Examining the nature and prevalence of e-banking fraud: a qualitative analysis of banks in South Africa

Tawona Matrokisi Chindara (), Janet Rozanne Smith () and Collins Achepsah Leke ()
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Tawona Matrokisi Chindara: University of Johannesburg, Johannesburg, South Africa
Janet Rozanne Smith: University of Johannesburg, Johannesburg, South Africa
Collins Achepsah Leke: University of Johannesburg, Johannesburg, South Africa

Access Journal, 2025, vol. 6, issue 2, 303-318

Abstract: Background: The banking sector is vital to every economy as it provides the necessary finance for all economic activities. However, it faces significant threats from cyber criminals. Objectives: The article aims to explore the nature and prevalence of electronic banking fraud and proposes practical solutions to mitigate it in South Africa. To combat electronic banking fraud and improve transparency and accountability in South Africa, it is crucial to understand the nature and prevalence of such fraud. This knowledge is vital for creating effective anti-fraud solutions. Methods/Approach: The study employed qualitative methods, including interviews with fifteen participants from the management of risk departments in five South African banks. It applied thematic analysis with Maxqda 24 software to identify patterns, generate codes, and categorize them. Key themes and concepts were supported by direct quotations. Results: Electronic banking fraud in South Africa is on the rise despite significant investments in security measures by banks. To effectively combat it and protect depositors’ funds and assets, banks need to adopt modern technological solutions, particularly those utilizing machine learning. Conclusions: Fraud poses major risks to the banking sector, requiring comprehensive strategies that incorporate advanced technologies and strong risk management. By proactively using machine learning algorithms, banks can improve fraud detection and prevention, ensuring secure and trustworthy digital transactions. The study reveals the nature and prevalence of electronic banking fraud in South Africa. Additionally, it suggests implementing proactive and robust mitigation strategies, leveraging machine learning algorithms, to effectively combat e-banking fraud and enhance the accountability of South African banks.

Keywords: Accountability; banking sector; machine learning; Algorithms; e-banking fraud; South Africa (search for similar items in EconPapers)
JEL-codes: M41 O3 O31 O32 O33 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:aip:access:v:6:y:2025:i:2:p:303-318

DOI: 10.46656/access.2025.6.2(4)

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