Answers
It uses statistical, ML, or deep learning methods to flag transactions, entries, or patterns that deviate from expected behavior—surfacing fraud, errors, and process gaps early.
Small datasets: statistical tests (Z-score, IQR). Medium to large datasets: ML (Isolation Forest, LOF). Time-series or sequential data: LSTM or autoencoders. Combine methods for best coverage.
Tune thresholds with validation data, engineer features that capture context (time-of-day, vendor history), and combine multiple detection methods. Monitor precision and recall consistently.
Reviewers and inspectors need to understand why an item was flagged. Plain-English reasons and linked evidence make AI-assisted triage defensible in file review.
Pilot on one population (e.g., journal entries or vendor payments). Clean data, set conservative thresholds, validate against known exceptions, and measure review time before expanding.
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