Efficient resampling for fraud detection during anonymised credit card transactions with unbalanced datasets

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Mrozek, Petr, Panneerselvam, J. and Bagdasar, Ovidiu 2020. Efficient resampling for fraud detection during anonymised credit card transactions with unbalanced datasets. IEEE. https://doi.org/10.1109/ucc48980.2020.00067
AuthorsMrozek, Petr, Panneerselvam, J. and Bagdasar, Ovidiu
Abstract

The rapid growth of e-commerce and online shopping have resulted in an unprecedented increase in the amount of money that is annually lost to credit card fraudsters. In an attempt to address credit card fraud, researchers are leveraging the application of various machine learning techniques for efficiently detecting and preventing fraudulent credit card transactions. One of the prevalent common issues around the analytics of credit card transactions is the highly unbalanced nature of the datasets, which is frequently associated with the binary classification problems. This paper intends to review, analyse and implement a selection of notable machine learning algorithms such as Logistic Regression, Random Forest, K-Nearest Neighbours and Stochastic Gradient Descent, with the motivation of empirically evaluating their efficiencies in handling unbalanced datasets whilst detecting credit card fraud transactions. A publicly available dataset comprising 284807 transactions of European cardholders is analysed and trained with the studied machine learning techniques to detect fraudulent transactions. Furthermore, this paper also evaluates the incorporation of two notable resampling methods, namely Random Under-sampling and Synthetic Majority Oversampling Techniques (SMOTE) in the aforementioned algorithms, in order to analyse their efficiency in handling unbalanced datasets. The proposed resampling methods significantly increased the detection ability, the most successful technique of combination of Random Forest with Random Under-sampling achieved the recall score of 100% in contrast to the recall score 77% of model without resampling technique. The key contribution of this paper is the postulation of efficient machine learning algorithms together with suitable resampling methods, suitable for credit card fraud detection with unbalanced dataset.

