Credit cards play a vital role in the financial transactions where it becomes inescapable portion for family, industry and worldwide actions. Although utilizing credit card offers enormous advantages when utilized carefully and liability, fraudulent activities can cause significant credit and financial harm. Several approaches have been suggested to counter the growth of credit card fraudulence, such as k-nearest neighbor, Random Forest, Neural Network, logistic regression, decision tree, hidden Markov model, naïve bayes, Support Vector Machine, or those represent the hybrid approaches etc. The techniques for fraudulence discovery track the user's actions and warn the user when detrimental incident happens. Fraudulence is inhuman behavior and there are an infinite number of various ways of fraud. Thus, based on previous research, various approaches and strategies will be investigated and addressed. In this paper we will learn how to tackle fraud and how to trust the hospital, organizations, and businesses as well insurance companies against fraudulence by Fraud Detection Systems (FDSs) and how to encounter them via Fraud Prevention Systems (FPSs). This survey presents a comprehensive reviewing for the fraudulence matter in credit card, fraud types and challenges that obstruct the performance of FDSs.
REFERENCES
1.
Raghavan
, P.
, & Gayar
, N. El.
(2019
). Fraud Detection using Machine Learning and Deep Learning
. Proceedings of 2019 International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2019
, 334
–339
. .2.
S.
G
and J. R.
R
, “A Study on Credit Card Fraud Detection using Data Mining Techniques
,” Int. J. Data Min. Tech. Appl.
, vol. 7
, no. 1
, pp. 21
–24
, 2018
, doi: .3.
Z. Z. Samaneh
Sorournejad
, R. E.
Atani
, and A. H.
Monadjemi
, “A survey of credit card fraud detection techniques: Data and technique oriented perspective
,” 2016
.4.
Assis
, C. A. S.
, Pereira
, A. C. M.
, Pereira
, M. A.
, & Carrano
, E. G.
(2014
). A genetic programming approach for fraud detection in electronic transactions
. 2014 IEEE Symposium on Computational Intelligence in Cyber Security (CICS
), 1
–8
.5.
H. C. E.
Sonepat
and M.
Bansal
, “Survey Paper on Credit Card Fraud Detection
,” Int. J. Adv. Res. Comput. Eng. Technol.
, vol. 3
, no. 3
, 2014
.6.
K.
Modi
and R.
Dayma
, “Review on fraud detection methods in credit card transactions
,” in 2017 International Conference on Intelligent Computing and Control (I2C2
), 2017
, pp. 1
–5
.7.
J. O.
Awoyemi
, A. O.
Adetunmbi
, and S. A.
Oluwadare
, “Credit card fraud detection using machine learning techniques: A comparative analysis
,” in 2017 International Conference on Computing Networking and Informatics (ICCNI
), 2017
, pp. 1
–9
.8.
Z.
Kazemi
and H.
Zarrabi
, “Using deep networks for fraud detection in the credit card transactions
,” in 2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI
), 2017
, pp. 630
–633
.9.
B.
Pushpalatha
and C. W.
Joseph
, “Credit card Fraud Detection based on the transaction by using Data mining techniques
,” Int. J. Innov. Res. Comput. Commun. Eng.
, vol. 5
, no. 2
, pp. 1785
–1794
, 2017
.10.
G.
Suresh
and R. J.
Raj
, “A study on credit card fraud detection using data mining techniques
,” Int. J. Data Min. Tech. Appl.
, vol. 7
, no. 1
, pp. 21
–24
, 2018
.11.
Mahajan
, Mehak
and Sandeep
Sharma
, “Detect Frauds in Credit Card using Data Mining Techniques
,” International Journal of Innovative Technology and Exploring Engineering.
(2019
): 48914895
.12.
O.
Adepoju
, J.
Wosowei
, S.
Lawte
, and H.
Jaiman
, “Comparative Evaluation of Credit Card Fraud Detection Using Machine Learning Techniques
,” 2019 Glob. Conf. Adv. Technol. GCAT 2019
, pp. 1
–6
, 2019
, doi: .13.
Abdallah
, A.
, Maarof
, M. A.
, & Zainal
, A.
(2016
). Fraud detection system: A survey
. Journal of Network and Computer Applications
, 68
, 90
–113
.14.
Ghobadi
, F.
, & Rohani
, M.
(2016
). Cost sensitive modeling of credit card fraud using neural network strategy
. 2016 2nd International Conference of Signal Processing and Intelligent Systems (ICSPIS
), 1
–5
.15.
Jain
, Y.
, NamrataTiwari
, S.
, & Jain
, S.
(2019
). A comparative analysis of various credit card fraud detection techniques
. International Journal of Recent Technology and Engineering
, 7
(5
), 402
–407
.16.
Lal
, V.
, & Choubey
, S.
(2015
). A Survey of Fraud Detection Techniques for Credit Card Based Transaction Processing
. Recent Trends in Parallel Computing
, 2
(1
), 10
–15
.17.
