Detect Fraud Ring Connections
“For each flagged account, which other accounts will it transact with in the next 30 days?”
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A real-world example
For each flagged account, which other accounts will it transact with in the next 30 days?
Fraud rings operate through networks of connected accounts. Investigators manually trace connections after fraud occurs. If you could predict the next accounts a flagged entity will transact with, you could proactively freeze or monitor the receiving accounts before funds move. Average fraud ring involves 8–12 accounts; recovering funds after movement drops below 20%.
How KumoRFM solves this
Graph-powered fraud intelligence
Kumo’s link prediction identifies which specific accounts from your database will form new transaction connections. It analyzes shared beneficiaries, common merchants, timing patterns, and account creation sequences to predict that A001 will send funds to A045 — a recently opened shell company account.
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
Flagged Accounts
| account_id | flag_reason | risk_score | flag_date |
|---|---|---|---|
| A001 | SAR filed | 92 | 2025-01-05 |
| A002 | ATO attempt | 87 | 2025-01-10 |
| A003 | Structuring | 78 | 2025-01-08 |
Transfers
| transfer_id | sender_id | receiver_id | amount | timestamp |
|---|---|---|---|---|
| TR01 | A001 | A045 | 9,800 | 2025-01-10 |
| TR02 | A001 | A078 | 4,500 | 2025-01-12 |
| TR03 | A002 | A045 | 15,200 | 2025-01-11 |
Accounts
| account_id | account_holder | account_type | open_date |
|---|---|---|---|
| A045 | Shell Corp X | Business | 2024-11-20 |
| A078 | J. Doe | Personal | 2024-12-01 |
Write your PQL query
Describe what to predict in 2-3 lines — Kumo handles the rest
PREDICT LIST_DISTINCT(TRANSFERS.RECEIVER_ID, 0, 30, days) FOR EACH FLAGGED_ACCOUNTS.ACCOUNT_ID
Prediction output
Every entity gets a score, updated continuously
| ACCOUNT_ID | CLASS | SCORE | TIMESTAMP |
|---|---|---|---|
| A001 | A045 | 0.94 | 2025-02-01 |
| A001 | A078 | 0.81 | 2025-02-01 |
| A002 | A045 | 0.89 | 2025-02-01 |
Understand why
Every prediction includes feature attributions — no black boxes
Account A001 → A045
Predicted: 94% link probability
Top contributing features
Shared transfer history (count)
3 prior transfers
36% attribution
Receiver account age (days)
72 days
24% attribution
Sender risk score
92
20% attribution
Transfer amount velocity (7d)
$14,300
12% attribution
Receiver account type
Business (shell)
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: Uncover 3–5 connected accounts per flagged entity. Freeze downstream accounts before funds move. Recover 40–60% more fraud losses.
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.
