TITLE

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

TYPE

Journal Article

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.

Citation

Indrajani, Prabowo I. H and Meyliana D. (2016). Learning Fraud Detection from Big Data in Online Banking Transactions: A Systematic Literature Review (SCOPUS). Journal of Telecommunication, Electronic and Computer Engineering, 8 (3), 127-131.

Keywords

Fraud; Fraud detection system; Big data; Online banking transaction; Algorithm

Published On

Journal of Telecommunication, Electronic and Computer Engineering

Author

Indrajani

Lektor Kepala

  • Ir. Harjanto Prabowo
  • Dr. Meyliana

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