Rank Suspicious Counterparties
“For each high-risk account, which counterparties should investigators review first? Top 10.”
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A real-world example
For each high-risk account, which counterparties should investigators review first? Top 10.
Each SAR investigation requires tracing the counterparty network. An investigator manually reviews 20–50 beneficiaries per case. Ranking the most suspicious future counterparties by likelihood of transaction cuts investigation time in half, letting teams process 2x more SARs.
How KumoRFM solves this
Graph-powered fraud intelligence
Kumo’s ranked link prediction generates a personalized prioritized list of counterparties for each flagged account. It analyzes transaction timing, amount patterns, beneficiary geography, and network overlap to rank BN05 (offshore, shared connections) higher than BN12 (known trade partner, low risk).
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
High-Risk Accounts
| account_id | risk_score | flag_reason | review_date |
|---|---|---|---|
| A001 | 92 | SAR filed | 2025-01-05 |
| A002 | 87 | Structuring | 2025-01-10 |
| A003 | 95 | Fraud ring | 2025-01-08 |
Wire Transfers
| wire_id | sender_id | beneficiary_id | amount | timestamp |
|---|---|---|---|---|
| W001 | A001 | BN05 | 45,000 | 2025-01-10 |
| W002 | A001 | BN12 | 28,000 | 2025-01-14 |
| W003 | A002 | BN05 | 9,800 | 2025-01-11 |
Beneficiaries
| beneficiary_id | beneficiary_name | country | entity_type |
|---|---|---|---|
| BN05 | Offshore Holdings | Cayman Is. | Corporate |
| BN12 | Trade Corp | Panama | Corporate |
Write your PQL query
Describe what to predict in 2-3 lines — Kumo handles the rest
PREDICT LIST_DISTINCT(WIRE_TRANSFERS.BENEFICIARY_ID, 0, 30, days) RANK TOP 10 FOR EACH HIGH_RISK_ACCOUNTS.ACCOUNT_ID
Prediction output
Every entity gets a score, updated continuously
| ACCOUNT_ID | CLASS | SCORE | TIMESTAMP |
|---|---|---|---|
| A001 | BN05 | 0.93 | 2025-02-01 |
| A001 | BN12 | 0.84 | 2025-02-01 |
| A002 | BN05 | 0.88 | 2025-02-01 |
Understand why
Every prediction includes feature attributions — no black boxes
Account A001 → BN05
Predicted: 93% counterparty link score
Top contributing features
Wire transfer volume to BN05 (30d)
$45,000
37% attribution
Beneficiary country risk
Cayman Is. (high)
25% attribution
Account risk score
92
18% attribution
Beneficiary entity type
Corporate (offshore)
12% attribution
Shared beneficiary overlap with other flagged accounts
3 accounts
8% 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: Cut investigation time 40–50% per case. Investigators review top 10 ranked counterparties instead of all 50. Process 2x more SARs with the same team.
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.
