PROPOSED DISTRIBUTED DATA MINING IN CREDIT CARD FRAUD DETECTION
Data Mining is popularly used to combact frauds because of its effectiveness. Using data mining techniques or model the Credit card fraud can be detected. The Credit card provides cashless shopping all over the world. Hence the risk of fraud using Credit card is been increasing. The clustering model used to classify the legal and fraudulent transaction using data cauterisation of region of parameter. Hidden Markov Model can detect whether an incoming transaction is fraudulent or not with low false alarm. HMM does not require fraud signatures and still it is capable to detect the frauds by bearing in mind a cardholder's spending habit. An HMM is initially trained with the normal behaviour of a cardholder. If an incoming credit card transaction is not accepted by the trend HMM with sufficiently high probability, it is consider to be fraudulent. At the same time, try to ensure that genuine transactions are not rejected. The financial losses due to Credit card fraud effect not only the individuals but also the merchants. Therefore the security measures need to be taken to detect the Credit card fraud.