中国中小企业大数据驱动的信用报告框架

Yunchuan Sun, Chunlei Li, Xuegang Cui, Guangzhi Zhang, Xiaoping Zeng, Xueying Chang, Dengbiao Tu, Yongping Xiong
{"title":"中国中小企业大数据驱动的信用报告框架","authors":"Yunchuan Sun, Chunlei Li, Xuegang Cui, Guangzhi Zhang, Xiaoping Zeng, Xueying Chang, Dengbiao Tu, Yongping Xiong","doi":"10.1109/IIKI.2016.46","DOIUrl":null,"url":null,"abstract":"SMEs (Small and Medium-size Enterprises) in China always face financing constraints and hardly obtain bank loans under unsound financing system. In external f Academic literature has shown that widespread information asymmetry may prevent the efficient allocation of lending, leading to credit rationing. Currently, most credit reporting, models for SMEs in China are primarily based on hard information about the enterprises and their owners but lack comprehensive evaluation based on the combination with soft information. To bridge the gap for SMEs, we propose a novel big-data-driven credit reporting framework which presents a new credit reporting system by including big data in business, finance, and social networks. The proposed approach features in capturing diversified data online, conducting evaluation and analysis in real-time, and automatically generating online credit reports for SMEs, banks, and government. It also provides an efficient interactive way for SMEs to check credit reports online.","PeriodicalId":371106,"journal":{"name":"2016 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A Novel Big-Data-Driven Credit Reporting Framework for SMEs in China\",\"authors\":\"Yunchuan Sun, Chunlei Li, Xuegang Cui, Guangzhi Zhang, Xiaoping Zeng, Xueying Chang, Dengbiao Tu, Yongping Xiong\",\"doi\":\"10.1109/IIKI.2016.46\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"SMEs (Small and Medium-size Enterprises) in China always face financing constraints and hardly obtain bank loans under unsound financing system. In external f Academic literature has shown that widespread information asymmetry may prevent the efficient allocation of lending, leading to credit rationing. Currently, most credit reporting, models for SMEs in China are primarily based on hard information about the enterprises and their owners but lack comprehensive evaluation based on the combination with soft information. To bridge the gap for SMEs, we propose a novel big-data-driven credit reporting framework which presents a new credit reporting system by including big data in business, finance, and social networks. The proposed approach features in capturing diversified data online, conducting evaluation and analysis in real-time, and automatically generating online credit reports for SMEs, banks, and government. It also provides an efficient interactive way for SMEs to check credit reports online.\",\"PeriodicalId\":371106,\"journal\":{\"name\":\"2016 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)\",\"volume\":\"66 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IIKI.2016.46\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IIKI.2016.46","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

中国的中小企业在不健全的融资体系下,一直面临融资约束,难以获得银行贷款。外部学术文献表明,普遍存在的信息不对称可能会阻碍贷款的有效配置,从而导致信贷配给。目前,中国大多数中小企业信用报告模型主要基于企业及其所有者的硬信息,缺乏与软信息相结合的综合评价。为了弥补中小企业的差距,我们提出了一种新的大数据驱动的信用报告框架,该框架通过将商业、金融和社交网络中的大数据纳入其中,提出了一种新的信用报告系统。该方法的特点是在线获取多样化的数据,实时进行评估和分析,并为中小企业、银行和政府自动生成在线信用报告。它还为中小企业提供了一种高效的互动方式,让他们在网上查阅信用报告。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
A Novel Big-Data-Driven Credit Reporting Framework for SMEs in China
SMEs (Small and Medium-size Enterprises) in China always face financing constraints and hardly obtain bank loans under unsound financing system. In external f Academic literature has shown that widespread information asymmetry may prevent the efficient allocation of lending, leading to credit rationing. Currently, most credit reporting, models for SMEs in China are primarily based on hard information about the enterprises and their owners but lack comprehensive evaluation based on the combination with soft information. To bridge the gap for SMEs, we propose a novel big-data-driven credit reporting framework which presents a new credit reporting system by including big data in business, finance, and social networks. The proposed approach features in capturing diversified data online, conducting evaluation and analysis in real-time, and automatically generating online credit reports for SMEs, banks, and government. It also provides an efficient interactive way for SMEs to check credit reports online.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Research on the Evaluation of Product Quality Perceived Value Based on Text Mining and Fuzzy Comprehensive Evaluation A New Pre-copy Strategy for Live Migration of Virtual Machines Hbase Based Surveillance Video Processing, Storage and Retrieval Mutual Information-Based Feature Selection and Ensemble Learning for Classification Implicit Correlation Intensity Mining Based on the Monte Carlo Method with Attenuation
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1