Proceedings of the 2021 International Conference on Enterprise Management and Economic Development (ICEMED 2021)

The Application of Big Data in Preventing Financial Risks of P2P Network Loan

Authors
ZHIQING LI
Corresponding Author
ZHIQING LI
Available Online 2 June 2021.
DOI
https://doi.org/10.2991/aebmr.k.210601.035How to use a DOI?
Keywords
big data, Internet finance, credit risk
Abstract

With the increasing impact of the Internet on people’s lives, Internet financial platforms are also emerging, According to the data published on zero one, By the end of 2020, P2P the number of online lending platforms has reached 6063, Among them, the number of platforms operating in normal state is 1185, 19.5 per cent of the total number of platforms, The year-on-year decline was 46.8 per cent, The number of platforms in an abnormal state is 4672, The proportion of the total number of platforms is 77.1. Although less, But the number of problem platforms is still huge. The number of problem platforms is enough to worry, along with credit risk, A lot of platform loans can’t be recovered, The non-performing loan rate has risen. From the beginning of online shopping, To Internet banking, third party payments, On the Internet, the rapid development of Internet finance takes only a few years. Especially in P2P, the development of recent years has reached an alarming rate, but there are also a series of credit risk problems. In order to ensure that the credit risk of Internet financial platform can be effectively reduced, it is necessary to use modern big data technology to rate the risk. This paper mainly studies a large amount of data formed by users on the Internet, and uses data crawler technology to collect, store and transmit data. After that, big data analysis software RapidMiner big data analysis software are used to preprocess the crawling data, and random forest algorithm is used to match the best data and rule scheme in the software. Different from the traditional research on Internet financial credit risk, this paper uses logical regression analysis to evaluate the default probability of users according to the rules, and predicts the default and fraud of users. The results are analyzed. So as to effectively reduce the credit risk of Internet financial platform enterprises.

Copyright
© 2021, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2021 International Conference on Enterprise Management and Economic Development (ICEMED 2021)
Series
Advances in Economics, Business and Management Research
Publication Date
2 June 2021
ISBN
978-94-6239-387-5
ISSN
2352-5428
DOI
https://doi.org/10.2991/aebmr.k.210601.035How to use a DOI?
Copyright
© 2021, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - ZHIQING LI
PY  - 2021
DA  - 2021/06/02
TI  - The Application of Big Data in Preventing Financial Risks of P2P Network Loan
BT  - Proceedings of the 2021 International Conference on Enterprise Management and Economic Development (ICEMED 2021)
PB  - Atlantis Press
SP  - 196
EP  - 204
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.210601.035
DO  - https://doi.org/10.2991/aebmr.k.210601.035
ID  - LI2021
ER  -