Predict Exchange Wallet Compromise
“Which exchange wallets will experience abnormally large outflows following unusual approval patterns?”
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A real-world example
Which exchange wallets will experience abnormally large outflows following unusual approval patterns?
Bybit heist ($1.5B, Feb 2025) — a spoofed signing interface tricked employees into approving malicious transactions. The pattern: unusual IPs or devices in approval events, followed by massive outflows. A 6-hour verification delay costs nothing; a missed compromise costs $100M+.
How KumoRFM solves this
Graph-powered fraud intelligence
Kumo filters by recent approval anomalies (IP changes, unusual signers), then predicts massive outflows. It correlates approval metadata — IP changes, signer identity, timing gaps — with historical compromise patterns across the exchange network. Wallet W002 shows a new-IP approval event 3 days ago, leading to a predicted $10K+ outflow with 91% probability.
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
Wallets
| wallet_id | exchange | wallet_type | balance | last_audit |
|---|---|---|---|---|
| W001 | ExchA | hot | 45M | 2025-01-01 |
| W002 | ExchB | cold | 200M | 2025-01-05 |
Approval Events
| approval_id | wallet_id | signer_id | ip_changed | timestamp |
|---|---|---|---|---|
| AP01 | W001 | S01 | 0 | 2025-01-10 |
| AP02 | W002 | S03 | 1 | 2025-01-14 |
On-Chain Transfers
| txn_hash | from_wallet | to_address | amount | timestamp |
|---|---|---|---|---|
| 0xa1... | W001 | 0x7f... | 120 | 2025-01-10 |
| 0xb2... | W002 | 0xd4... | 48,000 | 2025-01-15 |
Write your PQL query
Describe what to predict in 2-3 lines — Kumo handles the rest
PREDICT SUM(ON_CHAIN_TRANSFERS.AMOUNT, 0, 7, days) > 10000 FOR EACH WALLETS.WALLET_ID WHERE COUNT(APPROVAL_EVENTS.* WHERE APPROVAL_EVENTS.IP_CHANGED = 1, -3, 0, days) > 0
Prediction output
Every entity gets a score, updated continuously
| WALLET_ID | TIMESTAMP | TARGET_PRED | True_PROB |
|---|---|---|---|
| W001 | 2025-02-01 | False | 0.03 |
| W002 | 2025-02-01 | True | 0.91 |
| W003 | 2025-02-01 | False | 0.12 |
Understand why
Every prediction includes feature attributions — no black boxes
Wallet W002 (ExchB cold)
Predicted: 91% compromise probability
Top contributing features
Approval IP changed (3d)
1 event
42% attribution
On-chain transfer amount
48,000
26% attribution
Wallet type
cold
15% attribution
Wallet balance
$200M
10% attribution
Days since last_audit
10 days
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: Freeze outflows on wallets showing approval anomalies. A 6-hour verification delay costs nothing; a missed compromise costs $100M+.
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.
