Answers
**Point anomalies** are single transactions that stand out. **Contextual anomalies** look normal in isolation but are odd in context. **Collective anomalies** are patterns across several items. Knowing the type helps you choose the right detection method and follow-up.
Auditors can use **statistical checks** (Z-scores, IQR, duplicate rules), **machine learning** (Isolation Forest, LOF, One-Class SVM), or **sequence/deep learning** (autoencoders, LSTM-style models). Pick the simplest method that reliably addresses the risk.
Define the objective; trace data lineage and reconcile to totals; evaluate reliability of inputs; design the method and thresholds; run and review exceptions; conclude on sufficiency; and document the evidence packet so another auditor can reperform the work.
It pays off in **payables and vendors**, **journal entries**, **revenue and receivables**, and **access and configuration**. Align the method with the risk in each area.
Unreliable inputs, method mismatch, alert fatigue, and thin documentation. Avoid them by validating data, matching method to risk, tuning thresholds, and tying results to the assertion and materiality.
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