Bhatia
, S.
, Bajaj
, R.
, & Hazari
, S.
(2016
). Analysis of credit card fraud detection techniques
. International Journal of Science and Research
, 5
(3
), 1302
–1307
.18.
Lepoivre
, M. R.
, Avanzini
, C. O.
, Bignon
, G.
, Legendre
, L.
, & Piwele
, A. K.
(2016
). Credit card fraud detection with unsupervised algorithms
. Journal of Advances in Information Technology
, 7
(1
).19.
Kumari
, P.
, & Mishra
, S. P.
(2019
). Analysis of credit card fraud detection using fusion classifiers. In Computational Intelligence in Data Mining
(pp. 111
–122
). Springer
.20.
Bhatia
, S.
, Bajaj
, R.
, & Hazari
, S.
(2016
). Analysis of credit card fraud detection techniques
. International Journal of Science and Research
, 5
(3
), 1302
–1307
.21.
A.
Magalla
, “Security, Prevention and Detection of Cyber Crimes,” Tumaini Univ. Iringa Univ. Coll. Cyber Crime. Prep. By Asherry Magalla (LL. M-ICT LAW-10919)
, Supervised by Dr. Puluru, 2013
.22.
Nahar
, A.
, Roy
, S.
, & Hasan
, S. S.
(2016
). A Survey on Different Approaches used for Credit Card Fraud Detection
. Int. J. of Applied Information Systems (IJAIS)
Vol, 10
.23.
Saravanan
, P.
, Subramaniyaswamy
, V.
, Sivaramakrishnan
, N.
, Prakash
, M. A.
, & Arunkumar
, T.
(2014
). Data mining approach for subscription-fraud detection in telecommunication sector
. Contemporary Engineering Sciences
, 7
(11
), 515
–522
.24.
Dhingra
, S.
(2019
). Comparative Analysis of algorithms for Credit Card Fraud Detection using Data Mining: A Review
. Journal of Advanced Database Management & Systems
, 6
(2
), 12
–17
.25.
Ai
, J.
, Brockett
, P. L.
, Golden
, L. L.
, & Guillén
, M.
(2013
). A robust unsupervised method for fraud rate estimation
. Journal of Risk and Insurance
, 80
(1
), 121
–143
.26.
Bauder
, R.
, Khoshgoftaar
, T. M.
, & Seliya
, N.
(2017
). A survey on the state of healthcare upcoding fraud analysis and detection
. Health Services and Outcomes Research Methodology
, 17
(1
), 31
–55
.27.
J.
Akhilomen
, “Data mining application for cyber credit-card fraud detection system
,” in Industrial Conference on Data Mining
, 2013
, pp. 218
–228
.28.
Raghupathi
, W.
(2016
). Data mining in healthcare. Healthcare Informatics: Improving Efficiency through Technology
, Analytics, and Management
, 353
–372
.29.
Yang
, X.
(2021
). Algorithm Study of New Association Rules and Classification Rules in Data Mining
. Journal of Physics: Conference Series
, 1732
(1
), 12070
.30.
Lakshmi
, S.
, & Kavilla
, S. D.
(2018
). Machine learning for credit card fraud detection system
. International Journal of Applied Engineering Research
, 13
(24 Pt. 1
), 16819
–16824
.31.
Carneiro
, N.
, Figueira
, G.
, & Costa
, M.
(2017
). A data mining based system for credit-card fraud detection in e-tail
. Decision Support Systems
, 95
, 91
–101
. .32.
P.
Saravanan
, V.
Subramaniyaswamy
, N.
Sivaramakrishnan
, M. A.
Prakash
, and T.
Arunkumar
, “Data mining approach for subscription-fraud detection in telecommunication sector
,” Contemp. Eng. Sci.
, vol. 7
, no. 11
, pp. 515
–522
, 2014
.33.
Amin
, M. M.
, Zainal
, A.
, Azmi
, N. F. M.
, & Ali
, N. A.
(2020
). Detecting Telecommunication Fraud with Visual Analytics: A Review
. IOP Conference Series: Materials Science and Engineering
, 884
(1
), 12059
.34.
J. V.
Devi
and K. S.
Kavitha
, “Fraud detection in credit card transactions by using classification algorithms
,” in 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC
), 2017
, pp. 125
–131
.35.
P.
Save
, P.
Tiwarekar
, K. N.
Jain
, and N.
Mahyavanshi
, “A novel idea for credit card fraud detection using decision tree
,” Int. J. Comput. Appl.
, vol. 161
, no. 13
, 2017
.36.
Khan
, M. A.
, & Medvedev
, M.
(2017
). A REVIEW OF FRAUD DETECTION TECHNIQUES: CREDIT CARD ABSTRACT
, 517
–526
.37.
Gnecco
, G.
(2018
). Symmetric and antisymmetric properties of solutions to kernel-based machine learning problems
. Neurocomputing
, 306
, 141
–159
.38.
