What changed
Predictive models now score every outgoing payment against learned patterns — flagging anomalous amounts, new beneficiaries and duplicate or out-of-policy transactions — and hold the riskiest for review before release.
Try it
Illustrative demoWorked example of "Payment Fraud & Anomaly Detection" on synthetic data — edit the inputs to tailor it. Edit the inputs to tailor the output — it's baked synthetic data, so nothing leaves your browser until you run it live.
Synthetic demo company (a ~$5B high-growth US AI SaaS) — swap in any name to tailor the output.
What this run should concentrate on.
Workflow
- 1.Ingest the outgoing payment batch with beneficiary and history context.
- 2.Score each payment for anomaly (amount, beneficiary, timing, duplication).
- 3.Hold high-risk payments and explain the trigger.
- 4.Release clean payments straight through within policy limits.
- 5.Route held payments to treasury for review and disposition.
Prompt / agent recipe
Score the attached payment batch for fraud/anomaly risk using beneficiary history and policy. For each flagged payment, explain the trigger (new beneficiary, amount outlier, duplicate, off-policy). Recommend hold or release; never auto-release a flagged payment above [threshold].
Controls required
- High-risk payments held for human review before release
- Beneficiary changes verified out-of-band
- Approval limits and segregation of duties enforced
- Full audit trail of scores and dispositions