Anomaly Detection Audit Trail

Every anomaly flagged by your AI agent — logged, hashed, and independently verifiable. Thirteen anomaly subtypes including duplicate payments, revenue reversals, and velocity anomalies. Your transaction data stays in your environment.

Methodology for auditors · Verify an attestation

The problem

Finance teams deploy AI agents to monitor thousands of transactions per day for anomalies — duplicate payments, unusual vendor activity, transactions at suspicious times. But when an auditor asks how the organization governs these agents — what they flag, why they flag it, how accurate they are, and who reviews their work — most teams have no structured answer.

Thirteen anomaly subtypes

Sigmodx records the anomaly subtype the agent detected — including duplicate payments (same vendor, similar amount, close dates), revenue reversals after period close, velocity anomalies (unusual transaction frequency), and split transactions designed to avoid approval thresholds — without storing raw transaction data.

Reliability signals

Five signals are computed from human review: false positive rate, false negative rate, detection precision, escalation rate, and severity accuracy. False negative rate carries the most weight — an agent that misses real anomalies moves to LIMIT above 2% and BLOCK above 5%.

Integration

Available for Q3 2026 pilot

Pilot participants help shape what "AI agent audit infrastructure" means for continuous monitoring.

Request pilot access →