The usage of credit cards has been drastically increased.These days especially after demonetization,credit cards and debit cards have become the the most popular mode of payment for both online as well as regular purchase.With the increase in usage of these payments,the cases of fraud associated with it are also increasing day by day.
we model the sequence of operations in credit card transaction processing using a Hidden Markov Model (HMM) and show how it can be used for the detection of frauds. An HMM is initially trained with the normal behavior of a cardholder. If an incoming credit card transaction is not accepted by the trained HMM with sufficiently high probability, it is considered to be fraudulent.At the same we make sure that genuine transactions are not rolled back or rejected.This model detects the ability to find the behavior of credit card’s holder and also considering a cardholder’s spending habit.Card transaction processing sequence by the stochastic process of an HMM.
The details of items purchased in Individual transactions are usually not known to any Fraud Detection System(FDS) running at the bank that issues credit cards to the cardholders. Hence, we feel that HMM is an ideal choice for addressing this problem.It tries to find any anomaly in the transaction based on the spending profile of the cardholder, shipping address, and billing address, etc. If the FDS confirms the transaction to be of fraud, it raises an alarm, and the issuing bank declines the transaction.
1.Detection of frauds is much easier now,which can save number of lives.
2.In the above proposed system we maintain logs,so no need to worry about the actual card holder.
3.The most accurate detection using this technique.
4.This reduces the tedious work of an employee in the bank.