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The Sequence of Operations In Credit Card Transaction Processing

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They proposed credit card fraud detection with a neural network. They have built detection system, which is trained on a large sample of labeled credit card account transactions. These transactions contain example fraud cases due to lost cards, stolen cards, application fraud, counterfeit fraud, mail-order fraud, and non received issue (NRI) fraud. suggest a credit card fraud detection system (FDS)using meta-learning techniques to learn models of fraudulent credit card transactions. Metalearningis a general strategy that provides a means for combining and integrating a number of separately built classifiers or models. A meta-classifier is thus trained on the correlation of the predictions of the base classifiers. The same group has also worked on a cost-based model for fraud and intrusion detection. They use Java agents for Meta-learning (JAM), which is a distributed data mining system for credit card fraud detection. A number of important performance metrics like TP-FP (True Positive – False Positive) spread and accuracy have been defined by them. Present CARDWATCH, database mining system used for credit card fraud detection. The system, based on a neural learning module, provides an interface to a variety of commercial databases. Suggest the application of distributed data mining in credit card fraud detection. Use an agent-based approach with distributed learning for detecting frauds in credit card transactions. It is based on artificial intelligence and combines inductive learning algorithms and metal earning methods for achieving higher accuracy. Suggest the use of met classifier similar to in fraud detection problems. Proposed system which is an application of HMM in Anomaly Detection. The different steps in credit card transaction processing are represented as the underlying stochastic process of an HMM. The ranges of transaction amount can be used as the observation symbols, whereas the types of item have been considered to be states of the HMM.