Using isolation forest in anomaly detection: The case of credit card transactions

Soumaya Ounacer, Hicham Ait El Bour, Younes Oubrahim, Mohamed Yassine Ghoumari, Mohamed Azzouazi

Abstract


With the evolution of new technology especially in the domain of e-commerce and online banking, the payment by credit card has seen a significant increase. The credit card has become the most used tool for online shopping. This high rate in use brings about fraud and a considerable damage. It is very important to stop fraudulent transactions because they cause huge financial losses over time. The detection of fraudulent transactions is an important application in anomaly detection. There are different approaches to detecting anomalies namely SVM, logistic regression, decision tree and so on. However, they remain limited since they are supervised algorithms that require to be trained by labels in order to know whether the transactions are fraudulent or not. The goal of this paper is to have a credit card fraud detection system which is able to detect the highest number of new transactions in real time with high accuracy. We will also compare, in this paper, different unsupervised techniques for credit card fraud detection namely LOF, one class SVM, K-means and Isolation Forest so as to single out the best approach.

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References


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DOI: http://dx.doi.org/10.21533/pen.v6i2.533

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Copyright (c) 2019 soumaya ounacer, Hicham Ait El Bour, Younes Oubrahim, Mohamed Yassine Ghoumari, Mohamed Azzouazi

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN: 2303-4521

Digital Object Identifier DOI: 10.21533/pen

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License