AI in fixed income: from fragmentation to intelligent execution

MarketAxess is integrating artificial intelligence across pricing, liquidity discovery and execution workflows to help clients navigate fragmented fixed income markets. Combining proprietary trading data, advanced analytics and electronic trading infrastructure, the firm is helping shape the next generation of intelligent fixed income trading.

For decades, fixed income markets have been defined by fragmentation, opacity, and human intermediation. Unlike equities, where centralized order books provide continuous price discovery and relatively standardized execution, credit and emerging markets remain largely over-the-counter (OTC) ecosystems in which liquidity is dispersed, and pricing must be inferred rather than observed.

Artificial intelligence is beginning to change that structure, not by removing complexity, but by making it legible.

The irony is that fixed income’s historical inefficiencies are precisely what makes it such fertile ground for machine learning. Where prices are not directly observable, liquidity is fragmented across dealers, and the same instrument may trade at different levels depending on size or counterparty, AI has a structural advantage: it can synthesize partial signals into coherent estimates of market reality.

This article explores how:

• AI is moving from experimentation to infrastructure in fixed income: adoption is broad and increasingly embedded across workflows, not confined to isolated pilots.

• The most valuable current applications are pre‑trade pricing intelligence, liquidity sourcing and targeting, and execution—areas where fixed income’s fragmentation makes human-only interpretation difficult at scale.

• The differentiator is in high-quality, permissioned data and workflow integration, plus clear human control and governance. Outputs are explainable, controllable, and operational in real trading decisions.

From inference to algorithmic pricing intelligence

Nearly a decade ago, machine learning began to play a foundational role in early pricing models. These models demonstrated that fragmented OTC data could be transformed into usable signals, enabling better price discovery, more efficient execution, and more sophisticated liquidity provision strategies. Over time, this capability has evolved into more advanced infrastructure, including AI-driven pricing engines like CP+™ that combine public trade prints, proprietary transaction data, dealer responses to inquiries, and even non-executed “intent” signals from across the trading network.

A critical insight underpinning these systems is that information does not only reside in trades that happen, but also in trades that do not. Dealer responses, quoted levels, and patterns of engagement all contribute to a richer representation of market conditions. Machine learning models, when properly calibrated, can extract meaning from this broader informational layer, improving both transaction cost analysis and forward-looking price formation.

AI at the core of the trading workflow

At the same time, AI is increasingly being embedded not only in pricing but also directly into the trading workflow itself. One of the most significant shifts is occurring at the point of counterparty selection. Rather than relying solely on intuition, relationships, or static analytics, tools such as AI Select can evaluate dozens of dynamic variables to recommend the liquidity providers most likely to deliver a strong outcome. These include historical win rates, execution quality, responsiveness, and time-varying behavioral patterns. Models like Tradability use machine learning to estimate likely market depth at the bond, side, and size level, helping traders better anticipate liquidity conditions before initiating a request-for-quote.

Used alongside dealer axes, these systems help traders identify where liquidity is both most credible and most actionable, particularly in situations where a counterparty is simultaneously signaling interest and algorithmically surfaced as a strong execution partner.

This evolution is accompanied by new protocols designed to improve pre-trade information flow while reducing signaling risk. Time-limited “live axe” mechanisms, for example, allow dealers to publish actionable liquidity indications with greater precision and control, enhancing transparency while helping manage signaling risk.

The next stage will likely be defined by natural language interfaces that are deeply grounded in permissioned market data and embedded directly into trading workflows. Their value would move beyond simple conversational access to the ability to ask domain-specific questions—about recent prints, liquidity conditions, counterparty behavior, or execution strategies—and receive answers that are traceable, contextual, and actionable.

Collapsing the boundary between “what” and “how” to trade

The use of AI in fixed income is creating a deeper structural change in how decisions are made. Historically, fixed income workflows separated “what to trade” from “how to trade it,” with portfolio construction and execution treated as distinct problems. AI is beginning to narrow that separation. Investment intent and execution strategy are being considered together, with liquidity conditions, execution constraints, and counterparty behavior feeding back into portfolio design rather than being treated only as downstream execution considerations.

This is particularly relevant in portfolio trading, where execution quality is highly sensitive to size and composition. While portfolio trading can deliver strong outcomes, execution quality can become more sensitive as portfolio size, complexity, or liquidity dispersion increases. AI-enabled optimization allows these larger portfolios to be restructured into smaller, more digestible components, improving execution efficiency by aligning each basket with the most appropriate liquidity providers.

The block trade frontier

As more routine flow becomes automated and electronically intermediated, trader attention and balance sheet engagement can be focused more deliberately on complex transactions. In that sense, efficiency in smaller trades can support better handling of larger block trades, which remain among the least electronified segments of fixed income markets.

Even here, however, AI is beginning to play a role. Rather than attempting full automation, its impact is emerging through better counterparty targeting, improved request-for-quote distribution, and enhanced understanding of liquidity availability. By ensuring that large trades are directed only toward the most relevant counterparties, market impact can be reduced while execution quality improves.

Agentic AI and the question of control

Across all of these developments, a consistent distinction emerges between traditional algorithmic trading systems and newer agentic AI approaches. Conventional execution algorithms are typically rule-based, deterministic, and extensively backtested prior to deployment. By contrast, agentic systems are more adaptive: they can reason across broader datasets, respond to changing market context, and support more dynamic decision workflows. Therefore, an immediate opportunity for agentic AI is likely upstream of live execution, in areas such as data analysis and signal generation.

This flexibility, however, introduces new requirements. Unlike static models, adaptive systems cannot rely solely on historical validation. They require continuous oversight, rigorous governance frameworks, and, critically, a human-in-the-loop approach to ensure that flexibility does not compromise control.

From experimentation to infrastructure

As fixed income markets continue the transition towards the adoption of AI across trading workflows, the relationship between platform providers and clients is evolving. Market participants are building their own AI tools and combining them with external data and execution capabilities. The result is a hybrid model in which proprietary client systems and platform infrastructure interact to produce more sophisticated decision-making environments. Value is no longer concentrated in a single layer, but at the intersection of internal and external intelligence.

What becomes clear across all these developments is that AI is not simply accelerating existing processes. It is changing the structure of how fixed income markets process information. The traditional separation between data, decision, and execution is being replaced by a more continuous system in which information is constantly interpreted, refined, and acted upon.

As AI becomes more deeply embedded in market structure, competitive advantage will belong to firms that can translate complex market activity into scalable trading insight.

At MarketAxess, we are leading that evolution. The use of AI is embedded across our trading and workflow infrastructure, enabling clients to navigate fixed income markets with greater precision, efficiency and confidence. By combining one of the industry’s deepest pools of proprietary trading and workflow data with advanced analytics and electronic trading capabilities, we are transforming the future of fixed income trading.