Impact Factor:6.549
 Scopus Suggested Journal: Tracking ID for this title suggestion is: 55EC484EE39417F0

International Journal
of Computer Engineering in Research Trends (IJCERT)

Scholarly, Peer-Reviewed, Open Access and Multidisciplinary

Welcome to IJCERT

International Journal of Computer Engineering in Research Trends. Scholarly, Peer-Reviewed,Open Access and Multidisciplinary

ISSN(Online):2349-7084                 Submit Paper    Check Paper Status    Conference Proposal

Back to Current Issues

Creditcard Fraud Detection and Classification Using Machine Learning Based Classifiers

Y.Yashasree, Dr.K.Venkatesh Sharma, , ,
1M.Tech (Pursuing),CVR College of Engineering , Department of Computer Science and Engineering 2Professor,Department of Computer Science and Engineering, CVR College of Engineering

Nowadays, most transactions take place online, which means that credit cards and other online payment systems are involved. This method is convenient for the company and the customer. The digital age seems to have provided some very useful features that have changed the way businesses and consumers interact, but for a charge. “Credit card fraud” outlays the card industry literally billions of dollars a year. Financial institutions are constantly striving to improve fraud detection systems, but at the same time, fraudsters are finding new ways to break into systems. Preventing and detecting “Credit card fraud” has become an emergency. Data mining techniques can be very useful in detecting financial fraud, as large and complex financial data processing poses major challenges for financial institutions. In recent years, several studies have used machine learning and data mining techniques to combat this problem. The main aim of this paper is to implement the performance of the machine learning based classifiers on Credit card fraud detection dataset.

Y.Yashasree,Dr.K.Venkatesh Sharma."Creditcard Fraud Detection and Classification Using Machine Learning Based Classifiers ". International Journal of Computer Engineering In Research Trends (IJCERT) ,ISSN:2349-7084 ,Vol.7, Issue 9,pp.1-8, September - 2020, URL :,

Keywords : Machine Learning, Credit card fraud, fraud detection, Classifiers.

[1] Dornadula, V. N., & Geetha, S. (2019). Credit Card Fraud Detection using Machine Learning Algorithms. Procedia Computer Science, 165, 631–641. doi:10.1016/j.procs.2020.01.057 
[2] Venkata Suryanarayana, S., N. Balaji, G., & Venkateswara Rao, G. (2018). Machine Learning Approaches for Credit Card Fraud Detection. International Journal of Engineering & Technology, 7(2), 917. doi:10.14419/ijet.v7i2.9356 
[3] Al-Shabi, M. (2019). Credit Card Fraud Detection Using Auto encoder Model in Unbalanced Datasets. Journal of Advances in Mathematics and Computer Science, 33(5), 1-16.
[4] SADGALI, I., SAEL, N., & BENABBOU, F. (2019). Performance of machine learning techniques in the detection of financial frauds. Procedia Computer Science, 148, 45–54. doi:10.1016/j.procs.2019.01.007 
[5] Leo, M., Sharma, S., & Maddulety, K. (2019). Machine Learning in Banking Risk Management: A Literature Review. Risks, 7(1), 29. doi:10.3390/risks7010029 
[6] Sohony, I., Pratap, R., & Nambiar, U. (2018). Ensemble learning for credit card fraud detection. Proceedings of the ACM India Joint International Conference on Data Science and Management of Data - CoDS-COMAD ’18. doi:10.1145/3152494.3156815 
[7] Carcillo, F., Le Borgne, Y.-A., Caelen, O., Kessaci, Y., Oblé, F., & Bontempi, G. (2019). Combining Unsupervised and Supervised Learning in Credit Card Fraud Detection. Information Sciences. doi:10.1016/j.ins.2019.05.042
[8] John O. Awoyemi Department of Computer Science Federal University of Technology Akure, Nigeria
[9] Choi, D., & Lee, K. (2018). An Artificial Intelligence Approach to Financial Fraud Detection under IoT Environment: A Survey and Implementation. Security and Communication Networks, 2018, 1–15. doi:10.1155/2018/5483472 
[10] Popat, R. R., & Chaudhary, J. (2018). A Survey on Credit Card Fraud Detection Using Machine Learning. 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI). doi:10.1109/icoei.2018.8553963.
[11]  Roman Chuprina on April 14, 2020 at 1:30am; Blog, View. "The In-depth 2020 Guide to E-commerce Fraud Detection". Retrieved 2020-05-24.
[12] Velasco, Rafael B.; Carpanese, Igor; Interian, Ruben; Paulo Neto, Octávio C. G.; Ribeiro, Celso C. (2020-05-28). "A decision support system for fraud detection in public procurement". International Transactions in Operational Research: itor.12811. doi:10.1111/itor.12811. ISSN 0969-6016.
[13] Jump up to:a b c d Bolton, R. and Hand, D. (2002). Statistical fraud detection: A review. Statistical Science 17 (3), pp. 235-255
[14] Jump up to:a b G. K. Palshikar, The Hidden Truth – Frauds and Their Control: A Critical Application for Business Intelligence, Intelligent Enterprise, vol. 5, no. 9, 28 May 2002, pp. 46–51.
[15] Vani, G. K. (February 2018). "How to detect data collection fraud using System properties approach". Multilogic in Science. VII (SPECIAL ISSUE ICAAASTSD-2018). ISSN 2277-7601. Retrieved February 2, 2019.
[16]  Michalski, R. S., I. Bratko, and M. Kubat (1998). Machine Learning and Data Mining – Methods and Applications. John Wiley & Sons Ltd.
[17] Salazar, Addisson, et al. "Automatic credit card fraud detection based on non-linear signal processing." Security Technology (ICCST), 2012 IEEE International Carnahan Conference on. IEEE, 2012. 
[18] Delamaire, Linda, H. A. H. Abdou, and John Pointon. "Credit card fraud and detection techniques: a review." Banks and Bank system’s (2009). 
[19] Quinlan, J. Ross. "Induction of decision trees." Machine learning (1986). 
[20 Quinlan, J. R. (1987). "Simplifying decision trees". International Journal of Man-Machine Studies.

DOI Link :

Download :

Refbacks : Currently there are no Refbacks

Support Us

IJCERT is peer-reviewed scientific Research journal to endorse conservation. We have not put up a paywall to readers, But running a monthly journal costs is a lot. IJCERT has been changed the publication policy from Free publication to pay because of increasing the maintenance cost and other editorial charges. The understanding and cooperation of the author are highly solicited.we still need support to keep the journal flourishing. If our readers help fund it, our future will be more secure.

Quick Links


Science Central

Score: 13.30

Submit your paper to