AML Detection
“Which accounts show money laundering patterns?”
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A real-world example
Which accounts show money laundering patterns?
Banks spend $30B+ annually on AML compliance (LexisNexis Risk Solutions). Legacy rule-based transaction monitoring generates 95-98% false-positive rates on Suspicious Activity Report (SAR) alerts, burying investigators in noise. Meanwhile, sophisticated laundering networks exploit the gap between siloed monitoring systems, structuring deposits just below reporting thresholds, layering funds through shell-company networks, and using trade-based schemes that span multiple institutions. A top-10 bank processes 500K+ alerts annually with only 2-5% resulting in actual SARs.
How KumoRFM solves this
Relational intelligence built for banking and financial data
Kumo maps the full relational graph of accounts, transactions, counterparties, beneficial owners, and corporate structures. The model identifies patterns invisible to threshold-based rules: Account A-7012 receives structured deposits from 12 unrelated individuals, each below $10K, then wires funds through three intermediary accounts to an offshore entity whose beneficial owner shares an address with the original depositors. These multi-hop laundering patterns emerge naturally from the graph structure without hand-coded rules.
From data to predictions
See the full pipeline in action
Connect your tables, write a PQL query, and get predictions with built-in explainability — all in minutes, not months.
Your data
The relational tables Kumo learns from
ACCOUNTS
| account_id | owner_name | type | opened_date | jurisdiction |
|---|---|---|---|---|
| A-7012 | Apex Trading LLC | Business Checking | 2024-08-15 | US |
| A-7013 | Global Imports Inc | Business Checking | 2024-09-02 | US |
| A-7050 | Margaret Wilson | Personal Savings | 2019-03-10 | US |
TRANSACTIONS
| txn_id | from_account | to_account | amount | type | timestamp |
|---|---|---|---|---|---|
| T-001 | EXT-4421 | A-7012 | $9,800 | Cash Deposit | 2025-09-01 |
| T-002 | EXT-4422 | A-7012 | $9,700 | Cash Deposit | 2025-09-01 |
| T-003 | A-7012 | A-7013 | $48,500 | Wire | 2025-09-03 |
COUNTERPARTIES
| entity_id | name | type | risk_country | shared_address |
|---|---|---|---|---|
| EXT-4421 | John Doe | Individual | US | 142 Oak St |
| EXT-4422 | Jane Smith | Individual | US | 142 Oak St |
| EXT-9901 | Cayman Holdings | Corporation | KY | N/A |
CORPORATE_STRUCTURE
| entity_id | parent_entity | beneficial_owner | jurisdiction |
|---|---|---|---|
| A-7012 | Shell Corp A | Unknown | US |
| A-7013 | Shell Corp B | Unknown | US |
| EXT-9901 | Shell Corp A | Unknown | KY |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(ACCOUNTS.SAR_FILED = 'True', 0, 30, days) FOR EACH ACCOUNTS.ACCOUNT_ID WHERE ACCOUNTS.TYPE = 'Business Checking'
Prediction output
Every entity gets a score, updated continuously
| ACCOUNT_ID | OWNER | AML_RISK_SCORE | ALERT_PRIORITY | PATTERN_TYPE |
|---|---|---|---|---|
| A-7012 | Apex Trading LLC | 0.92 | Critical | Structuring + Layering |
| A-7013 | Global Imports Inc | 0.78 | High | Layering |
| A-7050 | Margaret Wilson | 0.04 | Low | None |
Understand why
Every prediction includes feature attributions — no black boxes
Account A-7012 (Apex Trading LLC)
Predicted: 92% AML risk score
Top contributing features
Structured deposits below $10K threshold
12 in 7 days
30% attribution
Counterparties sharing same address
4 of 12
25% attribution
Rapid layering to intermediary accounts
3 hops
20% attribution
Corporate structure opacity
Unknown UBO
15% attribution
Account age vs. transaction volume mismatch
13mo old, $2.1M flow
10% attribution
Feature attributions are computed automatically for every prediction. No separate tooling required. Learn more about Kumo explainability
PQL Documentation
Learn the Predictive Query Language — SQL-like syntax for defining any prediction task in 2–3 lines.
Python SDK
Integrate Kumo predictions into your pipelines. Train, evaluate, and deploy models programmatically.
Explainability Docs
Understand feature attributions, model evaluation metrics, and how to build trust with stakeholders.
Bottom line: Reduce AML false positives by 60% and surface 35% more true suspicious activity, saving $50-80M in annual compliance costs while strengthening regulatory standing.
Related use cases
Explore more financial services use cases
Topics covered
One Platform. One Model. Predict Instantly.
KumoRFM
Relational Foundation Model
Turn structured relational data into predictions in seconds. KumoRFM delivers zero-shot predictions that rival months of traditional data science. No training, feature engineering, or infrastructure required. Just connect your data and start predicting.
For critical use cases, fine-tune KumoRFM on your data using the Kumo platform and Data Science Agent for 30%+ higher accuracy than traditional models.
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
