Fraud is defined as any malicious behavior with the intent of causing financial loss to a third party[1]. Due to the unprecedented pandemic scenario prevailing over the world in the past 2 years, there has been rapid participation in online-based transactional activities using credit cards and it has coerced many people into the digital world[2].This increased utilization of advanced digital cash or plastic cash even in new nations has highlighted increased fraud cases related to credit cards[3]. Obviously, this elevated activity has resulted in criminals getting more attracted to it[4]. As part of comprehensive fraud prevention, fraud detection helps to reduce the manual aspects of the screening/checking process and makes the process automatic. This is now one of the most well-known industry/government issues[5]. Detection of potentially fraudulent transactions is the main goal, so as the customers are not charged for the products which were not purchased by them. Additionally, we have to reduce the business costs and make it profitable for credit cards companies. The many strategies used to identify credit card frauds are investigated in this study. This paper examines the algorithms used for detecting fraud related to credit cards, as well as their advantages and disadvantages, along with performance comparisons that will aid in the evaluation of their usability and accuracy.
REFERENCES
1.
Y.
Jain
, N.
Tiwari
, S.
Dubey
, and S.
Jain
, “A comparative analysis of various credit card fraud detection techniques
,” Int. J. Recent Technol. Eng.
, vol. 7
, no. 5
, pp. 402
–407
, 2019
.2.
Research and Markets
, “Credit Card Users in India 2020: A Comparison of Leading Brands
,” 2020
. https://www.globenewswire.com/news-release/2020/11/03/2119495/0/en/Credit-Card-Users-in-India-2020-A-Comparison-of-Leading-Brands.html (accessed Oct. 14, 2021).3.
L.
Nathaniel
, “Credit card fraud is on the rise due to Covid pandemic
.” https://www.cnbc.com/2021/01/27/credit-card-fraud-is-on-the-rise-due-to-covid-pandemic.html (accessed Oct. 14, 2021
).4.
R.
Ranga
, “Rise of financial fraud in a Covid-19 world - Maveric Systems
.” https://maveric-systems.com/news-events/rise-of-financial-fraud-in-a-covid-19-world/ (accessed Oct. 14, 2021
).5.
C.
Phua
, V.
Lee
, K.
Smith
, and R.
Gayler
, “A Comprehensive Survey of Data Mining-based Fraud Detection Research
,” 2010
, doi: .6.
Tandon
Simran
, “Fraudulent Online Transactions: Liability Of Banks!! - Finance and Banking - India
.” https://www.mondaq.com/india/financial-services/1030852/fraudulent-online-transactions-liability-of-banks (accessed Oct. 14, 2021
).7.
S.
Bagga
, A.
Goyal
, N.
Gupta
, and A.
Goyal
, “Credit Card Fraud Detection using Pipeling and Ensemble Learning
,” Procedia Comput. Sci.
, vol. 173
, pp. 104
–112
, 2020
, doi: .8.
S.
Misra
, S.
Thakur
, M.
Ghosh
, and S. K.
Saha
, “An Autoencoder Based Model for Detecting Fraudulent Credit Card Transaction
,” Procedia Comput. Sci.
, vol. 167
, pp. 254
–262
, 2020
, doi: .9.
Meenu
, Gupta
Swati
, Patel
Sanjay
, Kumar
Surender
, and Chauhan
Goldi
, “Anomaly Detection in Credit Card Transactions using Machine Learning
,” Int. J. Innov. Res. Comput. Sci. Technol.
, vol. 8
, no. 3
, pp. 67
–71
, May 2020, doi: .10.
S.
Makki
, Z.
Assaghir
, Y.
Taher
, R.
Haque
, M. S.
Hacid
, and H.
Zeineddine
, “An Experimental Study With Imbalanced Classification Approaches for Credit Card Fraud Detection
,” IEEE Access
, vol. 7
, pp. 93010
–93022
, 2019
, doi: .11.
S.
Xuan
, G.
Liu
, Z.
Li
, L.
Zheng
, S.
Wang
, and C.
Jiang
, “Random forest for credit card fraud detection
,” ICNSC 2018 - 15th IEEE Int. Conf. Networking, Sens. Control
, pp. 1
–6
, May 2018
, doi: .12.
N.
Rtayli
and N.
Enneya
, “Selection Features and Support Vector Machine for Credit Card Risk Identification
,” Procedia Manuf.
, vol. 46
, pp. 941
–948
, Jan. 2020
, doi: .13.
H. Z.
Alenzi
and N. O.
Aljehane
, “Fraud Detection in Credit Cards using Logistic Regression
,” Int. J. Adv. Comput. Sci. Appl.
