Proceedings of the 5th International Conference on Financial Innovation and Economic Development (ICFIED 2020)

Research on the Development of Financial Industry Under the Background of Big Data and Artificial Intelligence

Authors
Fei Lin
Corresponding Author
Fei Lin
Available Online 11 March 2020.
DOI
https://doi.org/10.2991/aebmr.k.200306.001How to use a DOI?
Keywords
big data and artificial intelligence, intelligent finance, Block chain financial core technology, scenario finance
Abstract
With the development of modern information technology, the integration of high and new technologies such as big data and artificial intelligence with traditional industries is getting closer and closer. In this context, big data finance and intelligent finance gradually become the focus of research and the key to financial business innovation. The efficient integration of big data, artificial intelligence and financial business will certainly push the development of financial business to a new level. This paper mainly analyzes the current situation of the fusion of financial industry and artificial intelligence, finds out the problems in the fusion, and puts forward the measures and suggestions for the efficient realization of intelligent finance.
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Proceedings
5th International Conference on Financial Innovation and Economic Development (ICFIED 2020)
Part of series
Advances in Economics, Business and Management Research
Publication Date
11 March 2020
ISBN
978-94-6252-923-6
ISSN
2352-5428
DOI
https://doi.org/10.2991/aebmr.k.200306.001How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Fei Lin
PY  - 2020
DA  - 2020/03/11
TI  - Research on the Development of Financial Industry Under the Background of Big Data and Artificial Intelligence
BT  - 5th International Conference on Financial Innovation and Economic Development (ICFIED 2020)
PB  - Atlantis Press
SP  - 1
EP  - 4
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.200306.001
DO  - https://doi.org/10.2991/aebmr.k.200306.001
ID  - Lin2020
ER  -