Flag Anomalous Single Transactions
“For each cardholder, will their single largest transaction exceed $10,000 in the next 30 days?”
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A real-world example
For each cardholder, will their single largest transaction exceed $10,000 in the next 30 days?
High-value single transactions — jewelry, electronics, gift cards — are the most common high-dollar fraud pattern. Rules decline transactions over a fixed limit, frustrating legitimate high spenders. Predicting which cardholders will have an anomalously large transaction lets you pre-authorize legitimate ones and tighten controls on suspicious ones.
How KumoRFM solves this
Graph-powered fraud intelligence
PQL’s MAX aggregation focuses on peak value, not totals. Kumo learns individual spending patterns: CH001 (Platinum, avg $4,200) having a $10K+ transaction is normal. CH003 (Silver, avg $650) having a $10K+ transaction is a red flag. The graph reveals device, location, and merchant signals that distinguish legitimate large purchases from fraud.
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
Cardholders
| cardholder_id | card_type | avg_monthly_spend | account_age_years |
|---|---|---|---|
| CH001 | Platinum | 4,200 | 5.3 |
| CH002 | Gold | 1,800 | 2.1 |
| CH003 | Silver | 650 | 0.8 |
Card Transactions
| txn_id | cardholder_id | amount | mcc_code | is_international | timestamp |
|---|---|---|---|---|---|
| T001 | CH001 | 245.00 | 5411 | 0 | 2025-01-05 |
| T002 | CH002 | 6,800 | 5944 | 0 | 2025-01-18 |
| T003 | CH003 | 35.00 | 5812 | 1 | 2025-01-10 |
Write your PQL query
Describe what to predict in 2-3 lines — Kumo handles the rest
PREDICT MAX(CARD_TRANSACTIONS.AMOUNT, 0, 30, days) > 10000 FOR EACH CARDHOLDERS.CARDHOLDER_ID
Prediction output
Every entity gets a score, updated continuously
| CARDHOLDER_ID | TIMESTAMP | TARGET_PRED | True_PROB |
|---|---|---|---|
| CH001 | 2025-02-01 | False | 0.11 |
| CH002 | 2025-02-01 | True | 0.84 |
| CH003 | 2025-02-01 | True | 0.67 |
Understand why
Every prediction includes feature attributions — no black boxes
Cardholder CH003
Predicted: 67% probability of $10K+ single transaction
Top contributing features
Avg monthly spend
$650
36% attribution
Card type
Silver
25% attribution
Account age (years)
0.8
19% attribution
Max transaction amount (30d)
$35.00
13% attribution
International transaction ratio
100%
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: MAX focuses on the peak value, not the total. CH003 (avg spend $650) flagged for a likely $10K+ transaction — set tighter auth. Prevent 1,000+ high-value fraud events and save $10M+ 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.