, vol. 11
, no. 12
, pp. 540
–551
, 2020
, doi: .14.
A. A.
Khine
and H. W.
Khin
, “Credit Card Fraud Detection Using Online Boosting with Extremely Fast Decision Tree
,” 2020 IEEE Conf. Comput. Appl. ICCA 2020
, Feb. 2020
, doi: .15.
D.
Kaur
and S.
kaur
, “Machine Learning Approach for Credit Card Fraud Detection (KNN & Naïve Bayes
),” SSRN Electron. J.
, Apr. 2020
, doi: .16.
M. S.
Kumar
, V.
Soundarya
, S.
Kavitha
, E. S.
Keerthika
, and E.
Aswini
, “Credit Card Fraud Detection Using Random Forest Algorithm
,” 2019 Proc. 3rd Int. Conf. Comput. Commun. Technol. ICCCT 2019
, pp. 149
–153
, Feb. 2019
, doi: .17.
J. O.
Awoyemi
, A. O.
Adetunmbi
, and S. A.
Oluwadare
, “Credit card fraud detection using machine learning techniques: A comparative analysis
,” Proc. IEEE Int. Conf. Comput. Netw. Informatics, ICCNI 2017
, vol. 2017
-January, pp. 1
–9
, Nov. 2017
, doi: .18.
A.
Mishra
and C.
Ghorpade
, “Credit Card Fraud Detection on the Skewed Data Using Various Classification and Ensemble Techniques
,” 2018 IEEE Int. Students’ Conf. Electr. Electron. Comput. Sci. SCEECS 2018
, Nov. 2018
, doi: .19.
S. D.
Kavila
, “Machine Learning For Credit Card Fraud Detection System
,” Int. J. Appl. Eng. Res.
, pp. 16819
–16824
, 2018
.20.
Chuprina
Roman
, “Credit Card Fraud Detection: Top ML Solutions in 2021.” https://spd.group/machine-learning/credit-card-fraud-detection/
(accessed Oct. 14, 2021
).21.
altexsoft
, “Credit Card Payment Processing & Gateways from American Express, Visa, Mastercard and others | AltexSoft
.” https://www.altexsoft.com/blog/online-payment-processing/ (accessed Oct. 14, 2021
).22.
S P
Maniraj
, Aditya
Saini
, Shadab
Ahmed
, and Swarna Deep
Sarkar
, “Credit Card Fraud Detection using Machine Learning and Data Science
,” Int. J. Eng. Res.
, vol. 08
, no. 09
, Sep. 2019
, doi: .23.
V. N.
Dornadula
and S.
Geetha
, “Credit Card Fraud Detection using Machine Learning Algorithms
,” Procedia Comput. Sci.
, vol. 165
, pp. 631
–641
, Jan. 2019
, doi: .24.
V. N.
Gudivada
, J.
Ding
, and A.
Apon
, “Data Quality Considerations for Big Data and Machine Learning: Going Beyond Data Cleaning and Transformations
,” Int. J. Adv. Softw.
, vol. 10
, no. 1
, pp. 1
–20
, 2017
, [Online. Available: https://www.researchgate.net/profile/Junhua-Ding/publication/318432363_Data_Quality_Considerations_for_Big_Data_and_Machine_Learni ng_Going_Beyond_Data_Cleaning_and_Transformations/links/59ded28b0f7e9bcfab244bdf/Dat a-Quality-Considerations-for-Big-Data-and.25.
D.
Varmedja
, M.
Karanovic
, S.
Sladojevic
, M.
Arsenovic
, and A.
Anderla
, “Credit Card Fraud Detection - Machine Learning methods
,” 2019 18th Int. Symp. INFOTEH-JAHORINA, INFOTEH 2019 - Proc.
, May 2019
, doi: .26.
Y.
Xu
and R.
Goodacre
, “On Splitting Training and Validation Set: A Comparative Study of Cross-Validation, Bootstrap and Systematic Sampling for Estimating the Generalization Performance of Supervised Learning
,” J. Anal. Test. 2018 23
, vol. 2
, no. 3
, pp. 249
–262
, Oct. 2018
, doi: .27.
Brownlee
Jason
, “Cost-Sensitive Learning for Imbalanced Classification
.” https://machinelearningmastery.com/cost-sensitive-learning-for-imbalanced-classification/ (accessed Oct. 14, 2021
).28.
J.
Jurgovsky
, “Context-aware credit card fraud detection
,” Univ. Lyon; Univ. Passau, Dtsch.
, pp. 1
–181
, Dec. 2019
, Accessed: Oct. 14, 2021. [Online]. Available: http://www.theses.fr/2019LYSEI109.29.
