A modern microservices-based digital banking platform that achieved 99.99% uptime and 3x faster transaction processing, replacing legacy mainframe infrastructure.
The Challenge
VaultPay's legacy mainframe system processed transactions in batch cycles, meaning customers waited hours for transfers to clear. The system experienced frequent outages during peak periods, averaging 99.2% uptime — far below the 99.99% required by regulators. Fraud detection relied on rule-based systems that generated thousands of false positives daily, overwhelming the compliance team and letting sophisticated fraud slip through.
- Legacy batch processing meant transactions took 4-6 hours instead of real-time
- System uptime averaged 99.2%, resulting in costly regulatory penalties
- Rule-based fraud detection generated 3,000+ false positives daily while missing actual fraud
- Mobile banking app had a 2.1-star rating with constant crash complaints
- Onboarding a new customer took 5 business days of manual KYC processing
Our Solution
Advenno designed a cloud-native microservices platform on Kubernetes with event-driven architecture for real-time transaction processing. The solution includes ML-powered fraud detection, automated KYC verification using computer vision, and a completely rebuilt mobile banking experience with biometric authentication and instant payments.
- Event-driven microservices architecture enabling real-time transaction processing
- ML-powered fraud detection with 97.3% accuracy and 85% fewer false positives
- Automated KYC with document verification and facial recognition reducing onboarding to 15 minutes
- Rebuilt mobile banking app with biometric auth, instant transfers, and spending insights
- Multi-region deployment with automated failover ensuring 99.99% uptime
Our Approach
Regulatory & Security Assessment
Worked with VaultPay's compliance team and external auditors to map all regulatory requirements (PCI-DSS, SOC 2, PSD2) and design security controls into the architecture from day one.
Strangler Fig Migration Strategy
Designed a gradual migration plan using the strangler fig pattern to replace legacy components one at a time without disrupting existing services or requiring a big-bang cutover.
Core Platform Development
Built the core transaction engine, account management, and payment processing microservices with comprehensive automated testing and chaos engineering practices.
Fraud Detection ML Pipeline
Trained and deployed machine learning models on 5 years of transaction data, implementing real-time scoring that evaluates each transaction in under 50 milliseconds.
Gradual Cutover & Monitoring
Migrated customers in cohorts of 10,000, running parallel systems to verify accuracy before full cutover, with 24/7 war-room support during the transition.
The Results
The new platform processes transactions in real-time with 99.99% uptime, meeting all regulatory requirements. Fraud detection accuracy improved dramatically, saving VaultPay an estimated $8.7 million annually in fraud losses and compliance costs. The rebuilt mobile app reached a 4.7-star rating, and customer onboarding dropped from 5 days to 15 minutes, driving a 40% increase in new account openings.
Technologies Used
The migration was something our board had been afraid of for years. Advenno's phased approach meant we never had a single minute of unplanned downtime during the transition. Our customers didn't even notice the switch — except that everything suddenly worked better.