The use of proactive communication through knowledge management to create awareness and educate clients on e-banking fraud prevention

Abstract

Purpose: This study sets out to address the lack of studies to educate clients and create awareness on fraud prevention in this sector, specifically on e-banking fraud that falls into two main categories: phishing/vishing/Smishing and malware practices.Design/methodology/approach: An interpretivist approach was used to analyse a real online website that is available to all users. The subject under study was the website of the South African Banking Risk Information Centre (SABRIC). The three concurrent cyclical flow of activity of the data analysis interactive model was utilised in the research. An abductive approach was used to report on the findings based on descriptions and interpretive comments relating it to and drawing on the theoretical thrusts identified. The research was conducted during the two time frames of 2017 and 2018.Findings/results: It was found that SABRIC did use its website to communicate proactively with clients to create awareness and educate them on e-banking fraud prevention measures. A significant increase in proactive communication through various activities was evident when the 2017 and 2018 statistics were compared.Practical implications: The main themes that emerged from the findings include the need for collaborative efforts, member training, intervention by government and law enforcement agencies and the importance of awareness of protective measures.Originality/value: This study contributes to the limited body of knowledge and literature investigating the need for proactive communication through knowledge management typologies in emerging economic contexts to create awareness and educate clients on e-banking fraud prevention to address the intensification of cases in fraud.

Application of Artificial Intelligence for Fraudulent Banking Operations Recognition

“…Phishing [5,6] The attacker steals login credentials or other personal information by tricking the victim into entering them on a fake banking website or through a fake email or text message.…”

“…This is due to the success of SGD in tackling vast and sparse machine-learning challenges commonly encountered in natural language processing and text classification. Due to the sparsity of the data, the classifiers developed using SGD have rapidly expanded to deal with problems with over 10 5 training examples and over 10 5 features [37].…”