Predict Account Takeover
“Which accounts will experience an unauthorized login in the next 14 days?”
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A real-world example
Which accounts will experience an unauthorized login in the next 14 days?
ATO is the fastest-growing fraud vector — up 72% year-over-year. Current rules trigger after 10 failed logins, but by then the attacker has already tried credential-stuffing. Banks need to predict which accounts are being targeted before the attack succeeds. Average ATO costs $12K per incident. At scale, 500 prevented ATOs = $6M saved.
How KumoRFM solves this
Graph-powered fraud intelligence
Kumo analyzes the relational graph of login events, device fingerprints, IP networks, and account relationships. When Account A002 shows login attempts from IPs that have attacked other accounts in the network, Kumo detects the cross-account signal that single-account rules miss.
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 | customer_name | account_age_years | mfa_enabled |
|---|---|---|---|
| A001 | Alice Martinez | 5.3 | 1 |
| A002 | Bob Chen | 2.1 | 0 |
| A003 | Carol Davis | 0.8 | 1 |
Login Events
| event_id | account_id | ip_address | device_fp | success | timestamp |
|---|---|---|---|---|---|
| LE01 | A001 | 72.14.x.x | d8f3a1 | 1 | 2025-01-10 |
| LE02 | A002 | 91.22.x.x | b7c2e9 | 0 | 2025-01-12 |
| LE03 | A002 | 103.5.x.x | a1f4d2 | 0 | 2025-01-12 |
Write your PQL query
Describe what to predict in 2-3 lines — Kumo handles the rest
PREDICT COUNT(LOGIN_EVENTS.* WHERE LOGIN_EVENTS.SUCCESS = 0, 0, 14, days) > 5 FOR EACH ACCOUNTS.ACCOUNT_ID
Prediction output
Every entity gets a score, updated continuously
| ACCOUNT_ID | TIMESTAMP | TARGET_PRED | True_PROB |
|---|---|---|---|
| A001 | 2025-02-01 | False | 0.04 |
| A002 | 2025-02-01 | True | 0.87 |
| A003 | 2025-02-01 | False | 0.02 |
Understand why
Every prediction includes feature attributions — no black boxes
Account A002 (Bob Chen)
Predicted: 87% ATO probability
Top contributing features
Failed logins (14d count)
12 attempts
38% attribution
Distinct IP addresses (14d)
9 IPs
25% attribution
MFA enabled
No
19% attribution
Account age (years)
2.1
11% attribution
Device fingerprint changes
6 new devices
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: Preemptive MFA step-up on 3% of accounts prevents 60%+ of ATO losses. Average ATO costs $12K per incident — preventing 500 saves $6M.
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.
