Answers
AI-powered anomaly detection uses machine learning to find transactions or patterns that deviate from expected behavior—surfacing unusual journal entries, rare vendor activity, or odd timing so auditors focus on higher-risk areas.
It looks at transaction structure and behavior—how entries are made, which accounts are used together, who posts them, and when—revealing control gaps that may not show in balance-sheet totals.
It examines how transactions are built—number of debits and credits, which accounts, and in what order. Inconsistent structures across regions or periods can point to policy deviations or manipulation.
Risk scoring assigns a score based on amount, frequency, timing, and user behavior. High scores indicate transactions deserving closer review—reducing time on low-risk items.
Full-population analysis removes sampling blind spots, supports continuous auditing, and helps surface issues that might fall between sample items—delivering better coverage and earlier detection.
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