CREDIT CARD FRAUD DETECTION USING RANDOM FOREST AND CART ALGORITHM (2023)
Mrs. M.SYAMALA SAISREE, K.SRICHANDANA, K.AISHWARYA, K. HARIKA JCR. 2023: 30-35
Billions of dollars of loss are caused every year by fraudulent credit card transactions. The design of efficient fraud detection algorithms is key for reducing these losses, and more and more algorithms rely on advanced machine learning techniques to assist fraud investigators. The design of fraud detection algorithms is however particularly challenging due to the non-stationary distribution of the data, the highly unbalanced class distributions, and the availability of few transactions labeled by fraud investigators. At the same time, public data are scarcely available for confidentiality issues, leaving unanswered many questions about what the best strategy is. In this thesis, we aim to provide some answers by focusing on crucial issues such as) why and how under-sampling is useful in the presence of class imbalance (i.e. frauds are a small percentage of the transactions), ii) how to deal with unbalanced and evolving data streams (non-stationarity due to fraud evolution and change of spending behavior), iii) how to assess performances in a way which is relevant for detection and iv) how to use feedbacks provided by investigators on the fraud alerts generated. Finally, we design and assess a prototype of a Fraud Detection System able to meet real-world working conditions and that can integrate investigators’ feedback to generate accurate alerts.
This is an open access journal which means that all content is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles in this journal without asking prior permission from the publisher or the author. This is in accordance with the Budapest Open Access Initiative (BOAI) definition of open access.
The articles in Journal of Critical Reviews are open access articles licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc-sa/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
Copyright � 2021 Journal of Critical Reviews All Rights Reserved. Subject to change without notice from or liability to Journal of Critical Reviews.
For best results, please use Internet Explorer or Google Chrome
Journal of Critical Review, Tower 23/4,
Kuala Lumpur, malaysia