Transaction monitoring is the beating heart of an AML program — and the hardest data to hand to a vendor. Payment records reveal customer behavior, counterparties and corridors. Exporting them in bulk to a monitoring cloud is exactly the kind of data movement compliance teams are trying to avoid.
Near-data monitoring removes the dilemma.
Bring the rules to the transactions
Instead of streaming transactions out, KeplerAI pushes the scoring logic in. Typology scenarios and models execute inside your data plane, right next to the payment tables. Alerts — not raw transactions — flow back to the monitoring dashboards.
Typologies as scenarios
Common money-laundering patterns become reusable scenarios:
- Structuring and smurfing — many sub-threshold deposits across accounts.
- Rapid movement — funds in and straight out, minimizing dwell time.
- High-risk geographies — flows touching flagged jurisdictions.
- Correspondent / nested relationships — unusual activity behind a single correspondent account.
Each scenario is tuned to your data by the data-science agent and validated against a rubric your team accepts.
From alert to case
Alerts land in monitoring with status, assignment, comments and an audit trail — the raw material for investigation and, where needed, a suspicious-activity report. Because everything is traceable, the path from detection to filing is defensible.
Why near-data wins
- No bulk export, so no new data-residency exposure.
- Lower latency — scoring runs where the data already is.
- Models retrain on fresh transactions without ever copying them off-site.
Monitoring is supposed to reduce risk, not create a new one. Running it near the data keeps it that way. Explore the AML solution.