Flag Stablecoin Sanctions Evasion
“Which addresses will swap into sanctioned or flagged stablecoins in the next 14 days?”
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A real-world example
Which addresses will swap into sanctioned or flagged stablecoins in the next 14 days?
A7A5 ruble-backed stablecoin processes $1B/day, linked to Russian sanctions evasion. Sanctioned entities use exotic stablecoins to move value outside the dollar system. Detecting before the swap executes demonstrates compliance maturity to regulators.
How KumoRFM solves this
Graph-powered fraud intelligence
Kumo learns the behavioral precursors to evasion swaps. Addresses that will swap into sanctioned stablecoins show patterns: sudden shifts in token holding composition, interaction with known facilitator addresses, and geographic correlation with sanctioned jurisdictions. The graph connects wallet behavior across DEX interactions.
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
Addresses
| address_id | first_seen | entity_type | chain |
|---|---|---|---|
| ADDR001 | 2024-04-12 | unknown | ETH |
| ADDR002 | 2024-10-05 | business | TRON |
Swap Events
| swap_id | address_id | from_token | to_token | amount | timestamp |
|---|---|---|---|---|---|
| SW01 | ADDR001 | USDT | A7A5 | 50000 | 2025-01-10 |
| SW02 | ADDR002 | USDC | RUBcoin | 120000 | 2025-01-14 |
Flagged Tokens
| token_id | token_name | flag_reason | flag_source |
|---|---|---|---|
| FT01 | A7A5 | sanctions_evasion | OFAC |
| FT02 | RUBcoin | sanctions_evasion | FinCEN |
Write your PQL query
Describe what to predict in 2-3 lines — Kumo handles the rest
PREDICT LIST_DISTINCT(SWAP_EVENTS.TO_TOKEN WHERE FLAGGED_TOKENS.FLAG_REASON = "sanctions_evasion", 0, 14, days) FOR EACH ADDRESSES.ADDRESS_ID
Prediction output
Every entity gets a score, updated continuously
| ADDRESS_ID | CLASS | SCORE | TIMESTAMP |
|---|---|---|---|
| ADDR001 | A7A5 | 0.89 | 2025-02-01 |
| ADDR002 | RUBcoin | 0.76 | 2025-02-01 |
Understand why
Every prediction includes feature attributions — no black boxes
Address ADDR001
Predicted: 89% probability of swapping into A7A5
Top contributing features
Swap from_token
USDT
36% attribution
Swap amount
$50,000
27% attribution
Flagged token flag_reason
sanctions_evasion
19% attribution
Flag source
OFAC
11% attribution
Address chain
ETH
7% 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: Flag addresses swapping into sanctioned stablecoins before the swap executes. Proactive detection demonstrates compliance maturity to regulators.
Related scenarios
Explore more fraud predictions
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.
