Transaction monitoring alerts are auto-enriched with customer history, prior dispositions, and open-source checks the moment they fire, then AI-ranked by risk — so analysts open a complete case file, not a raw alert, and clear the queue three times faster.
Typical results
3x
Analyst throughput
100%
Alerts pre-enriched
0
SLA breaches once live
Figures reflect typical results for this kind of automation.
The challenge
Every alert from the monitoring system arrived as a bare transaction record. Analysts spent the first half hour of each review pulling customer history, checking prior dispositions, and running the same open-source searches — before any actual judgment began. The backlog grew past nine hundred alerts, SLA breaches loomed, and genuinely risky cases waited behind trivial ones.
Before and after
Minutes per alert review
−70%Open alert backlog
−88%Alerts cleared per analyst daily
3×The automation
We built an enrichment pipeline that catches each alert as it fires and assembles the case file automatically: full customer profile and transaction history, every prior alert with its disposition, and sanctions and adverse-media checks from open sources. An AI model then scores each enriched alert and ranks the queue by risk, with its reasoning attached. Analysts start every review with the complete picture, and each enrichment step is logged for the audit trail.
How it works
- 01
Alert fires
A transaction monitoring alert is caught by webhook the instant the system raises it.
- 02
Case assembled
Customer profile, transaction history, and every prior alert with its disposition are pulled automatically.
- 03
Open-source checks
Sanctions lists and adverse media are queried, with results attached to the file.
- 04
AI risk ranking
A model scores the enriched alert and orders the queue by risk, reasoning included.
- 05
Analyst decides
The reviewer opens a complete case file and logs a disposition — every step recorded for audit.
Starts from
- Monitoring alerts
- Customer history
Delivers
- Complete case file
- Risk-ranked queue
- Audit trail
The impact
Review time typically falls 70%, from forty minutes to twelve, and each analyst clears three times as many alerts per day. A backlog of nine hundred alerts can shrink to about a hundred, high-risk cases surface first instead of waiting their turn, and the audit trail is ready whenever an examiner asks.
Questions about this automation
What information is added to each alert before an analyst sees it?
The flow pulls the customer profile, recent transaction history and every prior alert with its disposition. It also runs sanctions list and adverse media checks and attaches the results. The analyst opens one complete case file with all of this in place, instead of starting from a bare transaction record.
Does the AI decide whether an alert is closed?
No. The AI model only scores each enriched alert and orders the queue by risk, with its reasoning written next to the score. A human analyst reviews the case and records the disposition, so every decision on an alert stays with a person and the model's input remains visible.
How does it support audits and regulatory exams?
Every enrichment step is logged with a timestamp and its source: which records were pulled, which lists were checked and what risk score was given. Together with the analyst's disposition, this gives a complete trail for each alert that can be exported when an examiner or internal auditor asks for it.