Default Risk Assessment of Internet Financial Enterprises Based on Graph Neural Network
- Resource Type
- Conference
- Authors
- Qiu, Yuxin
- Source
- 2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) Information Technology,Networking,Electronic and Automation Control Conference (ITNEC), 2023 IEEE 6th. 6:592-596 Feb, 2023
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Engineering Profession
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Social networking (online)
Time series analysis
Companies
Feature extraction
Graph neural networks
Data models
Internet
heterogeneous graph neural network
graph mining
default risk assessment
deep learning
- Language
- ISSN
- 2693-3128
In recent years, the increasing number of default events happened in the Internet financial enterprises has incurred great financial losses to investors. Early warning to the enterprises with high default risk is of great significance to protecting the benefit of investors. There exist two challenges in traditional default risk assessment methods: poor data availability and neglect of risks from the affiliated entities. In order to address these problems, we collect the Internet financial enterprises’ nonfinancial data and propose a default risk assessment method for Internet financial enterprises based on heterogeneous graph neural network. Extensive experiments on a real-world dataset show that the proposed method outperforms the baseline models on the task of default risk assessment.