Credit Card Fraud Detection Techniques: A Survey

Fraud events are increasing on day to day bases. Credit card fraud is the most frequent fraud that is making a big financial loss on a global level. Researchers have implemented many machine learning algorithms to detect the credit card frauds. This research has briefly described several algorithms and compares the performance of Random Forest, Naïve Bayes, K-Nearest Neighbor, Logistic Regression and Multilayer Perceptron. Algorithms are also used to classify the real transactions or fraudulent transactions. These datasets are compared on the basis of accuracy, precision, recall & false positive rate. Comparison results show that Random Forest performs best in credit card fraud detection dataset among others. Research shows that any ML algorithm can be used to demonstrate the classification of fraud detection.

Content

Author and article information

Journal

Title: ScienceOpen Preprints

Publisher: ScienceOpen

Publication date (Electronic preprint): 6 July 2022

Affiliations

[1 ] SST, University of Management and Technology, University of Management & Technology (UMT) C-II Block C 2 Phase 1 Johar Town, Lahore, Punjab 54770, Pakistan

Author notes

[* ]Email: hazeelbutt2016@ 123456gmail.com .

Author information

Muhammad Hazeel Ahmed https://orcid.org/0000-0003-3869-8558

Article

DOI: 10.14293/S2199-1006.1.SOR-.PPFI7P0.v1

SO-VID: 80a0f97f-c092-4e0c-a37a-834e1e91adfb

License:

This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

History

Date received : 6 July 2022

Data availability: The datasets generated during and/or analysed during the current study are available in the repository: https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud

ScienceOpen disciplines: Security & Cryptology

Keywords: Fraud detection,Random forest,Fraudulent behavior detection,Security

References

Abdallah Aisha, Maarof Mohd Aizaini, Zainal Anazida. Fraud detection system: A survey. Journal of Network and Computer Applications. Vol. 68:90–113. 2016. Elsevier BV. [Cross Ref]
Awoyemi John O., Adetunmbi Adebayo O., Oluwadare Samuel A.. Credit card fraud detection using machine learning techniques: A comparative analysis. 2017 International Conference on Computing Networking and Informatics (ICCNI). 2017. IEEE. [Cross Ref]
Xuan Shiyang, Liu Guanjun, Li Zhenchuan, Zheng Lutao, Wang Shuo, Jiang Changjun. Random forest for credit card fraud detection. 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC). 2018. IEEE. [Cross Ref]
Varmedja Dejan, Karanovic Mirjana, Sladojevic Srdjan, Arsenovic Marko, Anderla Andras. Credit Card Fraud Detection - Machine Learning methods. 2019 18th International Symposium INFOTEH-JAHORINA (INFOTEH). 2019. IEEE. [Cross Ref]
Bagga Siddhant, Goyal Anish, Gupta Namita, Goyal Arvind. Credit Card Fraud Detection using Pipeling and Ensemble Learning. Procedia Computer Science. Vol. 173:104–112. 2020. Elsevier BV. [Cross Ref]
Saleh Hussein Ameer, Salah Khairy Rihab, Mohamed Najeeb Shaima Miqdad, Alrikabi Haider Th.Salim. Credit Card Fraud Detection Using Fuzzy Rough Nearest Neighbor and Sequential Minimal Optimization with Logistic Regression. International Journal of Interactive Mobile Technologies (iJIM). Vol. 15(05)2021. International Association of Online Engineering (IAOE). [Cross Ref]
Kundu Amlan, Sural Shamik, Majumdar A. K.. Two-Stage Credit Card Fraud Detection Using Sequence AlignmentInformation Systems Security. p. 260–275. 2006. Springer Berlin Heidelberg. [Cross Ref]
Mishra Ankit, Ghorpade Chaitanya. Credit Card Fraud Detection on the Skewed Data Using Various Classification and Ensemble Techniques. 2018 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS). 2018. IEEE. [Cross Ref]
Kulkarni Ajay, Chong Deri, Batarseh Feras A.. Foundations of data imbalance and solutions for a data democracyData Democracy. p. 83–106. 2020. Elsevier. [Cross Ref]

Comments

Comment on this article

[1] Abdallah Aisha, Maarof Mohd Aizaini, Zainal Anazida. Fraud detection system: A survey. Journal of Network and Computer Applications. Vol. 68:90–113. 2016. Elsevier BV. [Cross Ref]

[2] Awoyemi John O., Adetunmbi Adebayo O., Oluwadare Samuel A.. Credit card fraud detection using machine learning techniques: A comparative analysis. 2017 International Conference on Computing Networking and Informatics (ICCNI). 2017. IEEE. [Cross Ref]

[3] Xuan Shiyang, Liu Guanjun, Li Zhenchuan, Zheng Lutao, Wang Shuo, Jiang Changjun. Random forest for credit card fraud detection. 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC). 2018. IEEE. [Cross Ref]

[4] Varmedja Dejan, Karanovic Mirjana, Sladojevic Srdjan, Arsenovic Marko, Anderla Andras. Credit Card Fraud Detection - Machine Learning methods. 2019 18th International Symposium INFOTEH-JAHORINA (INFOTEH). 2019. IEEE. [Cross Ref]

[5] Bagga Siddhant, Goyal Anish, Gupta Namita, Goyal Arvind. Credit Card Fraud Detection using Pipeling and Ensemble Learning. Procedia Computer Science. Vol. 173:104–112. 2020. Elsevier BV. [Cross Ref]

[6] Saleh Hussein Ameer, Salah Khairy Rihab, Mohamed Najeeb Shaima Miqdad, Alrikabi Haider Th.Salim. Credit Card Fraud Detection Using Fuzzy Rough Nearest Neighbor and Sequential Minimal Optimization with Logistic Regression. International Journal of Interactive Mobile Technologies (iJIM). Vol. 15(05)2021. International Association of Online Engineering (IAOE). [Cross Ref]

[7] Kundu Amlan, Sural Shamik, Majumdar A. K.. Two-Stage Credit Card Fraud Detection Using Sequence AlignmentInformation Systems Security. p. 260–275. 2006. Springer Berlin Heidelberg. [Cross Ref]

[8] Mishra Ankit, Ghorpade Chaitanya. Credit Card Fraud Detection on the Skewed Data Using Various Classification and Ensemble Techniques. 2018 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS). 2018. IEEE. [Cross Ref]

[9] Kulkarni Ajay, Chong Deri, Batarseh Feras A.. Foundations of data imbalance and solutions for a data democracyData Democracy. p. 83–106. 2020. Elsevier. [Cross Ref]