Name
Abstract | ||
|---|---|---|
Credit card fraud is a growing problem that affects card holders around the world. Fraud detection has been an interesting topic in machine learning. Nevertheless, current state of the art credit card fraud detection algorithms miss to include the real costs of credit card fraud as a measure to evaluate algorithms. In this paper a new comparison measure that realistically represents the monetary gains and losses due to fraud detection is proposed. Moreover, using the proposed cost measure a cost sensitive method based on Bayes minimum risk is presented. This method is compared with state of the art algorithms and shows improvements up to 23% measured by cost. The results of this paper are based on real life transactional data provided by a large European card processing company. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICMLA.2013.68 | ICMLA), 2013 12th International Conference |
Keywords | Field | DocType |
Bayes methods,credit transactions,fraud,learning (artificial intelligence),risk analysis,Bayes minimum risk,European card processing company,card holders,cost measure,cost sensitive credit card fraud detection,machine learning,real life transactional data,Bayesian decision theory,Cost sensitive classification,Credit card fraud detection | Data mining,Credit card fraud,Computer security,Computer science,Risk analysis (business),Risk assessment,Bayes estimator,Transaction data,Bayes' theorem | Conference |
Volume | Citations | PageRank |
1 | 14 | 0.77 |
References | Authors | |
11 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alejandro Correa Bahnsen | 1 | 102 | 5.82 |
| Aleksandar Stojanovic | 2 | 81 | 5.01 |
| Djamila Aouada | 3 | 229 | 29.63 |
| Björn E. Ottersten | 4 | 6418 | 575.28 |