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Article overview
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Multiple perspectives HMM-based feature engineering for credit card fraud detection | Yvan Lucas
; Pierre-Edouard Portier
; Léa Laporte
; Olivier Caelen
; Liyun He-Guelton
; Sylvie Calabretto
; Michael Granitzer
; | Date: |
15 May 2019 | Abstract: | Machine learning and data mining techniques have been used extensively in
order to detect credit card frauds. However, most studies consider credit card
transactions as isolated events and not as a sequence of transactions.
In this article, we model a sequence of credit card transactions from three
different perspectives, namely (i) does the sequence contain a Fraud? (ii) Is
the sequence obtained by fixing the card-holder or the payment terminal? (iii)
Is it a sequence of spent amount or of elapsed time between the current and
previous transactions? Combinations of the three binary perspectives give eight
sets of sequences from the (training) set of transactions. Each one of these
sets is modelled with a Hidden Markov Model (HMM). Each HMM associates a
likelihood to a transaction given its sequence of previous transactions. These
likelihoods are used as additional features in a Random Forest classifier for
fraud detection. This multiple perspectives HMM-based approach enables an
automatic feature engineering in order to model the sequential properties of
the dataset with respect to the classification task. This strategy allows for a
15% increase in the precision-recall AUC compared to the state of the art
feature engineering strategy for credit card fraud detection. | Source: | arXiv, 1905.6247 | Services: | Forum | Review | PDF | Favorites |
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