KeywordsCredit cards; Vegetation; Machine learning algorithms
Year2020
Journal2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC)
2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC)
PublisherIEEE
Digital Object Identifier (DOI)https://doi.org/10.1109/ucc48980.2020.00067
Web address (URL)http://hdl.handle.net/10545/625574
hdl:10545/625574
ISBN9780738123943
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Publication dates30 Dec 2020
Publication process dates
Deposited01 Feb 2021, 11:09
Accepted06 Oct 2020
ContributorsUniversity of Derby and University of Leicester
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New results and conjectures on 2-partitions of multisets
Bagdasar, O. and Andrica, D. 2017. New results and conjectures on 2-partitions of multisets. 7th International Conference on Modeling, Simulation, and Applied Optimization, ICMSAO 2017. IEEE Computer Society. https://doi.org/10.1109/icmsao.2017.7934928
Collaborative actuation of wireless sensor and actuator networks for the agriculture industry.
Bai, Xingzhen, Liu, Lu, Cao, Maoyong, Panneerselvam, J., Sun, Qiao and Wang, Haixia 2017. Collaborative actuation of wireless sensor and actuator networks for the agriculture industry. IEEE Access. https://doi.org/10.1109/ACCESS.2017.2725342
InOt-RePCoN: Forecasting user behavioural trend in large-scale cloud environments.
Panneerselvam, J., Liu, Lu and Antonopoulos, Nikolaos 2017. InOt-RePCoN: Forecasting user behavioural trend in large-scale cloud environments. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2017.05.022
Mobilouds: An energy efficient MCC collaborative framework with extended mobile participation for next generation networks
Panneerselvam, J., Hardy, J., Liu, Lu, Yuan, Bo and Antonopoulos, Nikolaos 2017. Mobilouds: An energy efficient MCC collaborative framework with extended mobile participation for next generation networks. IEEE Access. https://doi.org/10.1109/ACCESS.2016.2602321
On the number of complex horadam sequences with a fixed period
Bagdasar, Ovidiu and Larcombe, Peter J. 2013. On the number of complex horadam sequences with a fixed period. Fibonacci Quarterly.
On the characterization of periodic complex Horadam sequences
Bagdasar, Ovidiu and Larcombe, Peter J. 2013. On the characterization of periodic complex Horadam sequences. Fibonacci Quarterly.
On a result of Bunder involving Horadam sequences: A proof and generalization
Larcombe, Peter J. and Bagdasar, Ovidiu 2013. On a result of Bunder involving Horadam sequences: A proof and generalization. Fibonacci Quarterly.
On a result of Bunder involving horadam sequences: A new proof
Larcombe, P.J., Bagdasar, O.D., Fennessey, E.J. and Bagdasar, O. 2014. On a result of Bunder involving horadam sequences: A new proof. Fibonacci Quarterly.
Influence discovery in semantic networks: An initial approach
Trovati, M. and Bagdasar, O. 2014. Influence discovery in semantic networks: An initial approach. IEEE. https://doi.org/10.1109/uksim.2014.48
Complementary iInequalities involving the Stolarsky Mean
Bagdasar, Ovidiu 2010. Complementary iInequalities involving the Stolarsky Mean. Journal of Inequalities and Applications. https://doi.org/10.1155/2010/492570
Concise computer mathematics: tutorials on theory and problems
Bagdasar, Ovidiu 2013. Concise computer mathematics: tutorials on theory and problems. Springer.
On the characterization of periodic generalized Horadam sequences
Bagdasar, Ovidiu and Larcombe, Peter J. 2014. On the characterization of periodic generalized Horadam sequences. Journal of Difference Equations and Applications. https://doi.org/10.1080/10236198.2014.891022
Local maximum points of explicitly quasiconvex functions
Bagdasar, Ovidiu and Popovici, Nicolae 2014. Local maximum points of explicitly quasiconvex functions. Optimization Letters. https://doi.org/10.1007/s11590-014-0781-3
A horadam-based pseudo-random number generator
Bagdasar, O.D., Chen, M. and Bagdasar, O. 2014. A horadam-based pseudo-random number generator. IEEE. https://doi.org/10.1109/uksim.2014.55
On the structure of periodic complex horadam orbits
Bagdasar, O.D., Larcombe, P.J., Anjum, A. and Bagdasar, O. 2016. On the structure of periodic complex horadam orbits. Carpathian Journal of Mathematics.
RVLBPNN: A workload forecasting model for smart cloud computing
Lu, Yao, Panneerselvam, J., Liu, Lu and Wu, Yan 2016. RVLBPNN: A workload forecasting model for smart cloud computing. Scientific Programming. https://doi.org/10.1155/2016/5635673
CCLBR: Congestion control-based load balanced routing in unstructured P2P systems
Shen, Xiang-Jun, Chang, Qing, Liu, Lu, Panneerselvam, J. and Zha, Zheng-Jun 2016. CCLBR: Congestion control-based load balanced routing in unstructured P2P systems. IEEE Systems Journal. https://doi.org/10.1109/JSYST.2016.2558515
Characterizations of Generalized Exponential Trichotomies for Linear Discrete-time Systems
Popa, I.-L., Ceauşu, T., Bagdasar, O. and Agarwal, R.P. 2016. Characterizations of Generalized Exponential Trichotomies for Linear Discrete-time Systems. Analele Stiintifice ale Universitatii Ovidius Constanta, Seria Matematica. 27 (2), pp. 153-166. https://doi.org/10.2478/auom-2019-0025
On some functions involving the lcm and gcd of integer tuples
Bagdasar, Ovidiu 2014. On some functions involving the lcm and gcd of integer tuples. Scientific Publications of the State University of Novi Pazar Series A: Applied Mathematics, Informatics and mechanics. https://doi.org/10.5937/SPSUNP1402091B
A critical review of the routing protocols in opportunistic networks
Panneerselvam, J., Atojoko, Anthony, Smith, K., Liu, Lu and Antonopoulos, Nikolaos 2014. A critical review of the routing protocols in opportunistic networks. EAI Endorsed Transactions on Industrial Networks and Intelligent Systems. https://doi.org/10.4108/inis.1.1.e6
iMIG: Toward an adaptive live migration method for KVM virtual machines
Li, Jianxin, Zhao, Jieyu Zhao, Li, Yi, Cui, Lei, Li, Bo, Liu, Lu and Panneerselvam, J. 2014. iMIG: Toward an adaptive live migration method for KVM virtual machines. The Computer Journal. https://doi.org/10.1093/comjnl/bxu065
Achieving dynamic load balancing through mobile agents in small world P2P networks
Shen, Xiang-Jun, Liu, Lu, Zha, Zheng-Jun, Gu, Pei-Ying, Jiang, Zhong-Qiu, Chen, Ji-Ming and Panneerselvam, J. 2014. Achieving dynamic load balancing through mobile agents in small world P2P networks. Computer Networks. https://doi.org/10.1016/j.comnet.2014.05.003