Business Goal
Optimizing bond trading with AI.
Challenge
Bond pricing is a real, long-standing challenge for traders. Making profitable trades hinges on being able to accurately calculate bond price movements, which can be achieved by utilizing AI models as part of the decisioning process. However, for traders to adopt AI models, it was imperative that all models were explainable at each moment to traders and business executives.
Solution
Royal Bank of Canada implemented a comprehensive ModelOps capability to support its municipal bond trading operations by providing transparent, interpretable, and easily accessible information that allows traders to make confident recommendations.
With this successful pilot, the team embarked on an AI operationalization strategy to provide consistent AI model deployment, scoring, governance, and monitoring across the enterprise.
Benefits
- Multi-million dollar benefits for muni-bond trading
- RBC Capital Markets team has a substantial competitive advantage for this fixed income asset trading
- 50% time to market reduction
The team architected an AI-orchestration platform using the ModelOps platform as the core technology, integrating with model development tools, model frameworks, containerization services, security systems, service desk applications, and DevOps tools.
Read the entire case study in “ModelOps for Dummies.”
