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Data Analytics in Audit
The use of statistical, machine learning, and anomaly detection techniques to discover patterns, deviations, and risks within large datasets. Auditors apply data analytics for fraud detection, transaction validation, and risk segmentation. Techniques include anomaly detection (Isolation Forest, Local Outlier Factor), link analysis, and network analysis.
Source: IIA GIAS 2024, MDPI research