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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 framework, we model a sequence of credit card transactions from three different perspectives, namely (i) The sequence contains or doesn't contain a fraud (ii) The sequence is obtained by fixing the cardholder or the payment terminal (iii) It is 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 sequences 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.Our multiple perspectives HMM-based approach offers automated feature engineering to model temporal correlations so as to improve the effectiveness of the classification task and allows for an increase in the detection of fraudulent transactions when combined with the state of the art expert based feature engineering strategy for credit card fraud detection.In extension to previous works, we show that this approach goes beyond ecommerce transactions and provides a robust feature engineering over different datasets, hyperparameters and classifiers. Moreover, we compare strategies to deal with structural missing values.

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.

International audienceMultistructured (M-S) documents were introduced as an answer to the need of ever more expressive data models for scholarly annotation, as experienced in the frame of Digital Humanities. Many proposals go beyond XML, that is the gold standard for annotation, and allow the expression of multilevel, concurrent annotation. However, most of them lack support for algorithmic tasks like validation and querying, despite those being central in most of their application contexts. In this paper, we focus on two aspects of annotation: data model expressiveness and validation. We introduce extended Annotation Graphs (eAG), a highly expressive graph-based data model, fit for the enrichment of multimedia resources. Regarding validation ofM-S documents, we identify algorithmic complexity as a limiting factor. We advocate that this limitation may be bypassed provided validation can be checked by construction, that is by constraining the shape of data during its very manufacture. So far as we know, no existing validation mechanism for graph-structured data meets this goal. We define here such a mechanism, based on the simulation relation, somehow following a track initiated in Dataguides. We prove that thanks to this mechanism, the validity of M-S data regarding a given schema can be guaranteed without any algorithmic chec