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

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.