Data Before AI: Building the 2030 Fixed Income Trading Desk
Fixed income leaders have high expectations of AI. But they are clear that the data it runs on has to come first.
That is the central finding on technology from The FILS Europe Playbook, for which Fixed Income Leaders Insights surveyed 100 senior fixed income professionals from asset managers, hedge funds and private banks across Europe.
Data capabilities top the 2030 agenda
A third of leaders say better data integration is the single most important capability for the trading desk of 2030. Add reference data, lifecycle data and traceability, and data capabilities make up 63% of answers. Only 4% choose AI-enabled workflows.
This is not a rejection of AI but a question of order. AI can help connect data from different sources, but it cannot fix the data itself, so the foundations need to be put in place first.
Where AI will add value
Leaders expect AI to deliver the greatest value in:
- Risk management (47%)
- Liquidity discovery (43%)
- Execution optimisation (41%)
- Pre-trade analytics (40%)
Interestingly, only 4% name risk management as their top strategic priority over the next two years. This suggests firms want to improve their risk management practices through AI, rather than as a separate project. Workflow automation, often described as AI’s easy win, ranks well behind at 26%.
The data barrier
When asked about the biggest obstacles to wider AI adoption, leaders point to:
- Poor data quality (50%)
- Difficulty proving ROI (42%)
- Fragmented data sources (41%)
The second barrier follows from the first. Without good data, it is hard to measure what AI actually delivers. Budget constraints, cited by just 14%, are not holding firms back, which the report sees as a sign of the industry’s continued commitment to AI.
“Firms cannot expect reliable, explainable outcomes from models trained on fragmented or inconsistent information.”
Swati Bhatia, Head of Fixed Income, SIX
Not everyone frames the problem in quite the same way.
“It’s not so much poor data quality, as poor data architecture and infrastructure. In fixed income, the amount of data we need to store is humongous.”
Ines de Tremiolles, Global Head of Trading, BNP Paribas
Building the foundations
Turning expectations for AI into measurable value will require progress in the data environment that surrounds it. That work is already emerging as a strategic priority. According to the report, trusted data, stronger integration and more connected infrastructure will provide the base from which AI can move deeper into everyday fixed income decision-making.
The FILS Europe Playbook explores data and digital market infrastructure in more depth, alongside liquidity, market structure and portfolio strategy.