I recently had the opportunity to contribute to a TabbFORUM report exploring the complexities of fixed income trading.
The research offers a comprehensive view of the challenges — and opportunities — in automating this vast and often fragmented market. In this post, I share key insights from the report and highlight where the industry is making progress, where it’s falling short and what it will take to close the gap.
How can a $50 trillion market still trade like it’s the 1990s?
While fixed income volumes and opportunities continue to grow, many firms still struggle to integrate the fragmented systems, manual workflows and scattered data that come with this opportunity.
The recent TabbFORUM report, “The Hidden Costs of Fragmented Integration in Fixed-Income Trading,” examines these challenges head-on.
The report finds that while electronic trading, data aggregation and workflow tools have advanced, much of the buy side remains constrained by architectural sprawl and poor integration.
As the report puts it:
“For a $46.9 trillion market, fixed income remains shockingly analog.”
In this post, I break down the report’s key findings, explore what’s standing in the way of transformation and automation and outline what firms must do to seize emerging opportunities in today’s evolving fixed-income landscape.
Today’s Fixed Income Market: Navigating the Divide of Automated and Manual Processes
According to SIFMA’s Q2 2025 report, US fixed income markets handle more than $1 trillion in daily activity across treasuries, mortgage-backed securities, corporate bonds and other instruments. Coalition Greenwich data confirms that corporate bond average daily notional volume (ADNV) rose to $47 billion per day in 2024, marking a 21 percent increase year-over-year.
However, within this activity, the TabbFORUM report indicates that there is an automation divide.
US treasuries are the most electronically traded segment in fixed income, with most activity happening online. The publication notes that corporate bond automation is also expanding, but overall adoption is uneven: electronic trading accounts for roughly 42 percent of investment-grade activity and 31 percent of high-yield.
By contrast, many other fixed income segments — such as off-the-run treasuries, municipal bonds and emerging market debt — remain largely analog. In these instruments, liquidity is fragmented, pricing transparency is inconsistent and workflows depend heavily on human intervention.
In markets like municipals, where small, segmented accounts dominate, managers often default to buy-and-hold strategies to avoid steep bid-ask spreads.
For these less-automated markets, workflows built for a low-rate, low-volatility era are now under pressure from tighter liquidity, faster rate cycles and growing regulatory demands for execution quality and transparency.
Closing the Automation Gap in Fixed Income Trading
Against this backdrop, firms are being forced to adapt and, as noted in the TabbFORUM research, “Address a widening gap: between high-function tools and low-penetration workflows, between institutional demands and a fragmented market structure."
Interviews with buy-side and vendor leaders reveal a consistent theme: trading desks want platforms with smarter integrations. But while infrastructure investment is accelerating, it is often paired with inconsistent data standards, limited interoperability and workflow misalignments that limit scalability.
Here is a summary of the top concerns outlined in the TabbFORUM report:
The Workflow Battleground
Years of investment in trading technology haven’t yet fully solved one stubborn problem: inconsistent reference data.
Differences in how some OMSs, EMSs, portfolio management platforms and risk systems define and tag instruments create costly inefficiencies — especially for firms trying to integrate execution across desks and asset classes.
Closing this standardization gap is critical to making trading technology “invisible” and empowering traders without adding friction to their process.
Data Expansion
As electronic quoting expands, trading desks face a flood of axes, runs, pricing files and unstructured messages—especially during the pre-trade process.
The challenge isn’t just volume; it’s data quality. Pricing and risk in fixed income can swing dramatically based on the quality and consistency of the underlying data. And without clean, structured inputs, desks often model margin and risk with broad assumptions, sometimes overestimating exposure just to stay protected.
From Architecture to Execution: Making Data Usable
While better data pipelines and normalization strategies are reducing fragmentation at the architectural level, execution infrastructure hasn’t always kept pace.
Even within unified tech stacks, fragmentation persists.
A senior trading executive at a prominent buy-side firm notes that while their desk increasingly relies on pre-trade pricing streams from dealer networks, true interactivity with that data remains limited.
As a result, the data is routed into the EMS rather than the OMS—an architectural workaround that signals deeper execution-level constraints.
Today, the challenge is less about access to data than making it usable. Rising data volumes risk generating noise instead of insight without consistent structure, standardized sourcing and seamless integration.
And, in an environment of intensifying regulatory pressure and heightened best-execution scrutiny, fragmented or unusable data doesn’t just hinder performance—it directly undermines operational transparency and compliance readiness.
AI and Automation in Fixed Income
AI and automation are already tackling challenges such as pricing illiquid bonds and recalculating portfolio risk in real time. However, the adoption of these technologies is still measured, limited by fragmented workflows and the complexity of diverse asset classes.
For now, automation complements rather than replaces human judgment, especially for mandates with nuanced investment criteria. Many firms automate routine, low-value processes to free human capital for higher-value decisions.
The next leap will come from integrating AI directly into existing systems, moving beyond isolated tools toward truly transformative workflows.
Buy Side Innovations
The buy side isn’t chasing innovation for innovation’s sake. It’s focused on practical gains such as smarter infrastructure, faster data pipelines and robust APIs and analytics that plug directly into existing workflows.
More asset managers are seeking direct API access to both market and trading data. EMS adoption of APIs remains modest, but demand for targeted, workflow-friendly tools is rising.
The expectation is clear: The tools should adapt to the workflow, not the other way around. Interfaces need to be intuitive, consistent with other trader-facing systems and deliver measurable value for the space they occupy. Vendors with a deep understanding of execution logic and market structure will be best positioned to deliver.
Fixed Income: Integration Over Invention
The TabbFORUM report makes it clear that integration matters more than invention when it comes to fixed income operations.
From standardizing reference data to bridging OMS-EMS gaps, and from embedding AI into workflows to delivering APIs that fit existing processes, the winning solutions remove friction without disrupting what already works.
As volumes grow and market structures evolve, the real competitive edge won’t come from adding more tools; it will come from making the right ones work together invisibly to improve execution and sharpen decision-making.
Read the full TabbFORUM report, “The Hidden Costs of Fragmented Integration in Fixed-Income Trading,” and learn more about our fixed-income trading technology.
