Real-Time Trade Finance Reconciliation Platform for a Major European Bank

Background & Challenge

• Outdated Platform: Built on Java 6 monoliths, brittle and difficult to maintain.
• Manual, Error-Prone Processes: Operations teams spent 8+ hours daily reconciling high-volume transactions.
• Scalability Constraints: Peak trade period spikes led to backlogs and SLA breaches.
• Compliance & Audit Gaps: Fragmented manual logs risked MiFID II non-compliance.
• Integration Complexity: Required interfacing with legacy CICS systems, Kafka messaging, and central data lake under monolithic codebase.

How We Helped

Strategic Partnership & Coordination

• Embedded engineers within Luxoft's engagement team for seamless coordination with the bank's stakeholders and legacy-system experts.
• Co-led technical redesign ensuring alignment with the institution's architectural standards.

Platform Migration & Modernization

• Migrated entire codebase from Java 6 to Java 17, refactoring deprecated APIs and adopting modern language features including modules, var, and enhanced concurrency utilities.
• Decomposed monolith into targeted Spring Boot microservices: ingestion, matching, exception handling, and reporting.
• Containerized all services with Docker and orchestrated deployment on Kubernetes.

API-First Integration Architecture

• Defined OpenAPI-spec REST endpoints for all service interactions.
• Encapsulated legacy CICS calls behind secure API gateway managed by Luxoft's integration platform.
• Established clean separation between modern microservices and legacy system dependencies.

Quality-Driven Development Process

• Enforced Test-Driven Development with JUnit 5 and Mockito, achieving >85% unit-test coverage.
• Introduced Spring Cloud Contract for contract testing, validating service interfaces before deployment.
• Automated static analysis with SonarQube and security scanning with OWASP Dependency-Check in CI pipeline.

Real-Time Streaming & Exception Management

• Leveraged Apache Kafka for ingesting SWIFT MT messages and internal transaction events.
• Built intelligent rule engine for automatic match resolution with human-review dashboard for exceptions.
• Persisted comprehensive audit logs in Elasticsearch with Kibana visualization for instant compliance reporting.

Performance & Scalability Engineering

• Load-tested with Gatling, optimizing Kafka consumers to handle 10,000+ messages per minute with sub-second processing.
• Configured Kubernetes autoscaling for transparent microservice scaling during peak trade windows.

Outcomes & Benefits

• Eliminated Manual Work: Automated 8+ hour daily reconciliation process, freeing operations team for strategic tasks.
• Sub-Second Processing: Achieved 10,000+ message throughput per minute with real-time transaction matching.
• MiFID II Compliance: Comprehensive audit trails and automated reporting ensure regulatory adherence.
• Zero Downtime Scaling: Kubernetes autoscaling handles peak trade periods without SLA breaches.
• Modern Architecture: Java 17 microservices platform enables rapid feature development and maintenance.
• Operational Visibility: Real-time Kibana dashboards provide instant insight into reconciliation status and system health.

Tech Stack

• Java 17 with Spring Boot microservices
• Apache Kafka for real-time message streaming
• Docker + Kubernetes containerized deployment
• Elasticsearch + Kibana for audit logging & visualization
• React-based exception management dashboard
• JUnit 5 + Mockito for comprehensive testing
• SonarQube + OWASP security scanning
• Gatling performance testing
• OpenAPI specification for service contracts

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