🚨 Fraud Detection AI - Real-time

Stop the fraud,
not the customer

Anti-fraud that is too aggressive blocks real customers and burns revenue; too soft and it costs you in chargebacks. We build an in-transaction decision engine (sub-100ms) that balances the block against conversion - and learns from every chargeback for the next round.

Anti-fraud consultation Back to FinTech AI
⚠️ The balance

Every false decline is a real customer who left

The metric is not "how much fraud we caught" - it is the ratio of fraud loss to false declines. Done right, AI moves both down.

<100ms
P95 decision
Graph
velocity + rings
Chargeback
feedback loop
PSD2
SCA / TRA
⚙️ What we build

An end-to-end anti-fraud engine

⚡

Real-time Scoring

A decision inside the transaction authorization window.

  • Latency sub-100ms P95
  • Real-time feature store
  • Approve / review / decline
  • Deterministic fallback
🖥️

Device & Behavioral

Who is behind the transaction - not just what is in it.

  • Device fingerprinting
  • Behavioral biometrics
  • Bot / emulator detection
  • Account takeover (ATO) signals
🏃

Velocity & Rules

Fast rules on top of an ML model.

  • Velocity checks (card / device / IP)
  • Rule engine for explicit policy
  • Hybrid: rules + GBM
  • Shadow mode before enforcement
🕸️

Graph & Fraud Rings

Organized fraud operates as a network.

  • Fraud-ring detection
  • Shared-identity linking
  • Shared-device / shared-card
  • Analyst-facing visualization
🔁

Chargeback Feedback Loop

Every chargeback is a label for the next model.

  • Ingest chargeback / dispute data
  • Automated relabeling
  • Controlled retraining
  • Fraud-rate tracking over time
🔐

SCA / Step-up

Step-up authentication only when needed - not for everyone.

  • Risk-based SCA (PSD2 TRA)
  • Dynamic step-up
  • Exemption management
  • Reduced friction for low-risk
📋 Regulation & standards

Built for the standard and the card schemes

💳

PCI-DSS + Card Schemes

PCI-DSS for card data, and Visa/Mastercard rules including their fraud monitoring programs (VAMP and equivalents).

🇪🇺

PSD2 SCA / TRA

Strong Customer Authentication and Transaction Risk Analysis exemptions under the PSD2 RTS - balancing security against conversion.

🇺🇸

US: Nacha + scheme rules

For US operations: Nacha rules for ACH fraud, plus card-scheme dispute and chargeback rules.

📊 Industry benchmarks

What public research shows - and where we come in

📉

Hybrid cuts false-positives

What the industry reports: Moving from rule-based to hybrid (behavioral + GBM) reduces false-positives by 30-50% while preserving recall (Bain payments tech 2024).

Where we come in: An ML layer on top of the rules engine - with shadow mode before enforcement goes live in production.

⚡

Latency is a product requirement

What the industry reports: An anti-fraud decision has to land inside the card authorization window (sub-100ms) to avoid hurting checkout.

Where we come in: Designing a feature store and serving that hold P95 sub-100ms under real load.

🔁

The feedback loop is the difference

What the industry reports: A model without a chargeback loop ages quickly as fraud patterns shift.

Where we come in: Wiring chargeback/dispute back into labeling and controlled retraining.

* Industry benchmark (Bain 2024), not a client metric. SLAtech, since 2004, 14 countries. Commercial examples on a consultation call.

Want anti-fraud that does not burn conversion?

30-minute consultation - we review the existing decision engine and show where AI lowers fraud loss and false declines together.