Hidden Markov Model

By Mohssen M. Z. E. Mohammed, Muhammad Badruddin Khan, Eihab B. M. Bashier. Essentials of Pattern Recognition, 2010

387 citations recorded by the source corpus.

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.

View Paper: https://www.semanticscholar.org/paper/841f08f1c001d49f78273274dcf70b44b9e85711

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Citations in the corpus

Volatility forecasting of crude oil market: A new hybrid method (2018, methodology): “For this purpose, the current study applies the hidden Markov model (HMM) (Eddy, 1996) to forecast crude oil price volatility, which can not only accurately describe volatility states but also eliminate the misjudgment of volatility states caused by subjective reasoning (Dias et al.”