Target Active Money Mule Accounts
“Among accounts that received deposits in the past 30 days, which will make rapid withdrawals in the next 7?”
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A real-world example
Among accounts that received deposits in the past 30 days, which will make rapid withdrawals in the next 7?
Money mule accounts follow a pattern: receive funds, then rapidly withdraw or transfer within days. Current rules flag all large withdrawals — 95% false positives. The "receive then move" pattern is the mule signature. A proactive account freeze costs $50; a completed mule chain costs $150K in regulatory exposure.
How KumoRFM solves this
Graph-powered fraud intelligence
Kumo’s backward time window filters to accounts with recent deposit activity, then predicts future withdrawals. It combines deposit recency, withdrawal velocity, account age, and network connections to identify the mule signature — recent deposits from flagged accounts followed by rapid outflows.
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 | account_holder | account_type | open_date |
|---|---|---|---|
| A001 | Alice Martinez | Personal | 2024-10-01 |
| A002 | Bob Chen | Personal | 2024-11-15 |
| A003 | Carol Davis | Personal | 2023-06-01 |
Transactions
| txn_id | account_id | amount | txn_type | timestamp |
|---|---|---|---|---|
| T001 | A001 | 8,500 | deposit | 2025-01-05 |
| T002 | A001 | 7,900 | withdrawal | 2025-01-07 |
| T003 | A002 | 12,000 | deposit | 2025-01-10 |
Write your PQL query
Describe what to predict in 2-3 lines — Kumo handles the rest
PREDICT SUM(TRANSACTIONS.AMOUNT WHERE TRANSACTIONS.TXN_TYPE = 'withdrawal', 0, 7, days) > 10000 FOR EACH ACCOUNTS.ACCOUNT_ID WHERE SUM(TRANSACTIONS.AMOUNT WHERE TRANSACTIONS.TXN_TYPE = 'deposit', -30, 0, days) > 5000
Prediction output
Every entity gets a score, updated continuously
| ACCOUNT_ID | TIMESTAMP | TARGET_PRED | True_PROB |
|---|---|---|---|
| A001 | 2025-02-01 | True | 0.89 |
| A002 | 2025-02-01 | True | 0.76 |
| A003 | 2025-02-01 | False | 0.08 |
Understand why
Every prediction includes feature attributions — no black boxes
Account A001 (Alice Martinez)
Predicted: 89% mule probability
Top contributing features
Deposit-to-withdrawal gap (days)
2 days
38% attribution
Deposit amount (30d sum)
$8,500
24% attribution
Withdrawal amount (7d sum)
$7,900
19% attribution
Account open date recency
92 days
12% attribution
Account type
Personal
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: Proactive account freeze costs $50; a completed mule chain costs $150K in regulatory exposure. The backward window focuses on the "receive then move" pattern. 50–70% fewer false positives vs. threshold rules.
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.
