can be seen as the next-generation source of power for the economy. Today, Big Data represents a new way to approach information and help all industry and business fields. The Chinese financial market has long been dominated by state-owned banks; however, these banks provide low-efficiency help toward small- and medium-sized enterprises (SMEs) and private businesses. The development of Big Data is changing the financial market, with more and more financial products and services provided by Internet companies in China. The credit rating models and borrower identification make online financial services more efficient than conventional banks. These services also challenge the domination of stateowned banks. IOP Publishing 2018 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/73267/1/SME.pdf Sheikh Hassan, Ahmad Fahmi and Ab Razak, Nazrul Hisyam and Tian, Z. (2018) Big data and SME financing in China. Journal of Physics: Conference Series, 1018. art. no. 012002. pp. 1-8. ISSN 1742-6588; ESSN: 1742-6596 https://iopscience.iop.org/article/10.1088/1742-6596/1018/1/012002/pdf 10.1088/1742-6596/1018/1/012002
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Big Data is becoming more and more prevalent in recent years, and it attracts lots of attention from various perspectives of the world such as academia, industry, and even government. Big Data can be seen as the next-generation source of power for the economy. Today, Big Data represents a new way to approach information and help all industry and business fields. The Chinese financial market has long been dominated by state-owned banks; however, these banks provide low-efficiency help toward small- and medium-sized enterprises (SMEs) and private businesses. The development of Big Data is changing the financial market, with more and more financial products and services provided by Internet companies in China. The credit rating models and borrower identification make online financial services more efficient than conventional banks. These services also challenge the domination of stateowned banks.
format Article
author Sheikh Hassan, Ahmad Fahmi
Ab Razak, Nazrul Hisyam
Tian, Z.
spellingShingle Sheikh Hassan, Ahmad Fahmi
Ab Razak, Nazrul Hisyam
Tian, Z.
Big data and SME financing in China
author_facet Sheikh Hassan, Ahmad Fahmi
Ab Razak, Nazrul Hisyam
Tian, Z.
author_sort Sheikh Hassan, Ahmad Fahmi
title Big data and SME financing in China
title_short Big data and SME financing in China
title_full Big data and SME financing in China
title_fullStr Big data and SME financing in China
title_full_unstemmed Big data and SME financing in China
title_sort big data and sme financing in china
publisher IOP Publishing
publishDate 2018
url http://psasir.upm.edu.my/id/eprint/73267/1/SME.pdf
http://psasir.upm.edu.my/id/eprint/73267/
https://iopscience.iop.org/article/10.1088/1742-6596/1018/1/012002/pdf
_version_ 1685580243158433792
score 13.211869

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