Analysis and prediction for credit card fraud detection dataset using data mining approaches

G. K. Arun; P. Rajesh

doi:10.53730/ijhs.v6nS5.9534

Authors

G. K. Arun Department of Computer and Information Science, Annamalai University, Annamalai Nagar – 608 002, Tamil Nadu, India
P. Rajesh
rajeshdatamining@gmail.com
PG Department of Computer Science, Government Arts College, Chidambaram – 608 102 (Deputed from Department of Computer and Information Science, Annamalai University, Annamalai Nagar), Tamil Nadu, India

Keywords:

Credit Card, Data Mining, Regression Model, Normalization, Correlation Coefficient

Abstract

Analysis of fraud detection is one of the recent research areas in data analytics. Credit card fraud is rapidly increasing in different ways significantly with modern technology. The Credit card consumers and processing financial company have losing large amount annually. The fraudsters rapidly and continuously try to find and follow novel technologies to commit the illegal actions against consumers. In COVID-19 pandemic situations online shopping trends growing exponentially and parallelly increase Credit card fraud rapidly. In this paper we trained different data mining techniques with statistical approaches for credit card fraud detection. Numerical illustrations to prove the proposed results.

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References

J. Leskovec, A. Rajaraman and J. D. Ullman, Mining of massive data sets, Cambridge university press. (2020).

P. K. Chan, W. Fan, A. L. Prodromidis and S. J. Stolfo, Distributed data mining in credit card fraud detection, IEEE Intel. Sys. and their App. 14(6) (1999) 67-74.

I. Mekterovic, M. Karan, D. Pintar and L. Brkic, Credit card fraud detection in card-not-present transactions: where to invest, Appli. Sci. 11(15) (2021) 6766

(2022) Statista retail e-commerce sales worldwide from 2014 to 2023 (in Billion U.S. Dollars). [online]. Available: https://www.statista.com/statistics/379046/worldwide-retail-e-commerce-sales

(2022) Statista value of annual losses on “card-not present” fraud on uk-issued debit and credit cards in the United Kingdom (UK) from 2002 to 2019. [online]. Available: https://www.statista.com/statistics/ 286245/united-kingdom-uk-card-not-present-fraudlosses/).

E. C. Bank, Sixth report on card fraud, European central bank: Frankfurt, Germany. (2020).

A. Dal Pozzolo, C. Andrea, L. Olivier, W. Yann-Ael, B. Serge and Gianluca, learned lessons in credit card fraud detection from a practitioner perspective, Exp. Syst. with Appli. 41(10) (2014) 4915-4928.

J. O. Awoyemi, A. O. Adetunmbi and S. A. Oluwadare, Credit card fraud detection using machine learning techniques: A comparative analysis, IEEE Int. Con. on Comp. Net. and Info. (2014) 1-9.

D. J. Hand, C. Whitrow, N. M. Adams, P. Juszczak and D. J. Weston, performance criteria for plastic card fraud detection tools, J. of the Oper. Res. Soc. 59(7) (2007) 956–962.

(2022) Binary classifier evaluation matrices: error rate, KS statistics, AUROC, (online), Available: http://rstudio-pubs-static.s3.amazonaws.com/303414_fb0a43efb0d7433983fdc9adcf87317f.htm.

A. Correa Bahnsen, D. Aouada, A. Stojanovic, B. Ottersten, Feature engineering strategies for credit card fraud detection, Exp. Sys. with Appli. 51 (2016) 134–142.

J. Jurgovsky, M. Granitzer, K. Ziegler, S. Calabretto, P. E. Portier, L. He-Guelton and O. Caelen, Sequence classification for credit-card fraud detection, Exp. Sys. with Appli. 100 (2018) 234-245.

L. Breiman, Random forests. Mach. Lear. 45(1) (2001) 5-32.

J. R. Quinlan, Induction on decision tree, Mach. Lear. 1(1), (1986) 81-106.

S. Bhattacharyya, S. Jha, K. Tharakunnel and J. C. Westland, Data mining for credit card fraud: a comparative study, Deci. Supp. Sys. 50(3) (2011) 602-613.

S. Xuan, G. Liu, Z. Li, L. Zheng, S. Wang and C. Jiang, Random Forest for credit card fraud detection, IEEE 15th Int. Con. on Net., Sen. and Con. (2018) 1-6.

P. Rajesh and M. Karthikeyan, A comparative study of data mining algorithms for decision tree approaches using WEKA tool, Adv. in Nat. and Appli. Sci. 11(9) (2011) 230-243.

P. Rajesh, M. Karthikeyan, B. Santhosh Kumar and M. Y. Mohamed Parveesh, Comparative Study of Decision Tree Approaches in Data Mining Using Chronic Disease Indicators (CDI) Data, J. of Compu. and Theo. Nano. 16(4) (2019) 1472–1477.

Rinartha, K., & Suryasa, W. (2017). Comparative study for better result on query suggestion of article searching with MySQL pattern matching and Jaccard similarity. In 2017 5th International Conference on Cyber and IT Service Management (CITSM) (pp. 1-4). IEEE.

F. Carcillo, Y. A. Le Borgne, O. Caelen, Y. Kessaci, F. Oble and G. Bontempi, Combining unsupervised and supervised learning in credit card fraud detection, Infor. Sci. 557, (2021) 317-331.

(2022) Credit card fraud detection dataset (online), Available: https://www.kaggle.com/dktalaicha/credit-card-fraud-detection-using-smote-adasyn/data.

(2022) Multiple linear regression, (Online), Available: https://www.investopedia.com/terms/m/mlr.asp

P. Rajesh and M. Karthikeyan, Data mining approaches to predict the factors that affect the agriculture growth using stochastic model, Int. J. of Comp. Sci. and Engg. 7(4) (2019) 18-23.

P. Rajesh and M. Karthikeyan, and R. Arulpavai, Data mining approaches to predict the factors that affect the groundwater level using stochastic model, AIP Con. Proc. 2177(1), (2019) 020079.

P. Rajesh, M. Karthikeyan and R. Arulpavai, Predication of labour demand in agriculture based on comparative study of different data using data mining and stochastic approach, Int. J. of Engg. Sci. Inv. 2 (2018) 86-97.

Ernawati, E., Baso, Y. S., Hidayanty, H., Syarif, S., Aminuddin, A., & Bahar, B. (2022). The effects of anemia education using web-based she smart to improve knowledge, attitudes, and practice in adolescent girls. International Journal of Health & Medical Sciences, 5(1), 44-49. https://doi.org/10.21744/ijhms.v5n1.1831

G. K. Arun and P. Rajesh, Design of metaheuristic feature selection with deep learning based credit card fraud detection model, Second International Conference on Artificial Intelligence and Smart Energy (ICAIS). (2022) 191-197.