Predict Rug Pull Risk
“Which token contracts will experience sudden liquidity removal exceeding $50K in the next 7 days?”
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A real-world example
Which token contracts will experience sudden liquidity removal exceeding $50K in the next 7 days?
Token creators drain liquidity pools, leaving holders with worthless tokens. Anonymous deployers, concentrated single-provider liquidity, and no lock periods form a recognizable pattern before the pull happens. Platforms that proactively flag rug pulls build trust and protect retail investors.
How KumoRFM solves this
Graph-powered fraud intelligence
Kumo analyzes deployer behavior, liquidity concentration, lock periods, and holder distribution. TC001 (MoonDoge) has deployer == sole liquidity provider, no lock period, deployer recently interacting with known rug addresses — 94% rug pull probability. The relational graph connects deployer wallet history across multiple token deployments.
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
Token Contracts
| contract_id | token_name | deployer_address | deploy_date | chain |
|---|---|---|---|---|
| TC001 | MoonDoge | 0xa1... | 2025-01-05 | ETH |
| TC002 | SafeYield | 0xb2... | 2024-11-10 | BSC |
Liquidity Events
| event_id | contract_id | provider_address | amount | event_type | timestamp |
|---|---|---|---|---|---|
| LE01 | TC001 | 0xa1... | 80,000 | add | 2025-01-05 |
| LE02 | TC002 | 0xc3... | 25,000 | add | 2024-11-10 |
Write your PQL query
Describe what to predict in 2-3 lines — Kumo handles the rest
PREDICT SUM(LIQUIDITY_EVENTS.AMOUNT WHERE LIQUIDITY_EVENTS.EVENT_TYPE = 'remove', 0, 7, days) > 50000 FOR EACH TOKEN_CONTRACTS.CONTRACT_ID
Prediction output
Every entity gets a score, updated continuously
| CONTRACT_ID | TIMESTAMP | TARGET_PRED | True_PROB |
|---|---|---|---|
| TC001 | 2025-02-01 | True | 0.94 |
| TC002 | 2025-02-01 | False | 0.08 |
| TC003 | 2025-02-01 | True | 0.72 |
Understand why
Every prediction includes feature attributions — no black boxes
Contract TC001 (MoonDoge)
Predicted: 94% rug pull probability
Top contributing features
Deployer == sole liquidity provider
true (0xa1...)
43% attribution
Liquidity add amount
$80,000
24% attribution
Token deploy_date recency
26 days
16% attribution
Chain
ETH
10% attribution
Distinct liquidity providers
1
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: Delist or warn users about tokens with >70% rug pull probability. Protect retail investors. Platforms that proactively flag rug pulls build trust.
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.
