Credit Card Fraud Detection Using Unsupervised Machine Learning Algorithms

Hariteja Bodepudi

doi:https://doi.org/10.14445/22312803/ IJCTT-V69I8P101

Research Article | Open Access | Download PDF

Volume 69 | Issue 8 | Year 2021 | Article Id. IJCTT-V69I8P101 | DOI : https://doi.org/10.14445/22312803/IJCTT-V69I8P101

Credit Card Fraud Detection Using Unsupervised Machine Learning Algorithms

Hariteja Bodepudi

Received	Revised	Accepted
01 Jul 2021	02 Aug 2021	13 Aug 2021

Citation :

Hariteja Bodepudi, "Credit Card Fraud Detection Using Unsupervised Machine Learning Algorithms," International Journal of Computer Trends and Technology (IJCTT), vol. 69, no. 8, pp. 1-3, 2021. Crossref, https://doi.org/10.14445/22312803/ IJCTT-V69I8P101

Abstract

In the modern era, the usage of the internet has increased a lot these days and becomes an essential part of the life. As the e-commerce has increased, the buying and selling the products over the internet becomes more easy and flexible. The usage of online shopping, online bill payment has increased a lot these days with the introduction of the modern technology like online banking, credit card payments. Due to the increase of the online payment and online shopping, the risk of credit card usage also increased as the credit card was used in many places as it becomes hard for the bank to distinguish the real transactions of the consumer versus the fraud transactions. Also, the credit card fraud transaction can also happen if the customer accidentally loses the credit card. So, it becomes complex for the banks to stop the fraud transactions at that point. This paper talks about how to detect the fraud transactions and block the payments before processing by using the Machine Learning on a real-time basis. This paper clearly explains about how the anomalies can be detected in an unsupervised approach and to achieve the higher accuracy. This Anomaly detection process used in this paper can be applied in a wide range of applications like fraud detections in banking, underbilling/overbilling for customers in telecommunications, security monitoring, network traffic, health care, and a wide range of manufacturing industries.

Keywords

Anomaly, Machine Learning, Supervised Learning, Unsupervised Learning.

References

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