Harnessing Machine Learning and Artificial Intelligence for Ear- ly Fraud Detection Among Banks in Harare, Zimbabwe: Internal Auditors’ Perspective

https://doi.org/10.34047/MMR.2024.111

Abstract

The study explores the transformative power of utilizing machine learning and artificial intelligence for early fraud detection among banks in Harare, Zimbabwe using qualitative research. Data were collected through document reviews and in-depth interviews with bank internal auditors and senior management. The study addressed three key research questions, namely, examining internal auditors’ understanding of machine learning and artificial intelligence tools/systems for fraud detection; understanding internal auditors’ perceptions on the effectiveness of machine learning and artificial intelligence-based fraud detection systems; and identifying major challenges faced by internal auditors during implementation of artificial intelligence fraud detection systems. Internal auditors’ perceptions were gathered through in-depth interviews which were conducted face to face and online. Findings from the study demonstrated strong consensus among internal auditors on the potential power of machine learning and artificial intelligence in detecting fraud at an early stage. In addition, the study revealed the potential benefits of utilizing machine learning algorithms and artificial intelligence which includes enhanced speed in identifying anomalies, improved accuracy, and the ability to detect fraud early, thereby enabling management to come up with internal control mechanisms which can prevent fraud. Successful implementation of machine learning and artificial intelligence-powered fraud detection systems require adequate training and support from the organization’s leadership, and ethical considerations.