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.
The Hidden Problem
"Real-time" stacks are brittle
Most teams believe they've solved fraud latency. Kafka streams events. Redis caches state. Feature stores serve features. But under load, these systems break in subtle ways—your scoring API evaluates against state that never coexisted.
Velocity counters lag
The counter says 2 transactions. There are actually 5 in flight. You approve all of them.
Feature propagation delays
Your ML feature was fresh when computed. By the time the model scores, the world moved.
Cross-channel mismatch
Web sees one state, mobile sees another. Both score "low risk." Together, it's fraud.
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.
Your pipeline is real-time. Your state isn't.
Kafka streams the event in 50ms. But your velocity counter updated 3 seconds ago. Your fraud score evaluated against state that never existed together.
Fast queries on inconsistent state is just well-documented loss.
The Goal
Transactional correctness, not just speed
A fraud decision without a transactional state update is a suggestion, not enforcement. You need decisions and state mutations in the same boundary—atomically.
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
Every channel scores against identical state
All signals converge in the Context Lake. Every scoring request sees the same point-in-time snapshot—no cross-channel mismatch, no stale velocity counters.
Watch the detection pipeline
Transaction initiated
Card-not-present
Input
Online Purchase
Card-not-present
Context Lake
Output
Risk Factors
Is this your problem?
If your fraud decisions touch shared mutable state—velocity counters, risk flags, account limits—and that state changes faster than your scoring layer refreshes it, you need transactional correctness.
When you need this
- •Transaction volume where batch windows matter
- •Multiple channels scoring the same accounts
- •Velocity counters, risk flags, or limits that update mid-transaction
- •Auth latency budgets under 100ms
When you don't
- •Manual review is acceptable
- •Low transaction volume
- •Single-channel operation
- •Seconds of latency tolerable
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
Stop catching fraud after the loss
We'll walk through your fraud stack and show you where latency windows create exposure.