Real-time credit card fraud detection using computational intelligence
Expert Systems with Applications: An International Journal
Credit Card Fraud Detection with Artificial Immune System
ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
Association rules applied to credit card fraud detection
Expert Systems with Applications: An International Journal
BDD minimization by scatter search
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Employing transaction aggregation strategy to detect credit card fraud
Expert Systems with Applications: An International Journal
A cost-sensitive decision tree approach for fraud detection
Expert Systems with Applications: An International Journal
Solving credit card fraud detection problem by the new metaheuristics migrating birds optimization
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
Using genetic programming to detect fraud in electronic transactions
Proceedings of the 19th Brazilian symposium on Multimedia and the web
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In this study we develop a method which improves a credit card fraud detection solution currently being used in a bank. With this solution each transaction is scored and based on these scores the transactions are classified as fraudulent or legitimate. In fraud detection solutions the typical objective is to minimize the wrongly classified number of transactions. However, in reality, wrong classification of each transaction do not have the same effect in that if a card is in the hand of fraudsters its whole available limit is used up. Thus, the misclassification cost should be taken as the available limit of the card. This is what we aim at minimizing in this study. As for the solution method, we suggest a novel combination of the two well known meta-heuristic approaches, namely the genetic algorithms and the scatter search. The method is applied to real data and very successful results are obtained compared to current practice.