An Efficient Method for Detecting Fraudulent Transactions Using Classification Algorithms on an Anonymized Credit Card Data Set

Sylvester Manlangit; Sami Azam; Bharanidharan Shanmugam; Krishnan Kannoorpatti; Mirjam Jonkman; Arasu Balasubramaniam

doi:10.1007/978-3-319-76348-4_41

An Efficient Method for Detecting Fraudulent Transactions Using Classification Algorithms on an Anonymized Credit Card Data Set

Sylvester Manlangit, Sami Azam, Bharanidharan Shanmugam, Krishnan Kannoorpatti, Mirjam Jonkman, Arasu Balasubramaniam

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper published in Proceedings

Abstract

Credit card fraudulent transactions are causing businesses and banks to lose time and money. Detecting fraudulent transactions before a transaction is finalized will help businesses and banks to save resources. This research aims to compare the fraud detection accuracy of different sampling techniques and classification algorithms. An efficient method of detecting fraud using machine learning is proposed. Anonymized data set from Kaggle was used for detecting fraudulent transactions. Each transaction has been labeled as either a fraudulent transaction or not. The severe imbalance between fraud and non-fraudulent data caused the algorithms to under-perform. This was addressed with the application of sampling techniques. The combination of undersampling and SMOTE raised the recall accuracy of the classification algorithm. k-NN algorithm showed the highest recall accuracy compared to the other algorithms.

Original language	English
Title of host publication	Intelligent Systems Design and Applications - 17th International Conference on Intelligent Systems Design and Applications ISDA 2017
Editors	Ajith Abraham, Pranab Muhuri, Azah Muda, Niketa Gandhi
Publisher	Springer-Verlag London Ltd.
Pages	418-429
Number of pages	12
ISBN (Electronic)	9783319763484
ISBN (Print)	9783319763477
DOIs	https://doi.org/10.1007/978-3-319-76348-4_41
Publication status	Published - 1 Jan 2018
Event	17th International Conference on Intelligent Systems Design and Applications, ISDA 2017 - Delhi, India Duration: 14 Dec 2017 → 16 Dec 2017

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	736
ISSN (Print)	2194-5357

Conference

Conference	17th International Conference on Intelligent Systems Design and Applications, ISDA 2017
Country	India
City	Delhi
Period	14/12/17 → 16/12/17

Access to Document

10.1007/978-3-319-76348-4_41

Link to publication in Scopus

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Manlangit, S., Azam, S., Shanmugam, B., Kannoorpatti, K., Jonkman, M., & Balasubramaniam, A. (2018). An Efficient Method for Detecting Fraudulent Transactions Using Classification Algorithms on an Anonymized Credit Card Data Set. In A. Abraham, P. Muhuri, A. Muda, & N. Gandhi (Eds.), Intelligent Systems Design and Applications - 17th International Conference on Intelligent Systems Design and Applications ISDA 2017 (pp. 418-429). (Advances in Intelligent Systems and Computing; Vol. 736). Springer-Verlag London Ltd.. https://doi.org/10.1007/978-3-319-76348-4_41

Manlangit, Sylvester ; Azam, Sami ; Shanmugam, Bharanidharan ; Kannoorpatti, Krishnan ; Jonkman, Mirjam ; Balasubramaniam, Arasu. / An Efficient Method for Detecting Fraudulent Transactions Using Classification Algorithms on an Anonymized Credit Card Data Set. Intelligent Systems Design and Applications - 17th International Conference on Intelligent Systems Design and Applications ISDA 2017. editor / Ajith Abraham ; Pranab Muhuri ; Azah Muda ; Niketa Gandhi. Springer-Verlag London Ltd., 2018. pp. 418-429 (Advances in Intelligent Systems and Computing).

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title = "An Efficient Method for Detecting Fraudulent Transactions Using Classification Algorithms on an Anonymized Credit Card Data Set",

abstract = "Credit card fraudulent transactions are causing businesses and banks to lose time and money. Detecting fraudulent transactions before a transaction is finalized will help businesses and banks to save resources. This research aims to compare the fraud detection accuracy of different sampling techniques and classification algorithms. An efficient method of detecting fraud using machine learning is proposed. Anonymized data set from Kaggle was used for detecting fraudulent transactions. Each transaction has been labeled as either a fraudulent transaction or not. The severe imbalance between fraud and non-fraudulent data caused the algorithms to under-perform. This was addressed with the application of sampling techniques. The combination of undersampling and SMOTE raised the recall accuracy of the classification algorithm. k-NN algorithm showed the highest recall accuracy compared to the other algorithms.",

keywords = "Anonymized data, Credit card, Fraud detection",

author = "Sylvester Manlangit and Sami Azam and Bharanidharan Shanmugam and Krishnan Kannoorpatti and Mirjam Jonkman and Arasu Balasubramaniam",

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Manlangit, S, Azam, S , Shanmugam, B , Kannoorpatti, K , Jonkman, M & Balasubramaniam, A 2018, An Efficient Method for Detecting Fraudulent Transactions Using Classification Algorithms on an Anonymized Credit Card Data Set. in A Abraham, P Muhuri, A Muda & N Gandhi (eds), Intelligent Systems Design and Applications - 17th International Conference on Intelligent Systems Design and Applications ISDA 2017. Advances in Intelligent Systems and Computing, vol. 736, Springer-Verlag London Ltd., pp. 418-429, 17th International Conference on Intelligent Systems Design and Applications, ISDA 2017, Delhi, India, 14/12/17. https://doi.org/10.1007/978-3-319-76348-4_41

An Efficient Method for Detecting Fraudulent Transactions Using Classification Algorithms on an Anonymized Credit Card Data Set. / Manlangit, Sylvester; Azam, Sami ; Shanmugam, Bharanidharan ; Kannoorpatti, Krishnan ; Jonkman, Mirjam; Balasubramaniam, Arasu.

Intelligent Systems Design and Applications - 17th International Conference on Intelligent Systems Design and Applications ISDA 2017. ed. / Ajith Abraham; Pranab Muhuri; Azah Muda; Niketa Gandhi. Springer-Verlag London Ltd., 2018. p. 418-429 (Advances in Intelligent Systems and Computing; Vol. 736).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper published in Proceedings

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AU - Manlangit, Sylvester

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AB - Credit card fraudulent transactions are causing businesses and banks to lose time and money. Detecting fraudulent transactions before a transaction is finalized will help businesses and banks to save resources. This research aims to compare the fraud detection accuracy of different sampling techniques and classification algorithms. An efficient method of detecting fraud using machine learning is proposed. Anonymized data set from Kaggle was used for detecting fraudulent transactions. Each transaction has been labeled as either a fraudulent transaction or not. The severe imbalance between fraud and non-fraudulent data caused the algorithms to under-perform. This was addressed with the application of sampling techniques. The combination of undersampling and SMOTE raised the recall accuracy of the classification algorithm. k-NN algorithm showed the highest recall accuracy compared to the other algorithms.

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Manlangit S, Azam S , Shanmugam B , Kannoorpatti K , Jonkman M, Balasubramaniam A. An Efficient Method for Detecting Fraudulent Transactions Using Classification Algorithms on an Anonymized Credit Card Data Set. In Abraham A, Muhuri P, Muda A, Gandhi N, editors, Intelligent Systems Design and Applications - 17th International Conference on Intelligent Systems Design and Applications ISDA 2017. Springer-Verlag London Ltd. 2018. p. 418-429. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-319-76348-4_41