Computer Science ›› 2021, Vol. 48 ›› Issue (4): 85-90.doi: 10.11896/jsjkx.200500109

Special Issue: Big Data & Data Scinece

• Database & Big Data & Data Science • Previous Articles     Next Articles

Study on Financial Credit Information Based on Graph Neural Network

LI Si-di, GUO Bing-hui, YANG Xiao-bo   

  1. Key Laboratory of Mathematics Informatics and Behavioral Semantics,Ministry of Education,School of Mathematical Sciences,BeihangUniversity,Beijing 100191,China
    Advanced Innovation Center for Big Data and Brain Computing,Beihang University,Beijing 100191,China
    Peng Cheng Laboratory,Shenzhen,Guangdong 518055,China
  • Received:2020-06-24 Revised:2020-08-14 Online:2021-04-15 Published:2021-04-09
  • About author:LI Si-di,born in 1998,postgraduate,is a member of China Computer Federation.Her main research interests include data science and complex intelligent system.(by_crazy_d@buaa.edu.cn)
    GUO Bing-hui,born in 1982,associate professor,is a member of China Computer Federation.His main research interests include data science and complex intelligent system.
  • Supported by:
    Artificial Intelligence Project(2018AAA0102301), National Natural Science Foundation of China(11671025,U20B2053) and Fundamental Research of Civil Aircraft (MJ-F-2012-04).

Abstract: The evaluation of the credit of users who apply for loans by financial institutions is one of the frontier directions in the field of Internet finance.Firstly,based on the historical data of the Internet financial loan network,the network modeling of the loan relationship between users reflects the complex network of loan correlation that integrates the interaction between user nodes and surrounding relationship nodes.Secondly,by introducing a graph neural network model based on the structural characteristic index of node centrality,a personal credit evaluation model with adjacent circle layer information and loan credit information is proposed.Finally,the model is implemented on a historical data set containing 756 100 transaction records,and is compared with the BP neural network algorithm and the RF-Logistic model.The results show that the proposed model has higher evaluation accuracy.

Key words: Complex loan network, Credit evaluation model, Graph neural networks

CLC Number: 

  • TP181
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