An Optimized Multiple Semi-Hidden Markov Model for Credit Card Fraud Detection

The credit card payment system is a widespread usable system which provides the easiest way of payment to the customers, but some of them misuse another individual's credit card for personal reasons. So, in order to provide credit card fraud detection, Multiple Semi-Hidden Markov Model is suggested to gather multiple observations and the detection phase is executed. It is significant to compute the good model parameters because it impacts the detection performance in the Multiple Semi-Hidden Markov Model. So, in this manuscript an innovative technique is introduced which is called Optimized Multiple Semi-Hidden Markov Model (OMSHMM) which is used for optimizing the model parameters. The Multiple Semi-Hidden Markov Model is used for detecting fraudulent users and for optimizing training values Cuckoo Search algorithm is proposed. The main intent of this research is automating the use of Multiple Semi-Hidden Markov Model, by liberating customers from the necessity of statistical knowledge. The number of states and also its model parameters are decided by the Cuckoo Search algorithm. An experimental result shows that when compared to the existing research there is high accuracy in the proposed research.