Classify Structuring Attempts
“For each cash deposit, does the pattern indicate structuring to avoid CTR filing?”
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A real-world example
For each cash deposit, does the pattern indicate structuring to avoid CTR filing?
Structuring — making multiple deposits just below $10,000 to avoid Currency Transaction Reports — is a federal crime. Current rules flag deposits between $8,000–$9,999, generating thousands of false positives. Pattern-based scoring on the full account behavior history can identify real structuring while cutting false alerts 70%+.
How KumoRFM solves this
Graph-powered fraud intelligence
Instead of threshold rules, Kumo scores each deposit based on the complete relational pattern: same account making near-threshold deposits at different branches, different tellers, on consecutive days. The graph reveals that A001 visits 4 branches in 3 days with deposits averaging $9,300 — a pattern invisible to single-transaction rules.
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
Cash Deposits
| deposit_id | account_id | amount | branch_id | teller_id | timestamp |
|---|---|---|---|---|---|
| CD01 | A001 | 9,400 | BR12 | TL05 | 2025-01-10 |
| CD02 | A001 | 9,200 | BR08 | TL11 | 2025-01-11 |
| CD03 | A002 | 4,500 | BR12 | TL05 | 2025-01-10 |
Accounts
| account_id | account_holder | risk_rating | kyc_date |
|---|---|---|---|
| A001 | Apex Corp | high | 2023-03-15 |
| A002 | J. Smith | low | 2022-08-01 |
Write your PQL query
Describe what to predict in 2-3 lines — Kumo handles the rest
PREDICT CASH_DEPOSITS.AMOUNT > 9000 FOR EACH CASH_DEPOSITS.DEPOSIT_ID WHERE ACCOUNTS.RISK_RATING = "high"
Prediction output
Every entity gets a score, updated continuously
| DEPOSIT_ID | SCORE |
|---|---|
| CD01 | 0.94 |
| CD02 | 0.91 |
| CD03 | 0.03 |
Understand why
Every prediction includes feature attributions — no black boxes
Deposit CD01
Predicted: 94% structuring probability
Top contributing features
Deposit amount
$9,400
35% attribution
Distinct branches (3d window)
4 branches
28% attribution
Distinct tellers (3d window)
4 tellers
18% attribution
Account risk rating
high
12% attribution
KYC recency (days since review)
672 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: Replace blunt threshold rules with pattern-based scoring. Cut false positives 70%+ while catching more real structuring. Save 4,000+ analyst hours annually.
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.
