Name
Abstract | ||
|---|---|---|
Fraud detection is a critical problem affecting large financial companies that has increased due to the growth in credit card transactions. This paper presents a new method for automatic detection of frauds in credit card transactions based on non-linear signal processing. The proposed method consists of the following stages: feature extraction, training and classification, decision fusion, and result presentation. Discriminant-based classifiers and an advanced non-Gaussian mixture classification method are employed to distinguish between legitimate and fraudulent transactions. The posterior probabilities produced by classifiers are fused by means of order statistical digital filters. Results from data mining of a large database of real transactions are presented. The feasibility of the proposed method is demonstrated for several datasets using parameters derived from receiver characteristic operating analysis and key performance indicators of the business. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/CCST.2012.6393560 | ICCST |
Keywords | Field | DocType |
credit transactions,digital filters,feature extraction,fraud,probability,signal processing,smart cards,advanced nongaussian mixture classification,automatic credit card fraud detection,credit card transactions,decision fusion,discriminant-based classifiers,fraudulent transactions,key performance indicators,large financial companies,legitimate transactions,nonlinear signal processing,order statistical digital filters,posterior probabilities,receiver characteristic operating analysis,result presentation,training,data mining,fraud detection,non-linear signal processing,order statistics filters,pattern recognition,business,classification algorithms | Data mining,Signal processing,Performance indicator,Computer security,Computer science,Smart card,Posterior probability,Artificial intelligence,Credit card fraud,Pattern recognition,Credit card,Feature extraction,Statistical classification | Conference |
ISSN | ISBN | Citations |
1071-6572 E-ISBN : 978-1-4673-2449-6 | 978-1-4673-2449-6 | 7 |
PageRank | References | Authors |
0.56 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Addisson Salazar | 1 | 121 | 23.46 |
| Gonzalo Safont | 2 | 54 | 12.55 |
| Soriano, A. | 3 | 7 | 0.56 |
| Luis Vergara | 4 | 24 | 3.05 |