M.
Zareapoor
and P.
Shamsolmoali
, “Application of credit card fraud detection: Based on bagging ensemble classifier
,” Procedia Comput. Sci.
, vol. 48
, no. 2015
, pp. 679
–685
, 2015
.39.
N.
Malini
and M.
Pushpa
, “Analysis on credit card fraud identification techniques based on KNN and outlier detection
,” in 2017 Third International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB
), 2017
, pp. 255
–258
.40.
Mao
, S.
, Tao
, D.
, Zhang
, G.
, Ching
, P. C.
, & Lee
, T.
(2019
). Revisiting hidden Markov models for speech emotion recognition
. ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP
), 6715
–6719
.41.
Robinson
, W. N.
, & Aria
, A.
(2018
). Sequential fraud detection for prepaid cards using hidden Markov model divergence
. Expert Systems with Applications
, 91
, 235
–251
.42.
M.
Zekić-Sušac
, N.
Šarlija
, A.
Has
, and A.
Bilandžić
, “Predicting company growth using logistic regression and neural networks
,” Croat. Oper. Res. Rev.
, vol. 7
, no. 2
, pp. 229
–248
, 2016
.43.
Planton
, S.
, van Kerkoerle
, T.
, Abbih
, L.
, Maheu
, M.
, Meyniel
, F.
, Sigman
, M.
, Wang
, L.
, Figueira
, S.
, Romano
, S.
, & Dehaene
, S.
(2020
). Mental compression of binary sequences in a language of thought
.44.
Devi
, J. V.
, & Kavitha
, K. S.
(2017
). Fraud detection in credit card transactions by using classification algorithms
. 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC
), 125
–131
.45.
R. D.
Patel
and D. K.
Singh
, “Credit card fraud detection & prevention of fraud using genetic algorithm
,” Int. J. Soft Comput. Eng.
, vol. 2
, no. 6
, pp. 292
–294
, 2013
.46.
Wang
, Z.
, Joshi
, S.
, Savel’ev
, S.
, Song
, W.
, Midya
, R.
, Li
, Y.
, Rao
, M.
, Yan
, P.
, Asapu
, S.
, & Zhuo
, Y.
(2018
). Fully memristive neural networks for pattern classification with unsupervised learning
. Nature Electronics
, 1
(2
), 137
–145
.47.
Fu
, K.
, Cheng
, D.
, Tu
, Y.
, & Zhang
, L.
(2016
). Credit card fraud detection using convolutional neural networks
. Inmternational Conference on Neural Information Processing
, 483
–490
.48.
S.
Hak
, S.
Singh
, and V.
Purohit
, “Credit Card Fraud Detection Using Advanced Combination Heuristic and Bayes’ Theorem
,” Int. J. Innov. Res. Comput. Commun. Eng.
, vol. 3
, no. 4
, pp. 2756
–2763
, 2015
.49.
Georgieva
, S.
, Markova
, M.
, & Pavlov
, V.
(2019
). Using neural network for credit card fraud detection
. AIP Conference Proceedings
, 2159
(1
), 30013
.50.
Kim
, E.
, Lee
, J.
, Shin
, H.
, Yang
, H.
, Cho
, S.
, Nam
, S.
, Song
, Y.
, Yoon
, J.
, & Kim
, J.
(2019
). Champion-challenger analysis for credit card fraud detection: Hybrid ensemble and deep learning
. Expert Systems with Applications
, 128
, 214
–224
.51.
Karthikeyan
, K.
, & Mala
, R.
(2016
). Artificial Neural Content Techniques for Enhanced Intrusion Detection and Prevention System
. Int. J. Innov. Res. Adv. Eng
, 3
(04
), 96
–104
.52.
Gulati
, A.
, Dubey
, P.
, MdFuzail
, C.
, Norman
, J.
, & Mangayarkarasi
, R.
(2017
). Credit card fraud detection using neural network and geolocation
. IOP Conference Series: Materials Science and Engineering
, 263
(4
), 42039
.53.
Bouazza
, I.
, & Ameur
, F.
(2018
). Datamining for Fraud Detecting, State of the Art
. International Conference on Advanced Intelligent Systems for Sustainable Development
, 205
–219
.54.
Lu
, H.
, Wang
, H.
, & Yoon
, S. W.
(2017
). Real time credit card default classification using adaptive boosting-based online learning algorithm
. IIE Annual Conference. Proceedings
, 422
–427
.55.
Kim
, E.
, Lee
, J.
, Shin
, H.
, Yang
, H.
, Cho
, S.
, Nam
, S.
, Song
, Y.
, Yoon
, J.
, & Kim
, J.
(2019
). Champion-challenger analysis for credit card fraud detection: Hybrid ensemble and deep learning
. Expert Systems with Applications
, 128
, 214
–224
.
This content is only available via PDF.
© 2022 Author(s).
2022
Author(s)
You do not currently have access to this content.
