Name
Abstract | ||
|---|---|---|
Credit card fraud events take place frequently and then result in huge financial losses. Criminals can use some technologies such as Trojan or Phishing to steal the information of other people's credit cards. Therefore, an effictive fraud detection method is important since it can identify a fraud in time when a criminal uses a stolen card to consume. One method is to make full use of the historical transaction data including normal transactions and fraud ones to obtain normal/fraud behavior features based on machine learning techniques, and then utilize these features to check if a transaction is fraud or not. In this paper, two kinds of random forests are used to train the behavior features of normal and abnormal transactions. We make a comparison of the two random forests which are different in their base classifiers, and analyze their performance on credit fraud detection. The data used in our experiments come from an e-commerce company in China. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICNSC.2018.8361343 | 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC) |
Keywords | Field | DocType |
Random forest,decision tree,credit card fraud | Decision tree,Credit card fraud,Phishing,Computer security,Computer science,Support vector machine,Control engineering,Trojan,Random forest,Database transaction,Transaction data | Conference |
ISSN | ISBN | Citations |
1810-7869 | 978-1-5386-5054-7 | 1 |
PageRank | References | Authors |
0.37 | 9 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shiyang Xuan | 1 | 1 | 0.70 |
| GuanJun Liu | 2 | 176 | 26.24 |
| Zhenchuan Li | 3 | 1 | 0.37 |
| Lutao Zheng | 4 | 7 | 1.82 |
| Shuo Wang | 5 | 303 | 54.05 |
| Changjun Jiang | 6 | 1350 | 117.57 |