A Review on the Performance Analysis of Supervised and Unsupervised algorithms in Credit Card Fraud Detection

Authors

  • Prathima Gamini Assistant Professor, Department of Electronics and Communication Engineering, Sagi Ramakrishnam Engineering College, Bhimavaram, India
  • Sai Tejasri Yerramsetti Student, Department of Electronics and Communication Engineering, Sagi Ramakrishnam Engineering College, Bhimavaram, India
  • Gayathri Devi Darapu Student, Department of Electronics and Communication Engineering, Sagi Ramakrishnam Engineering College, Bhimavaram, India
  • Vamsi Kaladhar Pentakoti Student, Department of Electronics and Communication Engineering, Sagi Ramakrishnam Engineering College, Bhimavaram, India
  • Prudhvi Raju Vegesena Student, Department of Electronics and Communication Engineering, Sagi Ramakrishnam Engineering College, Bhimavaram, India

Keywords:

credit card fraud detection, k-means, Local outlier factor, Neural Network, Random Forest, stacking classifier, Support Vector Machine

Abstract

The detection of credit card fraud is the most common issue encountered in the present scenario. Generally, credit card fraud occurs when a card is stolen and used for unauthorized purposes or even when the card information is misused. This paper provides a review of performance analysis of various machine learning algorithms. Here both supervised and unsupervised learning algorithms are considered for analysis. The accuracy, precision, recall, f1score, and specificity of algorithms are regarded here for analyzing their performance.

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Published

06-08-2021

Issue

Vol. 4 No. 8 (2021)

Section

Articles

License

Copyright (c) 2021 Prathima Gamini, Sai Tejasri Yerramsetti, Gayathri Devi Darapu, Vamsi Kaladhar Pentakoti, Prudhvi Raju Vegesena

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
P. Gamini, S. T. Yerramsetti, G. D. Darapu, V. K. Pentakoti, and P. R. Vegesena, “A Review on the Performance Analysis of Supervised and Unsupervised algorithms in Credit Card Fraud Detection”, IJRESM, vol. 4, no. 8, pp. 23–26, Aug. 2021, Accessed: Apr. 27, 2024. [Online]. Available: https://journal.ijresm.com/index.php/ijresm/article/view/1143