A Novel Hidden Markov Model for Credit Card Fraud Detection

Nowadays the customers prefer the most accepted payment mode via credit card for the convenient way of online shopping, paying bills in easiest way. At the same time the fraud transaction risks using credit card is a main problem which should be avoided. There are many data mining techniques available to avoid these risks effectively. In existing research they modelled the sequence of operations in credit card transaction processing using a Hidden Markov Model (HMM) and shown how it can be used for the detection of frauds. To provide better accuracy and to avoid computational complexity in fraud detection in proposed work semi Hidden Markov model (SHMM) algorithm of anomaly detection is presented which computes the distance between the processes monitored by credit card detection system and the perfect normal processes. With this we are implementing another method for fraud detection is that having a key idea is to factorize marginal log-likelihood using a variation distribution over latent variables. An asymptotic approximation, a factorized information criterion (FIC) obtained by applying the Laplace method to each of the factorized components. Our experimental results demonstrate that we can significantly reduce loss due to fraud through distributed data mining of fraud models.