Traditional internal audit samples 5-10% of transactions and finds issues months after they occur. Population-level continuous monitoring catches violations within hours and provides full coverage instead of statistical inference.
ML models trained on historical transactions score every new transaction against fraud, policy, and control patterns. LLM agents read transaction context (POs, invoices, expense narratives) and apply policy rules in natural language. High-risk items escalate immediately; low-risk pass through.
ML risk scoring engine + LLM agent layer + policy document RAG + workflow + alert management dashboard.
Continuous, real-time detection and prevention of access conflicts that would let a single person execute incompatible financial actions.
SoD controls define the policy ruleset that the agentic layer enforces and monitors.
Shift from period-end batch close to daily reconciliation and variance analysis, with automated flux coverage approaching 100% of accounts.
Continuous data feeds required for agentic real-time scanning must already exist.
AI-driven close execution with 99%+ auto-matching, auto-posted journal entries, and ML anomaly flagging — humans review exceptions only.
Autonomous close creates the structured transaction corpus that agentic monitors scan.
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