As the world is rapidly moving towards digitization and money transactions are becoming cashless, the use of credit cards has rapidly increased. The fraud activities associated with it have also been increasing which leads to a huge loss to the financial institutions. Therefore, we need to analyze and detect the fraudulent transaction from the non-fraudulent ones. In this paper, we present a comprehensive review of various methods used to detect credit card fraud. These methodologies include Hidden Markov Model, Decision Trees, Logistic Regression, Support Vector Machines (SVM), Genetic algorithm, Neural Networks, Random Forests, Bayesian Belief Network. A comprehensive analysis of various techniques is presented. We conclude the paper with the pros and cons of the same as stated in the respective papers.

credit-card-fraud-detection-hmm

Hidden Markov Model (HMM) is used to model the sequence of operations in credit card transaction processing and demonstrate how to use it to identify fraud. An HMM is initially conditioned on a cardholder's normal behaviour using the KMeans algorithm for clustering. HMM calculated the probability of sequence, and when a qualified HMM does not authorize a credit card purchase with a high enough likelihood, it is classified as 'Fraudulent.' This approach comes out to be efficient, scalable and quite accurate in real-time predictions.

credit-card-fraud-detection-using-hidden-markov-model

credit-card-fraud-detection-using-hmm

using HMM to detect credit card fraudulent transaction

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