| Issue |
E3S Web Conf.
Volume 214, 2020
2020 International Conference on Energy Big Data and Low-carbon Development Management (EBLDM 2020)
|
|
|---|---|---|
| Article Number | 02042 | |
| Number of page(s) | 4 | |
| Section | Machine Learning and Energy Industry Structure Forecast Analysis | |
| DOI | https://doi.org/10.1051/e3sconf/202021402042 | |
| Published online | 07 December 2020 | |
An Xgboost based system for financial fraud detection
1 University of Virginia, Virginia, United States
2 Washington University in Saint Louis, Saint Louis, United States
3 Fudan University, Shanghai, China
4 University of Cambridge, Shanghai, China
a sl2kd@virginia.edu
b ke.xu@wustl.edu
c 13761536034@163.com
d xinyesha98@outlook.com
Credit card fraud leads to billions of losses in online transaction. Many corporations like Alibaba, Amazon and Paypal invest billions of dollars to build a safe transaction system. There are some studies in this area having tried to use machine learning or data mining to solve these problems. This paper proposed our fraud detection system for e- commerce merchant. Unlike many other works, this system combines manual and automatic classifications. This paper can inspire researchers and engineers to design and deploy online transaction systems.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.