Data analytics and whistleblowing in detection: an academic perspective
Fraud remains a widespread threat with serious financial and reputational consequences, costing organisations an estimated 5% of annual revenue globally. Traditional rule-based detection methods are increasingly inadequate due to evolving fraud schemes and rapid digitalisation across sectors. As a r...
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| Main Authors: | , , |
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| Format: | Monograph |
| Language: | en |
| Published: |
Universiti Teknologi MARA, Negeri Sembilan
2025
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| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/131650/1/131650.pdf https://ir.uitm.edu.my/id/eprint/131650/ |
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| Summary: | Fraud remains a widespread threat with serious financial and reputational consequences, costing organisations an estimated 5% of annual revenue globally. Traditional rule-based detection methods are increasingly inadequate due to evolving fraud schemes and rapid digitalisation across sectors. As a result, data analytics and machine learning have become essential tools for identifying hidden patterns, predicting fraudulent activities, and enabling real-time interventions, with advanced techniques such as network and graph analysis enhancing detection of organised fraud. Effective fraud prevention, however, requires a holistic approach that combines technological solutions with human mechanisms like whistleblowing to strengthen organisational resilience. |
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