High-Throughput ML Fraud Detection Architecture
A sub-120ms real-time fraud scoring pipeline designed to evaluate millions of payment transactions per day.
Design targets, not measured client results.
The Problem
Why teams need this pattern
Fast-growing payment channels attract card-not-present fraud and account takeover. Rule-only engines generate false positives that frustrate customers and flood analyst queues.
Ideal for
- Payment platforms outgrowing rule engines
- Teams adding ML scoring to an existing flow
Our Approach
Architectural approach
An inline ML inference pipeline on Kafka with gradient-boosted models. Features are computed from streaming velocity metrics and user history, returning a risk score fast enough to approve or challenge a payment in-flow.
System Design
Architectural layers & components
How data and control flow from the edge of the system to the people who use it.
- 01
Stream Ingestion
Apache Kafka cluster receiving payment transaction payloads.
- 02
Real-Time Feature Store
Redis storing sliding-window features such as velocity and location history.
- 03
Model Serving API
Triton Inference Server running optimized XGBoost / ONNX models.
- 04
Decision & Audit Engine
Rule evaluator and audit log for regulatory compliance.
Design Targets
What this architecture is built to achieve
Targets we design toward. We confirm them against your own data and workload before you commit to a build.
- Model quality depends on labelled fraud data from your own traffic.
- Latency targets assume co-located feature store and serving.
What You Get
Artifacts tailored to your environment
The blueprint is a starting point. These are the working documents and code we adapt for you.
Learn about our Artificial Intelligence services- Kafka sizing guide
- Feature store schemas
- ONNX optimization code
- Docker Compose dev environment
How We Work
From first call to working prototype
- 130–45 min
Discovery call
We review your constraints, existing systems, and success criteria, and tell you honestly whether this blueprint fits.
- 21–2 weeks
Fit & feasibility workshop
We adapt the reference architecture to your stack, validate the design targets against your real data, and produce a scoped plan.
- 3Scoped per project
Prototype, then build
We ship a working slice first so you can judge the approach before committing to a full build.
Technology Stack
Default tools & infrastructure
We swap components to fit your stack.
- Apache Kafka
- Redis
- Python / ONNX
- Triton Inference
- AWS EKS
- PostgreSQL
FAQ
Common questions
Related Blueprints
Explore related architectures
Fintech Loan Processing Blueprint & Mobile Workflow
A digitized microloan verification and instant credit-scoring pattern designed for high-concurrency mobile lending.
Real-Time Ad Measurement Data Pipeline Blueprint
A scalable ingestion and stream-processing pattern for billions of daily ad events with sub-minute attribution lag.
HIPAA-Aligned Multi-Region Cloud Migration Framework
A zero-downtime migration pattern for clinical applications that need strong compliance controls and continuous availability.
Want an architecture like this?
Book a call and we'll tell you honestly whether this blueprint fits, and how we'd adapt it to your stack and constraints.