risk

Catch the pattern before the quarter ends.

Fraud hides in the seams between systems — refunds in payments, complaints in support, velocity in the logs. Your agent scans across all of them and returns the outliers worth a human look.

Why it works here

Fraud & anomaly detection, the Contextually way.

Signals across the seams

Payments, support, and event logs analyzed together — which is exactly where coordinated abuse patterns show up and single-system rules miss.

Models fit to the question

Isolation forests, velocity checks, graph clustering — the agent picks and writes the technique per ask, instead of tuning one static rules engine.

A queue, not a firehose

What returns is a ranked, explained shortlist sized for your review team — not ten thousand alerts.

The flow of data
snowflake.refunds 312,940 rows s3.support-2026 41,882 tickets contextually runtime align + isolation_forest read-only · audited 14 flagged merchants 1,310 tokens → context
The program, case by case
goal → anomalous refund patterns, payments × support, this quarter
refunds = sql("snowflake", "SELECT * FROM refunds WHERE qtr = current_qtr") tickets = scan("s3://support-2026", match="refund", fields=["order_id","reason"]) merged = align(refunds, tickets, key="order_id") flags = isolation_forest(merged, contamination=0.01) return report(flags, group_by="merchant", top=10)
scanned 312,940 refunds · 41,882 tickets returned 14 flagged merchants · 1,310 tokens → context
Get started

See it on your data.

Book a demo and we'll run fraud & anomaly detection against systems like yours — your stores, your policies, live — and map the path to a deployment in your VPC.