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Real-time Transactions Fraud Detection Via Machine Learning Techniques : A Review

Kapil Dev Tripathi1 , Vikas Singh Rajput2

Section:Review Paper, Product Type: Journal Paper
Volume-8 , Issue-6 , Page no. 51-56, Jun-2020

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v8i6.5156

Online published on Jun 30, 2020

Copyright © Kapil Dev Tripathi, Vikas Singh Rajput . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Kapil Dev Tripathi, Vikas Singh Rajput, “Real-time Transactions Fraud Detection Via Machine Learning Techniques : A Review,” International Journal of Computer Sciences and Engineering, Vol.8, Issue.6, pp.51-56, 2020.

MLA Style Citation: Kapil Dev Tripathi, Vikas Singh Rajput "Real-time Transactions Fraud Detection Via Machine Learning Techniques : A Review." International Journal of Computer Sciences and Engineering 8.6 (2020): 51-56.

APA Style Citation: Kapil Dev Tripathi, Vikas Singh Rajput, (2020). Real-time Transactions Fraud Detection Via Machine Learning Techniques : A Review. International Journal of Computer Sciences and Engineering, 8(6), 51-56.

BibTex Style Citation:
@article{Tripathi_2020,
author = {Kapil Dev Tripathi, Vikas Singh Rajput},
title = {Real-time Transactions Fraud Detection Via Machine Learning Techniques : A Review},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {6 2020},
volume = {8},
Issue = {6},
month = {6},
year = {2020},
issn = {2347-2693},
pages = {51-56},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5145},
doi = {https://doi.org/10.26438/ijcse/v8i6.5156}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v8i6.5156}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5145
TI - Real-time Transactions Fraud Detection Via Machine Learning Techniques : A Review
T2 - International Journal of Computer Sciences and Engineering
AU - Kapil Dev Tripathi, Vikas Singh Rajput
PY - 2020
DA - 2020/06/30
PB - IJCSE, Indore, INDIA
SP - 51-56
IS - 6
VL - 8
SN - 2347-2693
ER -

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Abstract

This paper represents survey of various techniques utilized in Credit Card Fraud Detection (CCFD) mechanisms. There are many new and modern techniques depending upon Neural Network (NN) and Artificial Intelligence (AI), Data mining (DM), Artificial Immune System (AIS), Bayesian Network (BN), Fuzzy Logic Based System, Decision Tree (DT), K- nearest neighbor (KNN) algorithm, Support Vector Machine (SVM), Machine Learning (ML), Genetic Programming (GP) etc., which has developed fraudulent transactions to detect various credit card. Various techniques for the FD system have been explained. The powerful FD system, which detects the fraud, but also detects it in a precise manner, is needed in order to stop these frauds. They also need to make our systems learn about or adapt to future new methods of fraud from past frauds. The concept of CC fraud, or its different types, has been introduced in this paper.

Key-Words / Index Term

Real-time Fraud Detection, Fraud Detection System (FDS), Machine Learning (ML), CCFD Techniques, CCF

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