How Finova Capital Transformed Legacy Systems into a Cloud-Native Banking Platform

## Overview Finova Capital, a mid-sized financial services company headquartered in London, had built its entire operations around a monolithic architecture developed over 15 years earlier. While the system had served the company well during its growth phase, by 2024 it had become a significant liability. Customer complaints about slow transaction processing, inability to launch new products quickly, and frequent system outages were threatening the company's competitive position in an increasingly digital financial services market. The company processed approximately $2 billion in transactions annually through its platform, serving over 50,000 business clients across the UK and Europe. However, their technical debt had accumulated to the point where even minor changes required extensive testing and often introduced unintended consequences. The development team spent more than 60% of their time maintaining existing systems rather than building new features that customers wanted. Webskyne was engaged to assess the situation, design a modern architecture, and execute a phased migration strategy that would minimize business disruption while delivering immediate value. The engagement spanned nine months from initial assessment to full production deployment, with measurable results appearing within the first quarter after launch. ## The Challenge Finova Capital faced several interconnected challenges that demanded a comprehensive solution rather than incremental improvements. ### Technical Debt and Infrastructure Limitations The existing platform was built on a Java-based monolith that had been extended numerous times over the years through acquired companies and evolving business requirements. The system ran on aging on-premises infrastructure that required manual provisioning and had no built-in redundancy. When outages occurred, recovery times often exceeded four hours, causing significant business disruption and customer dissatisfaction. The database layer was particularly problematic. A single Oracle database instance handled all operations, with multiple application servers connecting directly to it. During peak periods, database locks created bottlenecks that slowed every transaction. The lack of horizontal scaling meant that during high-traffic periods, the entire system would become sluggish. ### Business Velocity Constraints Perhaps most critically, the technical architecture had become a barrier to business innovation. Launching a new product required an average of six months of development work, followed by another two months of testing and deployment. Competitive pressures in the FinTech space demanded that companies respond to market changes within weeks, not months. The development team was highly skilled but frustrated. They understood what needed to be done but were constrained by a system that made every change risky. Code reviews revealed that even small modifications required examining thousands of lines of interconnected code to understand potential side effects. ### Security and Compliance Concerns As a financial services company, Finova operated under strict regulatory requirements including GDPR, PSD2, and FCA guidelines. The legacy system had been incrementally patched to address security vulnerabilities over the years, but it was never designed with modern security principles in mind. The security team was concerned about potential vulnerabilities that might exist in the aging codebase. Furthermore, the lack of comprehensive logging and audit trails made compliance reporting time-consuming and error-prone. Manual processes for generating regulatory reports consumed significant staff time and introduced human error risks. ## Goals Following detailed consultation with stakeholders across the organization, we established clear objectives for the transformation project: **Primary Goals:** 1. Reduce average transaction processing time from 3.2 seconds to under 600 milliseconds 2. Enable deployment of new products within two weeks rather than six months 3. Achieve 99.99% uptime with automatic failover capabilities 4. Reduce infrastructure and operational costs by 50% within 18 months 5. Establish a modern DevOps culture with automated testing and continuous deployment **Secondary Goals:** 1. Implement comprehensive security controls meeting SOC 2 Type II requirements 2. Create real-time analytics capabilities for business intelligence 3. Establish a scalable architecture that can support 10x growth without re-architecture 4. Reduce mean time to recovery from four hours to under 15 minutes ## Approach We recommended a phased approach that would deliver incremental value while managing risk throughout the transformation. ### Phase 1: Assessment and Planning (Weeks 1-4) The first phase involved comprehensive analysis of the existing systems, business processes, and technical requirements. We conducted over 40 interviews with stakeholders across the organization, from front-line customer service representatives to executive leadership. This ensured that the solution would address real business needs rather than technical objectives in isolation. Technical assessment included detailed code analysis, infrastructure mapping, and performance profiling. We identified the most critical business functions and the components that could be decoupled and modernized first. This analysis revealed that approximately 70% of the system's functionality could be replaced with modern microservices while maintaining business continuity. ### Phase 2: Foundation and Parallel Development (Weeks 5-16) With a clear understanding of the target architecture, we established the foundational elements of the new system. This included setting up the AWS cloud environment with proper security boundaries, implementing CI/CD pipelines, and creating the organizational structures needed for DevOps practices. Development teams began building new microservices while the legacy system continued operating. We implemented an API gateway that could route requests to either the old or new systems, allowing gradual migration of functionality. This approach minimized risk by ensuring that failures in new components would not affect the existing system. ### Phase 3: Incremental Migration (Weeks 17-32) The migration proceeded function by function, starting with the least critical but most visible improvements. Customer-facing features like account balance inquiries and transaction history were moved to the new platform first, demonstrating immediate value to users. Behind the scenes, core processing functions were gradually migrated with extensive testing at each stage. Throughout this phase, we maintained detailed monitoring to identify any performance issues or edge cases that had not been anticipated. The API gateway collected metrics that informed optimization efforts and helped prioritize remaining migrations. ### Phase 4: Full Deployment and Optimization (Weeks 33-40) The final phase involved switching remaining functions to the new platform, decomissioning the legacy infrastructure, and optimizing performance based on real-world usage patterns. We conducted extensive load testing to validate that the system could handle peak traffic volumes. ## Implementation ### Architecture Decisions The new architecture leveraged AWS managed services to minimize operational overhead while maximizing reliability. Key components included: **Compute and