research-article

Research on Credit Risk Identification of Internet Financial Enterprises Based on Big Data

Author:

Hua Peng Academic Editor:

Fazlullah KhanAuthors Info & Claims

Mobile Information Systems, Volume 2021

https://doi.org/10.1155/2021/1034803

Published: 01 January 2021 Publication History

Abstract

The advent of the era of big data has provided a new way of development for Internet financial credit collection. The traditional methods of credit risk identification of Internet financial enterprises cannot get the characteristics of credit risk zoning, leading to large errors in the results of credit risk identification. Therefore, this paper proposes a new method of credit risk identification based on big data for Internet financial enterprises. According to the big data perspective, the credit risk assessment steps of Internet financial enterprises are analyzed and the weight of assessment indicators is calculated using the improved analytic hierarchy process (AHP), and the linear weighted synthesis method is applied to comprehensively assess the credit of clients. Using the unique characteristics of big data credit risk region division, the big data credit risk is determined by rule-based matching method. The eXtreme Gradient Boosting (XGBoost) machine learning algorithm is used to establish a credit risk identification model of Internet financial enterprises. The kappa coefficient and ROC curve are used to evaluate the performance of the proposed method. Experimental results show that the proposed method can accurately assess the credit risk of Internet financial enterprises.

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Cited By

Fang X(2022)Financial Transaction Risk Identification Method Based on Boosting-SVM AlgorithmWireless Communications & Mobile Computing10.1155/2022/43962502022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/4396250
Zhang QZhou F(2022)Research on Enterprise Financial Management and Prediction System Based on SaaS ModelSecurity and Communication Networks10.1155/2022/32189032022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/3218903

Index Terms

Research on Credit Risk Identification of Internet Financial Enterprises Based on Big Data
1. Applied computing
2. Information systems
  1. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Mobile Information Systems

Mobile Information Systems Volume 2021, Issue

2021

6406 pages

ISSN:1574-017X

EISSN:1875-905X

Issue’s Table of Contents

Copyright © 2021 Hua Peng.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IOS Press

Netherlands

Publication History

Published: 01 January 2021

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Citations

Cited By

Fang X(2022)Financial Transaction Risk Identification Method Based on Boosting-SVM AlgorithmWireless Communications & Mobile Computing10.1155/2022/43962502022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/4396250
Zhang QZhou F(2022)Research on Enterprise Financial Management and Prediction System Based on SaaS ModelSecurity and Communication Networks10.1155/2022/32189032022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/3218903

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