Finance
Financial Risk Engine
Real-time fraud detection engine processing billions in daily transaction volume with sub-50ms inference latency.
85%
False Positive Drop
<50ms
Inference Latency
$2.4B
Daily Volume
99.9%
Detection Rate
85%
False Positive Drop
<50ms
Inference Latency
$2.4B
Daily Volume
99.9%
Detection Rate
Measurable Results
Business Impact
Context
The Challenge
A global payments processor needed fraud detection that could keep pace with transaction volume without overwhelming analysts or introducing latency into the payment flow.
“Fraud patterns evolve daily. We needed a system that learns as fast as the threat landscape—not one that requires manual rule updates.”
Rule-Based Systems
Static fraud rules generated excessive false positives, overwhelming analyst teams and delaying legitimate transactions.
Analyst Bottlenecks
Manual investigation workflows could not scale with 2.4B daily transaction volume.
Fragmented Signals
Transaction, device, and behavioral data lived in siloed systems with no unified feature layer.
Our Approach
The Solution
Built a real-time inference pipeline using ensemble ML models on Kubernetes, with feature stores, drift-triggered retraining, and analyst-facing investigation workflows.
Real-time Fraud Engine
Sub-50ms ensemble ML inference on billions of daily transactions without disrupting payment flows.
Adaptive ML Models
Self-learning fraud models with drift-triggered retraining that evolve as threat patterns change.
Analyst Workflows
Automated triage and investigation portal reducing analyst workload by 85%.
Risk Dashboard
Unified view of transaction risk, case management, and regulatory reporting for global operations.
Cloud Infrastructure
Kubernetes-native serving on AWS with Kafka streaming, Redis caching, and 99.99% uptime SLA.
System Design
Solution Architecture
Risk Analysts
Investigation Portal
Inference Engine
Ensemble ML Models
Feature Store
Risk Dashboard
Delivery
Implementation Timeline
Discovery
Fraud pattern analysis, data audit, and latency requirements definition.
Architecture
Kafka streaming design, feature store schema, and model serving plan.
Model Training
Ensemble model development and backtesting against historical fraud data.
Deployment
Shadow deployment, A/B validation, and analyst portal integration.
Go Live
Full production cutover processing $2.4B daily volume.
Engineering
Technology Stack
Python
FastAPI
Kubernetes
TensorFlow
Kafka
AWS
Docker
Redis
Transformation
Business Outcomes
Before
- Static fraud rules
- Manual investigations
- High false positive rates
- Reactive threat response
After
- Adaptive ML models
- Automated triage workflows
- 85% fewer false positives
- Real-time pattern detection
“The inference pipeline handles our full transaction volume without breaking a sweat. False positives dropped 85% and our analysts finally focus on real threats.”
