Building a Financial Matching Engine

  • 9/4/2013
  • One-minute read

Led the design and delivery of a real-time matching engine that consolidated cash-flow events to support liquidity forecasting and Basel III reporting.

Responsibilities

  • Captured requirements from treasury, liquidity risk, and regulatory reporting teams.
  • Designed the ingestion pipeline to normalise trades from multiple booking systems.
  • Implemented deterministic matching logic with unit and behaviour-driven tests (JUnit, TestNG, Cucumber).
  • Tuned Oracle Coherence caches to keep percentile response times under 10 ms during peak hours.

Technology Stack

  • Languages & Frameworks: Java, Spring Core, Spring Data, Spring Boot microservices
  • Messaging & Caching: IBM MQ, Oracle Coherence
  • Tooling: Maven, TeamCity CI, Jira for workflow tracking

Outcomes

  • Enabled treasurers to generate intraday liquidity coverage ratio (LCR) projections with <30-second latency.
  • Reduced reconciliation breaks by enforcing consistent trade identifiers across upstream systems.
  • Delivered auditable change history with pairing between user stories, code commits, and automated test runs.

Compliance Reminder

Liquidity risk tooling sits under stringent regulatory oversight (Basel III, PRA liquidity rules). Engage compliance, model validation, and internal audit before deploying enhancements to production.

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