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Fraud detection is an important topic that applies to various enterprisessuch as banking and financial sectors, insurance, government agencies, lawenforcement, and more. Fraud attempts have been risen remarkably in currentyears, shaping fraud detection an essential topic for research. One of the mainchallenges in fraud detection is acquiring fraud samples which is a complex andchallenging task. In order to deal with this challenge, we apply one-classclassification methods such as SVDD which does not need the fraud samples fortraining. Also, we present our algorithm REDBSCAN which is an extension ofDBSCAN to reduce the number of samples and select those that keep the shape ofdata. The results obtained by the implementation of the proposed methodindicated that the fraud detection process was improved in both performance andspeed.

Applying support vector data description for fraud detection