Detect Dark Web Marketplace Activity
“Which addresses will transact with known dark web marketplace wallets in the next 30 days?”
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A real-world example
Which addresses will transact with known dark web marketplace wallets in the next 30 days?
Abacus exit scam (2025). Dark web markets use crypto as their primary payment rail. By the time an address transacts with a marketplace, the evidence trail is established. Predicting marketplace connections before transactions happen supports FinCEN SAR filings with predictive intelligence.
How KumoRFM solves this
Graph-powered fraud intelligence
Kumo learns transactional patterns that precede dark web marketplace interactions. Addresses that will interact with marketplaces show specific behavioral signatures: fragmented transfers, privacy coin swaps, and connections to previously flagged intermediaries. The graph reveals 2-hop connections to labeled marketplace addresses.
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 | 2024-06-15 | unknown | BTC |
| ADDR002 | 2024-09-20 | exchange | BTC |
On-Chain Transfers
| txn_hash | from_address | to_address | amount | timestamp |
|---|---|---|---|---|
| 0xe5... | ADDR001 | ADDR200 | 0.45 | 2025-01-10 |
| 0xf6... | ADDR002 | ADDR201 | 1.20 | 2025-01-14 |
Labels
| address_id | tag | source | confidence |
|---|---|---|---|
| ADDR200 | marketplace | Elliptic | 0.95 |
| ADDR201 | marketplace | Chainalysis | 0.91 |
Write your PQL query
Describe what to predict in 2-3 lines — Kumo handles the rest
PREDICT LIST_DISTINCT(ON_CHAIN_TRANSFERS.TO_ADDRESS WHERE LABELS.TAG = "marketplace", 0, 30, days) FOR EACH ADDRESSES.ADDRESS_ID
Prediction output
Every entity gets a score, updated continuously
| ADDRESS_ID | CLASS | SCORE | TIMESTAMP |
|---|---|---|---|
| ADDR001 | ADDR200 | 0.86 | 2025-02-01 |
| ADDR002 | ADDR201 | 0.71 | 2025-02-01 |
Understand why
Every prediction includes feature attributions — no black boxes
Address ADDR001
Predicted: 86% probability of transacting with ADDR200 (marketplace)
Top contributing features
Transfer amount to ADDR200
0.45 BTC
35% attribution
Label tag (source)
marketplace (Elliptic)
28% attribution
Label confidence
0.95
17% attribution
Address entity_type
unknown
13% attribution
Address first_seen recency
7 months
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 1 hop upstream from dark web marketplaces. Support FinCEN SAR filings with predictive intelligence.
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.
