Faculty
Engineering
Supervisor Name
Xianbin Wang
Keywords
fraud detection
Description
In recent years, credit card fraud poses a significant threat to banks and customers financially over the world. However, in the banking industry, to counter this issue, machine learning algorithms has become a growing trend to put proactive intervention of credit card fraud in place. In this project, we are going to detect fraudulent credit card transactions with machine learning models. This data set includes 284807 credit card transactions of European cardholders over a period of two days with their personal information kept anonymous. Among all transactions, 492 were fraudulent.
Acknowledgements
Thank you to Dr. Xianbin Wang, the Western USRI program, and the Faculty of Engineering for their support.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Document Type
Event
Included in
credit card fraud detection
In recent years, credit card fraud poses a significant threat to banks and customers financially over the world. However, in the banking industry, to counter this issue, machine learning algorithms has become a growing trend to put proactive intervention of credit card fraud in place. In this project, we are going to detect fraudulent credit card transactions with machine learning models. This data set includes 284807 credit card transactions of European cardholders over a period of two days with their personal information kept anonymous. Among all transactions, 492 were fraudulent.
Comments
The dataset of the machine learning:
https://drive.google.com/file/d/15ItXL4cfkbvXQgHnvPifPSc7mIXqlymo/view?usp=sharing