Learning Fraud Detection from Big Data in Online Banking Transactions: A Systematic Literature Review

Authors

  • . Indrajani School of Information System, Bina Nusantara University, KH. Syahdan, 9, 11480 Jakarta, Indonesia
  • Harjanto Prabowo Bina Nusantara University, KH. Syahdan, 9, 11480 Jakarta, Indonesia
  • . Meyliana School of Information System, Bina Nusantara University, KH. Syahdan, 9, 11480 Jakarta, Indonesia

Abstract

The implementation of fraud detection in online banking transactions on big data is one of the most important strategies applied by banks to protect their transactions and highly related to algorithms. In fact, it is not easy to successfully implement this strategy because it requires a huge investment and is influenced by complexity algorithms, training, and testing. The frauds bring fatal impact, such as destruction of the banking reputation, banking loss, and state financial loss. One target of the fraud perpetrators in banking is online banking transactions. Security has become a major issue in the online banking transaction. Furthermore, the research of fraud is switching to big data and turns out that online banking data are stored in the database operational and big data. This study aims to find out what kind of algorithms fraud detection for online banking transactions using a systematic literature review to the 25 relevant papers.

Downloads

Published

2016-06-01

How to Cite

Indrajani, ., Prabowo, H., & Meyliana, . (2016). Learning Fraud Detection from Big Data in Online Banking Transactions: A Systematic Literature Review. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 8(3), 127–131. Retrieved from https://jtec.utem.edu.my/jtec/article/view/1017

Issue

Vol. 8 No. 3: Emerging Technologies in Communication and Computer Engineering for Smart City Applications I

Section

Articles

License

TRANSFER OF COPYRIGHT AGREEMENT

The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):

  1. All proprietary right other than copyright, such as patent rights
  2. The right to make further copies of all or part of the published article for my use in classroom teaching
  3. The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
  4. The right to make copies of the published work for internal distribution within the institution that employs me

I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.


Most read articles by the same author(s)