Transaction Fraud Detection
“Is this transaction fraudulent?”
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A real-world example
Is this transaction fraudulent?
US card fraud losses exceeded $12B in 2024 (Nilson Report). Legacy rule-based systems generate 95%+ false-positive rates, blocking legitimate purchases and driving $118B in annual false declines (Aite-Novarica). Every false decline costs the issuer $118 in lost revenue and customer goodwill. Meanwhile, sophisticated fraud rings exploit the blind spots between siloed detection systems, running small test transactions across merchant categories before executing high-value fraud. The data needed to detect these patterns spans cards, merchants, devices, and time-series transaction flows.
How KumoRFM solves this
Relational intelligence built for banking and financial data
Kumo connects cardholder profiles, transaction histories, merchant data, device fingerprints, and geographic signals into a single relational graph. The model detects that Transaction T-900412 involves a card whose recent velocity spiked 4x, at a merchant category the cardholder has never used, from a device IP in a different state than the cardholder's home region, and the transaction amount matches a known test-then-hit pattern. These multi-hop relational signals catch fraud that single-table models miss while reducing false positives by 40%.
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 | name | home_state | avg_monthly_spend | card_type |
|---|---|---|---|---|
| CH-4001 | Susan Chen | CA | $3,200 | Platinum |
| CH-4002 | Robert James | TX | $1,800 | Gold |
| CH-4003 | Ana Rivera | NY | $5,100 | Signature |
TRANSACTIONS
| txn_id | cardholder_id | merchant_id | amount | channel | timestamp |
|---|---|---|---|---|---|
| T-900410 | CH-4001 | M-220 | $12.50 | card_present | 2025-09-15 14:22 |
| T-900411 | CH-4001 | M-891 | $47.00 | online | 2025-09-15 14:38 |
| T-900412 | CH-4001 | M-3042 | $2,899 | online | 2025-09-15 14:41 |
MERCHANTS
| merchant_id | name | category | risk_tier | country |
|---|---|---|---|---|
| M-220 | Corner Coffee | Food & Beverage | Low | US |
| M-891 | StreamFlix | Digital Services | Low | US |
| M-3042 | ElectroMart | Electronics | Medium | US |
DEVICE_SIGNALS
| txn_id | device_hash | ip_state | browser | is_vpn |
|---|---|---|---|---|
| T-900410 | D-8812 | CA | Safari | False |
| T-900411 | D-8812 | CA | Safari | False |
| T-900412 | D-1199 | FL | Chrome | True |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(TRANSACTIONS.IS_FRAUD = 'True', 0, 0, days) FOR EACH TRANSACTIONS.TXN_ID WHERE TRANSACTIONS.AMOUNT > 100
Prediction output
Every entity gets a score, updated continuously
| TXN_ID | CARDHOLDER | AMOUNT | FRAUD_SCORE | DECISION |
|---|---|---|---|---|
| T-900410 | Susan Chen | $12.50 | 0.02 | Approve |
| T-900411 | Susan Chen | $47.00 | 0.05 | Approve |
| T-900412 | Susan Chen | $2,899 | 0.94 | Block |
Understand why
Every prediction includes feature attributions — no black boxes
Transaction T-900412 ($2,899 at ElectroMart)
Predicted: 94% fraud probability
Top contributing features
Device mismatch (new device, different state)
FL vs CA
31% attribution
Velocity spike (3 txns in 19 minutes)
4x normal
25% attribution
Merchant category never used before
Electronics
19% attribution
VPN detected on transaction device
True
14% attribution
Amount anomaly vs cardholder pattern
3.6x avg
11% 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: Reduce false positives by 40% and catch 25% more fraud, saving $150-250M annually for a top-10 issuer while recovering $118 in revenue per avoided false decline.
Related use cases
Explore more financial services use cases
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.
