Credit Card Fraud Detection Using Hidden Markov Model A ASP.Net Project

Credit Card Fraud Detection Using Hidden Markov Model A ASP.Net Project is for Computer science final year students. Credit-Card-Fraud-A-ASP-Net-Project


The trend today is to use a card instead of cash. Each time you walk past a a store or each time you peep into the wallet of a working class employee, executive or big shot entrepreneur all you tend to see are plastic cards which have great value! 

Nowadays the usage of credit cards has dramatically increased and has become the most popular mode of payment for both online as well as regular purchases. However, it is very easy to manipulate and make wrong use of these cards. The cases of fraud associated with credit cards are rising along with its user group. 

Making cash flow simpler: 

Credit-card-based purchases can be categorized as physical card and  virtual card. In a physical-card based purchase, the cardholders presents his card physically to a merchant for making a payment, in case another user is swiping the stolen or lost card , it can lead to substantial loss to the original card holder or the credit card company. In the latter purchase, only some important information about a card (card number, expiration date, secure code) is required in order to make the payment. 

Such purchases are normally done on the Internet or over the telephone. To commit fraud in these types of purchases, fraudsters require to know the card details.  The only way to detect this kind of fraud is to analyze the spending patterns on every card and to figure out any inconsistency with respect to the “usual” spending patterns.


Here, 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 cardholders. 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 time, we try to ensure that genuine transactions are not rejected. We present detailed experimental results to show the effectiveness of our approach and compare it with other techniques available in the literature.

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