Anomaly Detection in Credit Card Transactions using Machine Learning

Meenu; Swati Gupta; Sanjay Patel; Surender Kumar; Goldi Chauhan

doi:10.21276/ijircst.2020.8.3.5

Abstract

Anomaly Detection is a method of identifying the suspicious occurrence of events and data items that could create problems for the concerned authorities. Data anomalies are usually associated with issues such as security issues, server crashes, bank fraud, building structural flaws, clinical defects, and many more. Credit card fraud has now become a massive and significant problem in today's climate of digital money. These transactions carried out with such elegance as to be similar to the legitimate one. So, this research paper aims to develop an automatic, highly efficient classifier for fraud detection that can identify fraudulent transactions on credit cards. Researchers have suggested many fraud detection methods and models, the use of different algorithms to identify fraud patterns. In this study, we review the Isolation forest, which is a machine learning technique to train the system with the help of H2O.ai. The Isolation Forest was not so much used and explored in the area of anomaly detection. The overall performance of the version evaluated primarily based on widely-accepted metrics: precision and recall. The test data used in our research come from Kaggle.

Keywords

Anomaly Detection, Isolation Forest, Credit Card Fraud Detection, Classification using Machine Learning.

References

R. M. Jamail Esmaily, “Intrusion detection system based on multilayer perceptron neural networks and decision tree,” in International Conference on Information and Knowledge Technology, 2015.
Jain, Y. & Tiwari, N. & Dubey, S. & Jain, Sarika. (2019). “A comparative analysis of various credit card fraud detection techniques” in International Journal of Recent Technology and Engineering. 7. 402-407.
S. J. K. T. J. C. W. Siddhartha Bhattacharya, “Data Mining for credit card fraud: A comparative study,” Elsevier, vol. 50, no. 3, pp. 602613, 2011.
Y. Sahin and E. Duman, “Detecting Credit Card Fraud by Decision Trees and Support Vector Machines,” Int. Multiconference Eng. Comput. Sci., vol. I, pp. 442–447, 2011.
S P Maniraj , Aditya Saini , Shadab Ahmed , Swarna Deep Sarkar, 2019, Credit Card Fraud Detection using Machine Learning and Data Science, International Journal of Engineering Research & Technology (IJERT) Volume 08, Issue 09 (September 2019),
N. Malini and M. Pushpa, "Analysis on credit card fraud identification techniques based on KNN and outlier detection," 2017 Third International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB), Chennai, 2017, pp. 255-258.
Ishu Trivedi, Monika, Mrigya, Mridushi, “Credit Card Fraud Detection”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 5, Issue 1, January 2016.
David J.Wetson,David J.Hand,M Adams,Whitrow and Piotr Jusczak “Plastic Card Fraud Detection using Peer Group Analysis” Springer, Issue 2008.
Ekrem Duman, M. Hamdi Ozcelik “Detecting credit card fraud by genetic algorithm and scatter search”. Elsevier, Expert Systems with Applications, (2011). 38; (13057–13063).
S. Benson Edwin Raj, A. Annie Portia “Analysis on Credit Card Fraud Detection Methods”, IEEE-International Conference on Computer, Communication and Electrical Technology, (2011), pg.152-156.
Khyati Chaudhary, Yadav, Mallick, “Review of fraud detection techniques: credit card”, International Journal of Computer Applications (0975- 8887), Volume 45-No 1, May 2012.
Quah, J. T. S., and Sriganesh, M. (2008). Real-time credit card fraud detection using computational intelligence. Expert Systems with Applications, 35(4), 1721-1732.
Wen-Fang YU and Na Wang, “Research on Credit Card Fraud Detection Model Based on Distance Sum”, International Joint Conference on Artificial Intelligence 2009.

Cites this article as

Meenu, S. G. , S. Patel, S. Kumar, G. Chauhan, "Anomaly Detection in Credit Card Transactions using Machine Learning", International Journal of Innovative Research in Engineering & Management (IJIREM), Vol-8, Issue-3, Page No-67-71, 2020. Available from: https://doi.org/10.21276/ijircst.2020.8.3.5

Corresponding Author

Meenu

Assistant Professor, Department of Computer Science, Amity University, Gurugram, Haryana, India (email:mvijarania@ggn.amity.edu)

Download Full Paper

Download PDF

No. of Downloads: 1046 | No. of Views: 135529

Multiauditing Based Cloud Storage Using Dynamic Hash Table

Veena Mudhol R, Chandrakala B M.

May 2020 - Vol 8, Issue 3
Chronic Kidney Disease Detection Using Micro-service Architecture

Champa M S, Prof. Rekha B S.

May 2019 - Vol 7, Issue 3
A System for Recommendation of Medication Using Gaussian NaÃ¯ve Bayes Classifier

Amitha, Prof. Suresh Kumar M, Merin Meleet.

May 2019 - Vol 7, Issue 3

Anomaly Detection in Credit Card Transactions using Machine Learning

Citations

Download Full Paper PDF

Total View 135529

Total Download 1046