A Comparative Study of Machine Learning Techniques for Credit Card Fraud Detection Based on Time Variance

Shantanu Rajora; Dong Lin Li; Chandan Jha; Neha Bharill; Om Prakash Patel; Sudhanshu Joshi; Deepak Puthal; Mukesh Prasad

doi:10.1109/SSCI.2018.8628930

A Comparative Study of Machine Learning Techniques for Credit Card Fraud Detection Based on Time Variance

Shantanu Rajora, Dong Lin Li, Chandan Jha, Neha Bharill, Om Prakash Patel, Sudhanshu Joshi, Deepak Puthal, Mukesh Prasad

Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

59 Scopus citations

Abstract

This paper proposes a comparative performance of ten different machine learning algorithms, done on a credit card fraud detection application. The machine learning methods have been classified into two groups namely classification algorithms and ensemble learning group. Each group is comprised of five different algorithms. Besides, the 'Time' feature is introduced in the data set and performances of the algorithms are studied with and without the 'Time' feature. Two algorithms of the ensemble learning group have been found to perform better when the used dataset does not include the 'Time' feature. However, for the classification algorithms group, three classifiers are found to show better predictive accuracies when all attributes are included in the used dataset. The rest of the machine learning models have approximate similar scores between these datasets.

Original language	British English
Title of host publication	Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018
Editors	Suresh Sundaram
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1958-1963
Number of pages	6
ISBN (Electronic)	9781538692769
DOIs	https://doi.org/10.1109/SSCI.2018.8628930
State	Published - 28 Jan 2019
Event	8th IEEE Symposium Series on Computational Intelligence, SSCI 2018 - Bangalore, India Duration: 18 Nov 2018 → 21 Nov 2018

Publication series

Name	Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018

Conference

Conference	8th IEEE Symposium Series on Computational Intelligence, SSCI 2018
Country/Territory	India
City	Bangalore
Period	18/11/18 → 21/11/18

Keywords

ensemble-learning
fraud detection
non-ensemble learning
unbalanced data

Access to Document

10.1109/SSCI.2018.8628930

Cite this

Rajora, S., Li, D. L., Jha, C., Bharill, N., Patel, O. P., Joshi, S., Puthal, D., & Prasad, M. (2019). A Comparative Study of Machine Learning Techniques for Credit Card Fraud Detection Based on Time Variance. In S. Sundaram (Ed.), Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018 (pp. 1958-1963). Article 8628930 (Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI.2018.8628930

Rajora, Shantanu ; Li, Dong Lin ; Jha, Chandan et al. / A Comparative Study of Machine Learning Techniques for Credit Card Fraud Detection Based on Time Variance. Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018. editor / Suresh Sundaram. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1958-1963 (Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018).

@inproceedings{13faa45f09ab435d8714517166d7ff96,

title = "A Comparative Study of Machine Learning Techniques for Credit Card Fraud Detection Based on Time Variance",

abstract = "This paper proposes a comparative performance of ten different machine learning algorithms, done on a credit card fraud detection application. The machine learning methods have been classified into two groups namely classification algorithms and ensemble learning group. Each group is comprised of five different algorithms. Besides, the 'Time' feature is introduced in the data set and performances of the algorithms are studied with and without the 'Time' feature. Two algorithms of the ensemble learning group have been found to perform better when the used dataset does not include the 'Time' feature. However, for the classification algorithms group, three classifiers are found to show better predictive accuracies when all attributes are included in the used dataset. The rest of the machine learning models have approximate similar scores between these datasets.",

keywords = "ensemble-learning, fraud detection, non-ensemble learning, unbalanced data",

author = "Shantanu Rajora and Li, {Dong Lin} and Chandan Jha and Neha Bharill and Patel, {Om Prakash} and Sudhanshu Joshi and Deepak Puthal and Mukesh Prasad",

note = "Funding Information: This work was supported in part by Ministry of Science and Technology, R.O.C. under the Grant numbers: MOST 107-2634-F-009-009. Funding Information: This work was supported in part by Ministry of Science and Technology, R.O.C. Under the Grant numbers: MOST 107-2634-F-009-009. Publisher Copyright: {\textcopyright} 2018 IEEE.; 8th IEEE Symposium Series on Computational Intelligence, SSCI 2018 ; Conference date: 18-11-2018 Through 21-11-2018",

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Rajora, S, Li, DL, Jha, C, Bharill, N, Patel, OP, Joshi, S, Puthal, D & Prasad, M 2019, A Comparative Study of Machine Learning Techniques for Credit Card Fraud Detection Based on Time Variance. in S Sundaram (ed.), Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018., 8628930, Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1958-1963, 8th IEEE Symposium Series on Computational Intelligence, SSCI 2018, Bangalore, India, 18/11/18. https://doi.org/10.1109/SSCI.2018.8628930

A Comparative Study of Machine Learning Techniques for Credit Card Fraud Detection Based on Time Variance. / Rajora, Shantanu; Li, Dong Lin; Jha, Chandan et al.
Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018. ed. / Suresh Sundaram. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1958-1963 8628930 (Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Patel, Om Prakash

AU - Joshi, Sudhanshu

AU - Puthal, Deepak

AU - Prasad, Mukesh

N1 - Funding Information: This work was supported in part by Ministry of Science and Technology, R.O.C. under the Grant numbers: MOST 107-2634-F-009-009. Funding Information: This work was supported in part by Ministry of Science and Technology, R.O.C. Under the Grant numbers: MOST 107-2634-F-009-009. Publisher Copyright: © 2018 IEEE.

PY - 2019/1/28

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N2 - This paper proposes a comparative performance of ten different machine learning algorithms, done on a credit card fraud detection application. The machine learning methods have been classified into two groups namely classification algorithms and ensemble learning group. Each group is comprised of five different algorithms. Besides, the 'Time' feature is introduced in the data set and performances of the algorithms are studied with and without the 'Time' feature. Two algorithms of the ensemble learning group have been found to perform better when the used dataset does not include the 'Time' feature. However, for the classification algorithms group, three classifiers are found to show better predictive accuracies when all attributes are included in the used dataset. The rest of the machine learning models have approximate similar scores between these datasets.

AB - This paper proposes a comparative performance of ten different machine learning algorithms, done on a credit card fraud detection application. The machine learning methods have been classified into two groups namely classification algorithms and ensemble learning group. Each group is comprised of five different algorithms. Besides, the 'Time' feature is introduced in the data set and performances of the algorithms are studied with and without the 'Time' feature. Two algorithms of the ensemble learning group have been found to perform better when the used dataset does not include the 'Time' feature. However, for the classification algorithms group, three classifiers are found to show better predictive accuracies when all attributes are included in the used dataset. The rest of the machine learning models have approximate similar scores between these datasets.

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KW - unbalanced data

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Rajora S, Li DL, Jha C, Bharill N, Patel OP, Joshi S et al. A Comparative Study of Machine Learning Techniques for Credit Card Fraud Detection Based on Time Variance. In Sundaram S, editor, Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1958-1963. 8628930. (Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018). doi: 10.1109/SSCI.2018.8628930

A Comparative Study of Machine Learning Techniques for Credit Card Fraud Detection Based on Time Variance

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