EVALUATING THE PERCEIVED RISKS OF AI IN SOUTH AFRICAN FINANCIAL INSTITUTIONS: A MULTIDIMENSIONAL APPROACH

Risk management procedures in financial institutions around the world have been significantly altered by artificial intelligence (AI). However, little is known about the perceived risks of implementing AI, especially in developing nations such as South Africa. The aim of this study is to assess, from a multidimensional perspective, the perceived risks of AI adoption by employees in South African financial institutions. This study employs a mixed-methods approach, using a purposive and snowball sample of 90 survey respondents and semi-structured interviewees from several South African financial institutions. The study revealed a broad spectrum of concerns ranging from AI-induced unemployment to cybercrime vulnerabilities. The analysis provides layered insights into how different departments, including Risk Management, IT, and Operations Management, uniquely perceive and manage AI-related challenges. This study underscores the need for personalised risk management strategies that meet unique departmental concerns, as well as the importance of strategic planning in the integration of AI technology by financial institutions to maximise potential while limiting associated risks. It adds to the growing body of knowledge on AI adoption in emerging markets by providing practical information to practitioners and policymakers.

Authors

Institutions

Publication Details

Journal
ACTA ECONOMICA
Published
2025-12-22
DOI
https://doi.org/10.63356/ace.2025.010
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

EVALUATING THE PERCEIVED RISKS OF AI IN SOUTH AFRICAN FINANCIAL INSTITUTIONS: A MULTIDIMENSIONAL APPROACH

Mariette Geyser, Merylin Mabingwe-Mashashave
ACTA ECONOMICA
Ethics and Social Impacts of AI
article

EVALUATING THE PERCEIVED RISKS OF AI IN SOUTH AFRICAN FINANCIAL INSTITUTIONS: A MULTIDIMENSIONAL APPROACH

Mariette Geyser, Merylin Mabingwe-Mashashave
article en

Abstract

Risk management procedures in financial institutions around the world have been significantly altered by artificial intelligence (AI). However, little is known about the perceived risks of implementing AI, especially in developing nations such as South Africa. The aim of this study is to assess, from a multidimensional perspective, the perceived risks of AI adoption by employees in South African financial institutions. This study employs a mixed-methods approach, using a purposive and snowball sample of 90 survey respondents and semi-structured interviewees from several South African financial institutions. The study revealed a broad spectrum of concerns ranging from AI-induced unemployment to cybercrime vulnerabilities. The analysis provides layered insights into how different departments, including Risk Management, IT, and Operations Management, uniquely perceive and manage AI-related challenges. This study underscores the need for personalised risk management strategies that meet unique departmental concerns, as well as the importance of strategic planning in the integration of AI technology by financial institutions to maximise potential while limiting associated risks. It adds to the growing body of knowledge on AI adoption in emerging markets by providing practical information to practitioners and policymakers.

ACTA ECONOMICAVol. 23(43)
Institute of Economic Research of the Slovak Academy of Sciences (SK)
Openalex Percentile: Top 29%
Ethics and Social Impacts of AI
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.