In today's business world fraud continues to be a terrible scourge depriving organisations of valuable assets despite the numerous detection and prevention techniques researchers have derived over the years. Fraudsters are constantly discovering new and innovative ways to commit occupational fraud. Therefore, the aim of this research is to study occupational fraud detection mechanisms from a different perspective. Since fraudsters are finding innovative ways, it is high time fraud detection mechanisms are combined with new and emerging technologies. For this research, the variables studied are data analytics, artificial intelligence and machine learning, and fraud risk assessments as fraud detection mechanisms. A mixed method strategy was used whereby quantitative data and qualitative data were collected from external auditors in Mauritius. The objective of this study was to determine whether the fraud detection mechanisms stated increase the accuracy and effectiveness of occupational fraud detection and whether significant impacts are made. The analysis found that data analytics, AI and ML, and fraud risk assessments have significant positive impacts on occupational fraud detection. © 2023 IEEE.