“
Data Science Project - Detect Credit Card Fraud with Machine Learning in R - DataFlair
.” https://data-flair.training/blogs/data-science-machine-learning-project-credit-card-fraud-detection/ (accessed Oct. 14, 2021
).30.
R. S.
More
, C. J.
Awati
, S. K.
Shirgave
, R. J.
Deshmukh
, and S. S.
Patil
, “Credit Card Fraud Detection Using Supervised Learning Approach
,” Int. J. Sci. Technol. Res.
, vol. 9
, no. 10
, pp. 216
–219
, 2020
, Accessed: Oct. 14, 2021. [Online]. Available: www.ijstr.org.31.
N.
Malini
and M.
Pushpa
, “Analysis on credit card fraud identification techniques based on KNN and outlier detection
,” Proc. 3rd IEEE Int. Conf. Adv. Electr. Electron. Information, Commun. Bio-Informatics, AEEICB 2017
, pp. 255
–258
, Jul. 2017
, doi: .32.
Y.
Kumar
, S.
Saini
, and R.
Payal
, “Comparative Analysis for Fraud Detection Using Logistic Regression, Random Forest and Support Vector Machine
,” SSRN Electron. J.
, no. December
, 2021
, doi: .33.
B. M.
Devi
, B.
Janani
, S.
Gayathri
, and N.
Indira
, “Credit Card Fraud Detection Using Random Forest Algorithm
,” Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol.
, pp. 301
–306
, 2019
, doi: .34.
E.-A.
MINASTIREANU
and G.
MESNITA
, “Reducing Type Ii Errors in Credit Card Fraud Detection Using Xgboost Classifier
,” Proc. 19th Int. Conf. INFORMATICS Econ. Educ. Res. Bus. Technol.
, vol. 2020
, pp. 174
–182
, 2020
, doi: .35.
S.
Sorournejad
, Z.
Zojaji
, R. E.
Atani
, and A.
Monadjemi
, “A Survey of Credit Card Fraud Detection Techniques: Data and Technique Oriented Perspective
,” Comput. Sci. Proj.
, pp. 1
–26
, 2016
.36.
Saloni
and M.
Rout
, “Analysis and Comparison of Credit Card Fraud Detection Using Machine Learning
,” Lect. Notes Electr. Eng.
, vol. 709
, pp. 33
–40
, 2021
, doi: .37.
H. M and S. M.N
, “A Review on Evaluation Metrics for Data Classification Evaluations
,” Int. J. Data Min. Knowl. Manag. Process
, vol. 5
, no. 2
, pp. 01
–11
, Mar. 2015
, doi: .38.
Narkhede
Sarang
, “Understanding Confusion Matrix | by Sarang Narkhede | Towards Data Science
,” 2018
. https://towardsdatascience.com/understanding-confusion-matrix-a9ad42dcfd62 (accessed Oct. 14, 2021).39.
destoyer-byte
, “Model Evaluation Techniques|Machine Learning Model Evaluation
.” https://www.analyticsvidhya.com/blog/2021/05/machine-learning-model-evaluation/ (accessed Oct. 14, 2021
).40.
ULB
, “ULB Machine Learning Group | Computer Science Department
.” https://mlg.ulb.ac.be/wordpress/ (accessed Oct. 14, 2021
).41.
V. V.
Madhav
and K. A.
Kumari
, “Analysis of Credit Card Fraud Data using PCA
,” vol. 10
, no. 1
, pp. 10
–14
, 2020
.42.
Brownlee
Jason
, “Train-Test Split for Evaluating Machine Learning Algorithms
.” https://machinelearningmastery.com/train-test-split-for-evaluating-machine-learning-algorithms/ (accessed Oct. 14, 2021
).43.
S.
Rajora
et al., “A Comparative Study of Machine Learning Techniques for Credit Card Fraud Detection Based on Time Variance
,” Proc. 2018 IEEE Symp. Ser. Comput. Intell. SSCI 2018
, pp. 1958
–1963
, Jan. 2019
, doi: .44.
F. Pedregosa
FABIANPEDREGOSA
et al., “Scikit-learn: Machine Learning in Python Gaël Varoquaux Bertrand Thirion Vincent Dubourg Alexandre Passos PEDREGOSA, VAROQUAUX, GRAMFORT ET AL. Matthieu Perrot
,” J. Mach. Learn. Res.
, vol. 12
, pp. 2825
–2830
, 2011
, Accessed: Oct. 14, 2021. [Online]. Available: http://scikit-learn.sourceforge.net.
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