Back to PortfolioFintech Case Study

Fraud Detection Platform

Real-time transaction monitoring system processing over 500k transactions daily, using machine learning to detect fraudulent activity with 95% accuracy while maintaining a false positive rate below 1.5%.

PythonMachine LearningAWSRedisPostgreSQLKafka
500k+
Transactions/day
95.0%
Detection accuracy
<300ms
Average latency
$1M+
Fraud prevented/year

The Challenge

Our fintech client was experiencing a surge in fraudulent transactions, with their legacy rule-based system struggling to keep pace with increasingly sophisticated fraud patterns. Key challenges included:

  • High false positive rate (6%) causing legitimate customer transactions to be blocked
  • Processing delays of 1-3 seconds impacting user experience
  • Manual review backlog of 2,500+ flagged transactions
  • Inability to detect emerging fraud patterns in real-time

Client Profile

Industry
Financial Services
Company Size
Series B Fintech Startup
Transaction Volume
500k+ daily transactions
Project Duration
12 weeks
Team Size
4 engineers + 1 ML specialist

Our Solution

ML-Powered Detection

Built ensemble model combining Random Forest, XGBoost, and neural networks trained on 2+ years of historical transaction data with 180+ features.

Real-Time Processing

Event-driven architecture using Kafka for streaming and Redis for sub-300ms scoring, with automatic scaling to handle peak loads.

Adaptive Learning

Continuous model retraining pipeline that learns from analyst feedback and emerging fraud patterns, with automated A/B testing.

Technical Architecture

System Components

Data Pipeline

Kafka streaming for real-time ingestion, S3 data lake for historical analysis

ML Models

SageMaker for model training/deployment, MLflow for experiment tracking

Inference Engine

Lambda functions with Redis caching for sub-300ms response times

Security & Compliance

End-to-end encryption, PCI DSS compliance, audit logging

Monitoring

CloudWatch dashboards, PagerDuty alerts, model drift detection

Analytics Dashboard

React-based analyst portal for reviewing flagged transactions

Key Technologies

Python 3.11
TensorFlow
XGBoost
AWS SageMaker
Apache Kafka
Redis
PostgreSQL
Docker
Terraform
FastAPI
React
CloudWatch

Results & Impact

Performance Improvements

75% reduction in false positives
From 6% to 1.5%, improving customer experience
5x faster transaction processing
Reduced latency from 1.5s to 280ms average
95% detection accuracy
Up from 92% with rule-based system
65% reduction in manual review queue
From 2,500 to 800 flagged transactions daily

Business Impact

$1M+ fraud prevented annually
Conservative estimate based on detected patterns
25% reduction in chargeback rates
Improved merchant relationships and trust
Significant boost in customer trust
Measured through positive support feedback
Positive ROI within 6 months
Including infrastructure and development costs
"PrismLabs transformed our fraud detection capabilities. The system not only catches more fraud but does it faster and with fewer false positives. Our fraud analysts can now focus on truly suspicious cases instead of wading through false alarms."
- VP of Engineering, Fintech Client

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