Credit Card Fraud Detection Using Hidden Markov Model

• Published in: IEEE transactions on dependable and secure computing Vol. 5; no. 1; pp. 37 - 48
• Main Authors: Srivastava, A., Kundu, A., Sural, S., Majumdar, A.K.
• Format: Journal Article
• Language: English
• Published: Washington IEEE 01.01.2008; IEEE Computer Society
• Subjects: Algorithms; Cards; Computation; Credit card fraud; Credit card industry; Credit cards; Data mining; Digital Object Identifier; Electronic commerce; Fraud; Fraud prevention; Hidden Markov models; Information analysis; Internet telephony; Markov analysis; Mathematical models; Neural networks; On-line systems; Online; Online transaction processing; Pattern analysis; Purchasing; Security; Security and Protection; Service introduction; Shopping; State estimation; Studies; Transaction processing; Electronic Commerce; Security and Protection
• ISSN: 1545-5971; 1941-0018
• DOI: 10.1109/TDSC.2007.70228

Due to a rapid advancement in the electronic commerce technology, the use of credit cards has dramatically increased. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. In this paper, 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 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.