FinTech Data Modernization: Legacy to Cloud-Native Pipeline
Client Background
Our client is a fintech company providing payment processing and risk management services to merchants and financial institutions. They process millions of transactions daily and need to make real-time risk decisions.
The Challenge
- Mainframe-based transaction processing system (30+ years old)
- Batch ETL jobs running overnight
- Risk analytics based on 24-hour-old data
- Manual processes for data reconciliation
- Compliance reporting taking days to generate
Business Problems:
- Risk decisions were made on stale data, leading to higher fraud rates
- Fraud detection was reactive (detecting fraud after it happened)
- Compliance reports couldn't be generated on-demand for audits
- New features were impossible to implement on the mainframe
- System downtime was frequent (1.5% of the time)
Technical Challenges:
- Mainframe code was difficult to modify and maintain
- Batch processing meant 24-hour delays for analytics
- No real-time capabilities for fraud detection
- Data silos made it difficult to get a complete picture
- Scaling was expensive and slow
Our Solution
We designed and implemented a complete data modernization to a cloud-native architecture.
Architecture Overview
1. Real-Time Event Streaming
- All transactions flow through Apache Kafka topics in real-time
- Topics partitioned by merchant ID for parallel processing
- Event schemas versioned for backward compatibility
- Exactly-once semantics to prevent duplicate processing
Benefits:
- Real-time visibility into all transactions
- Multiple systems can consume the same events
- Can replay events for debugging or reprocessing
2. Cloud-Native Data Pipeline
- AWS Kinesis for high-throughput event processing
- Lambda functions for real-time transformations and enrichment
- Step Functions for complex workflow orchestration
- S3 for long-term storage
Benefits:
- Auto-scaling based on transaction volume
- Pay only for compute used
- No infrastructure to manage
3. Real-Time Risk Analytics
- Apache Flink for real-time stream processing
- Risk scoring models run on every transaction
- Results stored in Redis for sub-millisecond lookups
- Machine learning models deployed for fraud detection
Risk Scoring Pipeline:
1. Transaction event arrives in Kafka 2. Flink job enriches with customer history, merchant data, geolocation 3. ML model scores transaction for fraud risk 4. Risk score stored in Redis 5. Payment gateway queries Redis before authorizing transaction
Benefits:
- Risk decisions in <100ms (vs 24 hours before)
- Proactive fraud detection (blocking fraud before it happens)
- Can update models without downtime
4. Modern Data Warehouse
- Historical transaction data stored in Snowflake
- dbt transformations create business-ready datasets
- Automated compliance reporting
- Can query years of historical data in seconds
Benefits:
- Fast analytics queries (seconds vs hours)
- On-demand compliance reporting
- Can analyze trends over long time periods
5. Data Governance and Compliance
- Schema registry for event versioning and compatibility
- Data quality monitoring with Great Expectations
- Audit logging for all data access (required for compliance)
- GDPR-compliant data retention policies
- Encryption at rest and in transit
Benefits:
- Meets regulatory requirements (PCI DSS, GDPR, SOC 2)
- Data quality issues detected automatically
- Complete audit trail for compliance
Migration Strategy
We used a phased approach to minimize risk:
Phase 1 (Weeks 1-4): Event Streaming Infrastructure
- Set up Kafka cluster
- Define event schemas
- Build event producers for new transactions
- Dual-write to both legacy system and Kafka
Phase 2 (Weeks 5-8): Real-Time Risk Analytics
- Build Flink streaming jobs
- Deploy ML models for fraud detection
- Set up Redis for risk scores
- Gradually route risk queries to new system
Phase 3 (Weeks 9-12): Historical Data and Reporting
- Migrate historical data to Snowflake
- Build dbt transformations
- Create automated compliance reports
- Train users on new reporting tools
Phase 4 (Weeks 13-16): Decommission Legacy
- Verify all functionality migrated
- Run both systems in parallel for 2 weeks
- Gradually shift all traffic to new system
- Decommission mainframe
Results
Performance Improvements
- Processing Speed: 5x improvement (real-time vs 24-hour batch)
- Risk Decision Latency: Reduced from 24 hours to <100ms
- Compliance Report Generation: Reduced from days to minutes
- Analytics Query Time: Reduced from hours to seconds
Business Impact
- Fraud Detection Rate: Improved by 35% with real-time ML models
- False Positive Rate: Reduced by 20% (better models, real-time data)
- System Uptime: 99.99% (up from 98.5%)
- New Feature Development: Can now deploy new features in days (vs months)
Cost Improvements
- Infrastructure Costs: Reduced by 30% (cloud-native vs mainframe)
- Maintenance Costs: Reduced by 50% (automated vs manual)
- Development Velocity: 3x faster (modern tools vs mainframe)
Technical Highlights
Real-Time Risk Scoring
- Enriches each transaction with 50+ features (customer history, merchant data, geolocation, device fingerprinting)
- Runs ML model inference in <50ms
- Stores risk score in Redis for <1ms lookup
- Payment gateway queries Redis before authorizing transaction
Machine Learning Models
- Trained on 2 years of historical transaction data
- Features include: transaction amount, merchant category, time of day, customer behavior patterns, device fingerprinting
- Models updated weekly with new data
- A/B testing framework for model improvements
Compliance Reporting
- Daily transaction summaries
- Weekly risk reports
- Monthly compliance dashboards
- On-demand audit reports
- All reports generated in minutes (vs days)
Lessons Learned
- Real-time is possible: Legacy systems can be modernized to support real-time processing
- Phased migration reduces risk: Gradual migration allows for testing and validation
- Data governance is critical: Compliance requirements must be built in from the start
- ML models need real-time data: Stale data leads to poor model performance
- Cloud-native scales better: Can handle traffic spikes without over-provisioning
Conclusion
By modernizing from a legacy mainframe to a cloud-native, real-time data platform, we enabled the client to make risk decisions in milliseconds instead of days, improve fraud detection by 35%, and reduce infrastructure costs by 30%. The new architecture provides a foundation for continued innovation and growth.