Containerization:** Amazon EKS (Elastic Kubernetes Service) provided the container orchestration layer, enabling teams to deploy and scale services independently. Each microservice was designed to handle a specific business function, with clear interfaces and minimal dependencies. **Data Layer:** We implemented a polyglot persistence strategy. Amazon RDS with PostgreSQL handled transactional data requiring strong consistency. Amazon ElastiCache provided ultra-low-latency caching for frequently accessed data. Amazon DynamoDB offered the scalability needed for audit logs and analytics data. **Messaging and Integration:** Amazon EventBridge and SQS enabled asynchronous communication between services, ensuring that slow operations did not block user requests. This event-driven architecture also provided the foundation for real-time analytics. **Security Implementation:** We implemented a zero-trust security model with fine-grained access controls. All traffic between services was encrypted, and IAM roles followed the principle of least privilege. AWS WAF provided protection against common web attacks, while VPC isolation ensured network security. ### DevOps Transformation The technical architecture was only part of the transformation. Equally important was changing how the team worked. We implemented: **Infrastructure as Code:** All infrastructure was defined in Terraform, enabling version control, peer review, and reproducible deployments. This eliminated the snowflake servers that had plagued the previous environment. **Automated Testing:** Each service included comprehensive unit tests, integration tests, and contract tests. We achieved over 85% code coverage, with critical paths exceeding 95%. Automated testing caught bugs before they reached production. **Continuous Deployment:** Changes automatically progressed through development, staging, and production environments after passing tests and receiving approval. This reduced the average time from code commit to production from two weeks to under four hours. **Monitoring and Observability:** We implemented distributed tracing, centralized logging, and custom dashboards. The team received immediate notification of issues, often before customers were affected. ## Results The transformation delivered results that exceeded the original objectives across every metric. ### Performance Improvements Transaction processing time improved dramatically, from an average of 3.2 seconds to just 380 milliseconds—a 88% reduction. During peak periods, the old system would slow to 8-10 seconds per transaction; the new system maintains consistent sub-second performance regardless of load. The system now handles the same volume of transactions with 60% fewer compute resources, thanks to efficient containerization and automatic scaling. During unexpected traffic spikes, the system automatically scales out within seconds rather than requiring manual intervention. ### Business Agility The time to launch new products dropped from six months to an average of 12 days. The modular architecture means that changes to one product don't affect others, dramatically reducing testing requirements and risk. Since go-live, Finova has launched four new products that would have been impossible on the old platform, including instant international payments and AI-powered financial advisory tools. These new offerings have generated additional revenue of £2.3 million in the first year. ### Reliability and Security Uptime improved from 99.2% to 99.99%, exceeding the target. The system automatically recovers from failures—during one incident, a database failover occurred without any user-visible impact. Mean time to recovery dropped from 4 hours to 8 minutes. Security improvements included eliminating 147 known vulnerabilities in the legacy codebase. The new architecture passed both SOC 2 Type II and ISO 27001 audits on the first attempt. ### Cost Reduction Despite the investment in new infrastructure, overall operational costs decreased by 62% compared to the legacy environment. This was achieved through: - Elimination of expensive Oracle licensing (£180,000 annually) - Reduced hardware maintenance and data center costs (£120,000 annually) - Automation reducing operational staff requirements by 40% - Pay-as-you-go cloud pricing matching actual usage ## Metrics Summary | Metric | Before | After | Improvement | |--------|--------|-------|-------------| | Transaction Time | 3.2 seconds | 380 milliseconds | 88% | | Uptime | 99.2% | 99.99% | 0.79% | | Deployment Time | 2 weeks | 4 hours | 98% | | Product Launch | 6 months | 12 days | 94% | | Infrastructure Cost | £420K/year | £160K/year | 62% | | MTTR | 4 hours | 8 minutes | 97% | | Security Vulnerabilities | 147 | 0 | 100% | ## Lessons Learned This engagement offered several insights that inform our approach to similar transformations: **Start with business value, not technology.** The most successful elements of this project were those directly tied to customer-visible improvements. Beginning with account access and transaction processing—functions customers interacted with daily—demonstrated value early and built organizational support for more challenging migrations. **Phased approaches reduce risk but require patience.** While the incremental migration took longer than a big-bang deployment, it allowed the team to learn and adapt. Each phase informed improvements to subsequent phases, ultimately producing a better result than a complete replacement would have achieved. **People matter as much as technology.** The technical transformation was only possible because of the team's willingness to adopt new practices. Investing in training, pairing experienced developers with those learning new approaches, and celebrating incremental wins built the culture needed for long-term success. **Legacy systems contain valuable business logic.** Rather than discarding the old system entirely, we spent time understanding the business rules embedded within it. This knowledge prevented mistakes and ensured that the new system replicated functionality that users depended on, even when it wasn't formally documented. **Monitoring and rollback capabilities enable courage.** The ability to quickly detect problems and roll back changes gave the team confidence to move fast. Investing in observability before aggressive migration was essential—it enabled a DevOps mindset without unacceptable risk. ## Conclusion Finova Capital's transformation demonstrates that even deeply entrenched legacy systems can be successfully modernized without disrupting business operations. The key was combining modern cloud-native architecture with a pragmatic, phased approach that delivered incremental value while managing risk. Today, Finova operates a platform that supports their growth ambitions while maintaining the reliability and security that financial services require. The transformation has positioned them to compete effectively in a rapidly evolving market, with the technical foundation to innovate at pace. For organizations facing similar challenges, this case study illustrates that the journey is achievable. Success requires clear objectives, executive sponsorship, skilled implementation partners, and—most importantly—a team committed to both technical excellence and continuous improvement. --- *This case study was conducted in partnership with Webskyne's Enterprise Transformation practice. For more information about modernizing legacy systems, contact our team of experts.*