research-article

Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs

Authors:

Pierre-Edouard Portier,

Liyun He-Guelton,

Olivier Caelen,

Michael Granitzer,

Sylvie CalabrettoAuthors Info & Claims

Volume 102, Issue C

Pages 393 - 402

https://doi.org/10.1016/j.future.2019.08.029

Published: 01 January 2020 Publication History

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 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 card-holder 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.

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Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs
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Published In

cover image Future Generation Computer Systems

Future Generation Computer Systems Volume 102, Issue C

Jan 2020

1062 pages

ISSN:0167-739X

Issue’s Table of Contents

Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 January 2020

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Cited By

Liu MGuo CXu L(2024)An interpretable automated feature engineering framework for improving logistic regressionApplied Soft Computing10.1016/j.asoc.2024.111269153:COnline publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111269
Kumar VBhardwaj VAnbarasu V(2023)Credit Card Fraud Detector for Lower Ranged Transactions using AI AlgorithmsProceedings of the 5th International Conference on Information Management & Machine Intelligence10.1145/3647444.3647881(1-5)Online publication date: 23-Nov-2023
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Cherif ABadhib AAmmar HAlshehri SKalkatawi MImine A(2023)Credit card fraud detection in the era of disruptive technologiesJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2022.11.00835:1(145-174)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.jksuci.2022.11.008
Karthika JSenthilselvi A(2023)Smart credit card fraud detection system based on dilated convolutional neural network with sampling techniqueMultimedia Tools and Applications10.1007/s11042-023-15730-182:20(31691-31708)Online publication date: 15-May-2023
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Liu JZhang SFan H(2022)A two-stage hybrid credit risk prediction model based on XGBoost and graph-based deep neural networkExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.116624195:COnline publication date: 1-Jun-2022
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Rodrigues VPolicarpo Lda Silveira Dda Rosa Righi Rda Costa CBarbosa JAntunes RScorsatto RArcot T(2022)Fraud detection and prevention in e-commerceElectronic Commerce Research and Applications10.1016/j.elerap.2022.10120756:COnline publication date: 1-Nov-2022
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