There is no denying the major impact that technology solutions such as AI and data are already having on the overall wealth management sector, including the fixed income space. Despite the vast size of debt markets, the fixed income sector lags behind equity trading in transparency and efficiency. But technological advancements are causing meaningful progress in these markets, too.
Fixed income solution provider BondWave is on the front lines of this evolution, both witnessing and being involved in the ongoing changes.
WSR caught up with Michael Ruvo, the firm’s CEO, to chat about the innovation and technology trends that he says are shaping the fixed income space, including AI, data analysis and the trend to make debt trading more like equity.
WSR: In an environment of rate uncertainty and market volatility, how can technology enhance transparency and liquidity, and ultimately provide better outcomes for clients?
Ruvo: Data and technology – especially advanced decision-making tools powered by enriched data sets – are critical to supporting clients during uncertain markets.
With continued advancements in AI and machine learning, firms can now analyze massive amounts of data in real time, enhancing transparency, improving decision-making and driving better outcomes for clients.
These capabilities enable advisors to quickly assess market impacts on client portfolios and deliver timely, actionable guidance that helps minimize risk while maximizing returns.
With access to data at an all-time high and the ability to process information more quickly than ever, firms will need to adopt or develop solutions that leverage both data and technology to differentiate themselves and help clients effectively navigate today’s turbulent markets.
WSR: How does the fixed income market compare with equities in terms of electronification and workflow automation? What developments signal the most meaningful progress for wealth management firms and advisors, and what’s driving that acceleration now?
Ruvo: While fixed income markets continue to significantly lag equity markets in electronification and automation, strides are being made to enhance transparency and workflow efficiency.
Equity-like trading protocols – such as portfolio trading, trading at the close and non-broker-dealer automated market makers – are gaining wider adoption and driving bid/offer spreads lower.
However, the lack of a clear, displayed best bid/offer that exists in the equity markets continues to present challenges for pre-trade price discovery and efficient execution – with roughly half of all trades still conducted over the phone.
As the equitization of fixed income markets continues, a push toward liquidity aggregation will be critical to enhancing pre-trade transparency, while also providing more efficient execution and workflows. This enhanced transparency will allow wealth management firms to better serve clients at lower costs and help them make more informed decisions around their fixed income investments.
WSR: Is data a differentiator in fixed income? Is it a challenge? What data-related obstacles most affect wealth management firms today, and how are firms beginning to overcome them?
Ruvo: Data continues to be a critical differentiator in fixed income. The data that is most important to wealth management firms includes reference, pricing, trade, ratings and yield curve data – to name a few. Since there is no public source for this data, it can be costly to purchase and requires integration into internal systems and processes.
Pre-trade data poses particular challenges due to the bifurcated nature of fixed income markets and the absence of a centralized execution venue.
Pre-trade data poses particular challenges due to the bifurcated nature of fixed income markets and the absence of a centralized execution venue.
To effectively find the right bond for their clients, firms must scour numerous electronic communication networks (ECNs) and engage directly with dealers to source the best bond at the best price. More firms are now adopting solutions that better aggregate market data and create an order management system (OMS) workflow that more closely resembles equities and packaged products.
WSR: AI is reshaping workflows across financial markets. Where do you see the most immediate and meaningful applications of AI for wealth management fixed income teams, and what barriers still need to be addressed?
Ruvo: The effective adoption of AI for wealth management firms will be critical over the next 12-18 months.
The effective adoption of AI for wealth management firms will be critical over the next 12-18 months.
While some firms remain hesitant to incorporate AI into client-facing applications, an increasing number are leveraging AI to boost operational efficiency and scale. Firms are beginning to train enterprise AI solutions against internal data assets, such as emails, proposals, research and prospectuses. These implementations provide immediate benefits across workflows, including proposal generation, market analysis and pitch book creation.
Operational tasks such as scheduling, meetings and documentation are already seeing efficiency gains from internal AI tools. As the models continue to improve, additional task automation and workflow efficiencies will become possible. A key barrier remains the ability to securely externalize these capabilities while protecting sensitive client and firm data.
Jeff Berman, Contributing Editor and Reporter at Wealth Solutions Report, can be reached at jeff.berman@wealthsolutionsreport.com.
