The problem with fraud detection
Your fraud team caught the pattern yesterday. The money left last week.
When batch systems update hourly and fraudsters move in milliseconds, you're not detecting fraud — you're documenting losses.
Fraud activity
What the model sees
The Shift
The Old Era
Humans review flagged transactions. Batch pipelines run overnight. Rules update weekly. Fraud teams accept some loss as cost of doing business. The gap between fraud and detection is measured in hours.
The New Era
Fraudsters move in milliseconds. Coordinated rings. Synthetic identities. Card testing at scale. By the time your batch job runs, the money is gone and they've moved on.
Sound Familiar?
The struggles we hear every week
These aren't edge cases. They're Tuesday.
The pattern was obvious. In hindsight.
A compromised card hits your system. By the time your batch model flags it, there are 47 approved transactions across 12 merchants. The chargeback paperwork alone takes a week.
You're not catching fraud — you're processing claims.
Mobile says yes. Call center says no.
A customer calls, frustrated. Your app approved a transfer. Your phone rep sees a fraud hold. Same account, same minute, different answers.
Conflicting decisions erode trust faster than fraud erodes revenue.
The ring moved on before your rules caught up.
Your fraud team identifies a new pattern on Tuesday. The rules deploy Thursday. By Friday, the attackers have shifted tactics.
Static rules can't catch dynamic adversaries.
The Goal
Fraud detection that keeps up
Not faster batch jobs. Not more rules. A fundamentally different architecture where decisions happen on live context — not yesterday's snapshot.
Where Latency Matters
Every fraud use case has a latency budget. Miss it, and the transaction clears before your model scores it.
< 50ms
CNP Fraud
Score during authorization
Real-time
Account Takeover
Cross-channel correlation
Live
Velocity
Detect rapid-fire patterns
Unified
Ring Detection
Multi-account signals
What to Evaluate
When comparing fraud infrastructure, these four dimensions separate real-time systems from batch pipelines with a fast API.
Latency
vs. Minutes to hours
Freshness
vs. Last pipeline run
Consistency
vs. Siloed systems
Adaptability
vs. Static rules
How Tacnode Delivers
Cross-channel fraud correlation
Suspicious signals from multiple channels converge in the Context Lake for real-time scoring.
Watch the detection pipeline
Transaction initiated
Card-not-present
Input
Online Purchase
Card-not-present
Context Lake
Output
Risk Factors
Related Reading
Articles and guides on the concepts behind real-time fraud detection
Feature Freshness, Explained
How freshness breaks for ML features — and why it matters for fraud scoring
What Is a Feature Store?
Consistent, real-time features for every model and agent in your stack
What Is Data Freshness?
Why fast queries on stale data are worse than slow queries on fresh data
What Is a Context Lake?
A unified data layer for shared, live, semantic context across agents
See Tacnode on your data
We'll walk through your architecture and show you where freshness gaps are costing you.