Despite technological leaps in equity portfolio analytics, fixed income remains behind the curve. According to Chris Smith-Hill, product lead at technology solutions provider Confluence, the reasons go beyond technology: they cut into market structure and ingrained habits.
“Data and culture,” he says when asked why fixed income still lags equities on the technology front. “Holdings data required for proper analysis just isn’t maintained or distributed in the way the market needs it to be. And from that, a culture of opacity has grown that stands in contrast to the transparency common in equity analysis.”
The transparency gap
The friction lies in how data is shared—especially for over-the-counter (OTC) instruments. While many asset managers have systems that monitor OTC exposure internally, the problem is enabling the end investor to do the same.
“Investors expect to assess risk and performance independently. Yet many asset managers only provide partial visibility, especially in commingled funds. ETFs, which disclose holdings daily, have been attracting significant inflows,” Smith-Hill highlights.
Lack of standardisation in OTC data makes it difficult for investors to validate holdings or conduct meaningful bottom-up analysis, particularly when building fund-of-fund portfolios. That, Smith-Hill argues, is not just a data problem—it’s a commercial one. “Transparency isn’t only a regulatory concern anymore. It’s a competitive advantage,” he adds.
Colin Vidal, head of fund representation solutions (asset services) at private bank Reyl Intesa Sanpaolo, echoes this concern, pointing out that fixed income’s complexity—fragmented markets, OTC trading and varied structures—makes data harder to collect and standardise than in equities. Vidal says: “The diversity of instruments and lack of centralised trading make meaningful analytics far more difficult to achieve at scale.”
Fund managers may struggle with portfolio construction and risk monitoring, while selectors and regulators face barriers to due diligence and oversight. “It limits transparency, which affects decision-making and investor confidence,” Vidal adds.
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Tech gaps and compliance risks
Trading data from TradeSmart supports this shift, showing double-digit growth in electronic fixed income trading volumes and a sharp rise in automation and new protocol usage.
The way data moves within an asset manager’s infrastructure is a basic part of the issue. An OTC trade typically starts in a front-office system where its terms and conditions (maturity dates, counterparties, notional legs) are recorded. However, this granular data doesn’t always travel downstream to the accounting system, which may only log a single-line entry for net asset value calculations.
The result is a disconnect. Risk systems may have access to enriched trade data, but the investor-facing holdings files, usually built from accounting records, don’t. This limits the ability of consultants or platforms to conduct independent risk analysis.
“Compliance teams should care about this,” explains Smith-Hill. “Because fixing the problem not only strengthens regulatory alignment but also reduces reputational risk.”
Culture and AI
With AI and data science’s growing influence on the asset management industry, why hasn’t fixed income seen more progress? Again, Smith-Hill brings the answer back to basics: data and culture. “The data required to price OTCs isn’t typically part of an investor report. We’re using AI in places to bridge that, but the bigger obstacle is cultural,” he says.
Some firms may be hesitant to reveal too much, fearing that transparency could expose proprietary strategies or invite competition. “AI can’t solve that,” he admits. “But we’re working to help asset managers get comfortable with secure, appropriately delayed fixed income transparency.”
Smith-Hill sees regulators playing a key role in nudging the industry toward more modern, tech-enabled practices. “Regulators are the ultimate drivers of transparency. If the market doesn’t move, they’ll eventually step in,” he says.
The Global Investment Performance Standards — a voluntary framework for consistent reporting — is a successful example of industry-led self-regulation.
Tools for investor-led analysis
Initiatives like fairCT, a consolidated tape project for fixed income coordinated by fixed income technology provider Ediphy, are also helping move the needle. While focused on post-trade data at the market level, they support broader calls for greater standardisation and visibility in bond markets.
The project brings together a range of market participants in an effort to standardise and consolidate data. FairCT aims to create a single, official record of trades that bridges the gap between delayed, fragmented free data and costly proprietary feeds.
Bond trading in the UK is currently fragmented, with liquidity spread across numerous trading venues and various lit and dark trading protocols.
This supports the Financial Conduct Authority’s (FCA) goal of lowering data access costs and enhancing investor participation. “We aim to return any economic value generated over costs and reasonable returns back to the users of the tape,” said Chris Murphy, CEO of Ediphy. The model reflects collaboration across the market and fits with the FCA’s Consumer Duty framework, Murphy adds.
At Confluence, the team is trying to tackle the problem differently. The investor-centric approach, Smith-Hill explains, is focused on enabling holdings-based fixed income analysis that goes beyond internal risk views.
“We’re going the extra mile to collect the data needed, and we’re feeding it into its in-house performance and risk system,” he says.
One innovation has been the extension of its equity analytics tool to cover fixed income exposures.
Confluence’s tools are being designed not just for portfolio managers but also for the end investor. “Some institutional investors and wealth managers know exactly what that means in terms of data and analytics. Others may not—or they’ve tried and given up because it’s too complicated or manual,” Smith-Hill says.
Ultimately, fixed income’s future rests on better data, a cultural shift and smarter tools.
