大数据背景下的互联网融资信用评价体系构建

发布时间：2018-11-21 20:27

【摘要】：互联网融资平台为小微企业提供融资新途径,但由于对小微企业征信体系建设知识的匮乏,造成互联网融资平台资金供应链断裂,损害多方利益,引发平台信用危机。该文立足于大数据分析环境,即以小微企业互联网融资流程为基础,分析所涉及的人、物、事及它们之间的关系,并以这些主体作为中心概念点扩展数据收集的范畴;构建企业本体,在全局概念的基础上利用层次聚类算法、关联规则挖掘算法、统计学及概念相似度分析方法,获取小微企业互联网融资大数据中的信用评价指标概念及概念间关系,并计算概念权重,构建信用评价指标体系。搭建Python 3.0仿真实验环境,经验证,利用该文所提算法在对概念抽取的精准度上要高于普通文本挖掘算法,但算法运行中复杂度较高,分析耗费的时间效率较低。
[Abstract]:Internet financing platform provides a new way of financing for small and micro enterprises, but because of the lack of knowledge about the construction of credit information system of small and micro enterprises, it leads to the breakage of the capital supply chain of Internet financing platform, damages the interests of many parties, and causes the platform credit crisis. This paper is based on big data's analytical environment, that is, based on the Internet financing process of small and micro enterprises, analyzes the people, things, events and their relationships, and takes these subjects as the central concept points to expand the scope of data collection. On the basis of global concept, enterprise ontology is constructed using hierarchical clustering algorithm, association rule mining algorithm, statistics and concept similarity analysis method. This paper obtains the concept of credit evaluation index and the relationship between the concepts in the Internet financing big data of small and micro enterprises, calculates the weight of the concept, and constructs the credit evaluation index system. The simulation environment of Python 3.0 is built. It is verified that the proposed algorithm is more accurate than the ordinary text mining algorithm in terms of concept extraction, but the complexity of the algorithm is high and the time efficiency of analysis is low.
【作者单位】：江西财经大学马克思主义学院;
【基金】：江西省政府创新基金课题(YC2015-B059)
【分类号】：F724.6;F832.4

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