Challenge
This client set out to automate derivative operations and manage risk in real time across a complex book spanning credit total return swaps (TRS), equity TRS, equity TRS baskets, and credit forwards. The firm’s treasury operations supported a sophisticated trade lifecycle, from booking, settlement, and counterparty confirmation to valuation and reporting, yet the underlying data infrastructure had not kept pace with the complexity and evolution of the business.
Daily processes were almost entirely manual. Analysts spent significant time stitching together data from disconnected data sources, including derivatives systems, pricing feeds, and Excel-based workflows. With no automated process to ingest and normalize data, daily reporting of mark to market valuations and counterparty exposure could not be automated.
The absence of a structured data foundation further limited the firm’s ability to respond to new reporting needs. When business stakeholders requested new reports or analytics, the lack of a unified data model meant requests had to be routed to engineers, often taking weeks or months to fulfill, and requiring custom development work each time, rather than leveraging a consistent, reusable data layer. Business users had no ability to create or modify reports themselves and were dependent on technical resources for straightforward data requests.
Leadership recognized that modernizing treasury operations would have to go beyond workflow fixes to include a strong data foundation that could automate daily data operations, centralize derivative and pricing data across systems, and create the infrastructure necessary to support more artificial intelligence and other advanced analytics.
Solution
The firm selected Rivvit, a cloud-native data management and analytics platform purpose-built for managing investment data. Rivvit was configured specifically for the firm’s derivative book, with Phase 1 focused on total return swaps and credit forwards.
The Rivvit team delivered a tailored implementation that included:
- Building a dedicated cloud environment on Google Cloud, Microsoft Azure, and Snowflake, establishing a structured ingest-to-reporting pipeline that automated the daily processing of derivative, pricing, securities, and reference data.
- Mapping data from the firm’s derivatives and pricing systems into Rivvit’s investment data warehouse, with full lineage, transformation rules, and field definitions captured in the data catalog, providing detailed traceability and governance across the entire data lifecycle.
- Configuring custom dashboards and reports in Rivvit’s Insights Library, including risk margin and mark to market dashboards that provide daily visibility into TRS financing costs, mark to market on underlying reference securities, and margin exposure by counterparty and product type.
- Configuring Rivvit’s Workflow Manager to automate and monitor daily data schedules and event tracking, alerting the team to processing exceptions before they could impact downstream reporting.
- The Document Library was deployed to support centralized management and natural language analysis of ISDA agreements, Credit Support Annexes, and other counterparty-related documentation.
- Critically, the data foundation was designed with self-service in mind. Rivvit’s View Manager provides business users with the ability to create and run their own queries against a governed semantic data layer, without writing code or engaging engineering resources. By abstracting the complexity of the underlying data warehouse into business-friendly views, View Manager puts reporting capability directly in the hands of business users.
Outcomes
Rivvit delivered the fully configured platform on time and within budget, completing implementation in under eight weeks from contract execution, exactly in line with the timeline committed at the outset of the engagement.
Results include:
- Processes that previously didn’t exist in any automated form now run daily. What had been a manual, error-prone exercise of assembling data off-system across disconnected sources is now a governed, automated pipeline that delivers clean, reconciled derivative data to business users each morning, without manual intervention.
- Report development timelines collapsed from weeks or months to days. With a robust, well-structured data foundation in place, new reports and analytics can be built rapidly against a single source of truth, eliminating the custom data wrangling that had previously been required before any analysis could begin.
- Business users can now create their own reports without relying on engineers. With Rivvit’s View Builder, analysts and operations staff can query the semantic data layer directly to build and refine their own reports on demand. What previously required a formal request and weeks of development time can now be accomplished independently, in minutes.
- The firm is now positioned for more advanced analytics and artificial intelligence. The data architecture established in Phase 1, including the semantic layer, data catalog, and Snowflake-native infrastructure, provides the foundation necessary to layer in AI-powered analysis, data quality automation, and expanded instrument coverage in future phases.
With Phase 1 complete and the core data infrastructure live, the firm and Rivvit are well-positioned to expand coverage to additional instrument types, including commercial paper and repos, and to unlock more advanced capabilities, including automated data quality detection, exception management, and AI-powered analytics that leverage the semantic layer already in place. The foundation built in Phase 1 ensures that each new capability can be delivered faster and with greater confidence than would have been possible before.