Empirical Analysis of Machine Learning Algorithms on Detection of Fraudulent Electronic Fund Transfer Transactions-Reference-Cited by-同舟云学术

Empirical Analysis of Machine Learning Algorithms on Detection of Fraudulent Electronic Fund Transfer Transactions

Published:2022-03-28 Issue: Volume: Page:1-13
ISSN:0377-2063
Container-title:IETE Journal of Research
language:en
Short-container-title:IETE Journal of Research

Author:

Arfeen A. Asad¹^ORCID,Khan B. Muhammad Asim¹

Affiliation:

1. National Center for Cyber Security, Department of Computer and Information Systems, NED University of Engineering and Technology, Karachi, Pakistan

Funder

Higher Education Commission (HEC), Pakistan

NCCS-NED

Publisher

Informa UK Limited

Subject

Electrical and Electronic Engineering,Computer Science Applications,Theoretical Computer Science

Link

https://www.tandfonline.com/doi/pdf/10.1080/03772063.2022.2048700

Reference41 articles.

1. Risk Types and Risk Amplification of Online Finance

2. INTERPOL warns of financial fraud linked to COVID-19, March 2020.

3. K. Rivera, C. Rohn, J. Donker, and C. Butter. Fighting fraud: A never-ending battle. Survey WLT127074948, PwC, 2020.

4. A State of the Art Survey of Fraud Detection Technology

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Hybrid Undersampling and Oversampling for Handling Imbalanced Credit Card Data;IEEE Access;2024

2. A Machine Learning-Based Framework for Detecting Credit Card Anomalies and Fraud;2023 27th International Conference on Information Technology (IT);2023-02-15

3. A Comparative Study Between Various Machine-Learning Algorithms Implemented for the Proper Detection of Fraudulent and Non-fraudulent Transactions Through Credit Card;Lecture Notes in Networks and Systems;2023