Title of Research Output

credit card fraud detection

Student Information

Charles WangFollow

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.

Comments

The dataset of the machine learning:

https://drive.google.com/file/d/15ItXL4cfkbvXQgHnvPifPSc7mIXqlymo/view?usp=sharing

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

Document Type

Event

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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.