Forecast Fraud Losses per Account
“How much in fraudulent transaction losses will each account experience in the next 30 days?”
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A real-world example
How much in fraudulent transaction losses will each account experience in the next 30 days?
Fraud ops allocates analyst bandwidth evenly across accounts. But 80% of losses come from 5% of accounts. If you could predict which accounts will generate the most fraud losses next month, you could pre-assign senior investigators, tighten auth controls, and reduce net losses. With average fraud losses exceeding $4.7M per incident at large banks, even a 15% improvement in allocation translates to millions saved.
How KumoRFM solves this
Graph-powered fraud intelligence
Kumo’s graph transformers analyze the full relational structure — account metadata, transaction patterns, merchant connections, and historical alert sequences — to predict cumulative fraud dollar amounts. Traditional ML uses account-level features only; Kumo sees that Account A001 shares merchants, IP addresses, and behavioral patterns with known fraud accounts, amplifying the loss prediction signal.
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 | risk_tier |
|---|---|---|---|
| A001 | Apex Corp | Commercial | high |
| A002 | J. Smith | Retail | medium |
| A003 | Vega LLC | Commercial | low |
Fraud Alerts
| alert_id | account_id | loss_amount | alert_type | timestamp |
|---|---|---|---|---|
| FA001 | A001 | 12,450 | card_fraud | 2025-01-10 |
| FA002 | A001 | 8,200 | wire_fraud | 2025-01-12 |
| FA003 | A002 | 340 | card_fraud | 2025-01-11 |
Write your PQL query
Describe what to predict in 2-3 lines — Kumo handles the rest
PREDICT SUM(FRAUD_ALERTS.LOSS_AMOUNT, 0, 30, days) FOR EACH ACCOUNTS.ACCOUNT_ID
Prediction output
Every entity gets a score, updated continuously
| ACCOUNT_ID | TIMESTAMP | TARGET_PRED |
|---|---|---|
| A001 | 2025-02-01 | $48,200 |
| A002 | 2025-02-01 | $320 |
| A003 | 2025-02-01 | $0 |
Understand why
Every prediction includes feature attributions — no black boxes
Account A001
Predicted: $48,200 in fraud losses
Top contributing features
Fraud alerts (30d count)
7 alerts
41% attribution
Wire fraud loss amount
$28,650
27% attribution
Connected high-risk accounts
4 accounts
18% attribution
Account risk tier
high
9% attribution
Account type
Commercial
5% 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: Focus investigator bandwidth on the 5% of accounts driving 80% of losses. Reduce net fraud losses 15–25% without adding headcount.
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.
