Transforming fixed income data management with AI and human expertise

With the increasing complexity of global finance, speed must be matched with accuracy and context.

Recognizing this, for some use cases, Bloomberg has adopted a “human-in-the-loop” workflow in which human intelligence augments automated systems. This approach allows subject matter experts with deep domain knowledge to supervise AI-driven data extraction, ensuring that the outputs meet the high-quality standards expected by professionals working in the financial sector. 

For Bloomberg’s fixed income data, this combination has proven particularly effective in addressing the complexities of term sheets, creating innovative solutions to ingest this data that better serve both internal teams and customers. 

The challenging role of term sheets in fixed income 

The corporate bond market encompasses a multitude of structures tailored to the needs of various investors, financing companies across the economy. Bloomberg plays a crucial role throughout the issuance process, providing yield calculators, risk metrics, trade ticketing, and reference data. Corporate bond data is vital for investors to make informed decisions on whether to purchase debt instruments from corporate entities. Given that there is no official centralized data source, Bloomberg serves as a key provider of this information to clients.  

Clients are increasingly looking to electronify and automate the new issuance process to improve efficiency and reduce operational risk. As automation advances, the timeliness of data becomes even more critical, allowing investors to make faster, more informed decisions. 

As one industry report highlights: “The top operational priority for buy-side, and many sell-side, traders in a year of record bond issuance is to streamline and electronify the primary market bond investment process.” 

Processing the term sheets that outline the basic terms and conditions of each bond has historically been a labor-intensive task, requiring analysts to manually extract key data points. However, as the volume and complexity of financial instruments have grown, this manual approach has become unsustainable. 

To address this challenge, Bloomberg’s Data team has developed AI-driven systems to automate the extraction of term sheet data. Machine learning models were created to recognize and extract crucial data fields, such as the bond’s term, interest rate, and yield—significantly improving the speed and efficiency of this process. 

Historically, training these models required a substantial number of annotated term sheets due to variations in reporting formats. Experts reviewed the model outputs, ensuring annotations were precise to prevent incorrect training data from compromising the system’s effectiveness. 

“This was a complex project,” recalls Esmie Papadimitriou, Team Leader, Fixed Income Data. “We quickly realized that we needed our best people for the annotation work. The deeper their subject matter expertise, the more efficient and effective they are. If annotations were not done well, we could have ended up with incorrect model training and months of wasted work.” 

Addressing global market variability 

One of the biggest challenges in automating term sheet processing is accounting for differences across global markets. Term sheets vary significantly in: 

1. Structure – The length, delivery method, and order of information differ by region. 
2. Language – In EMEA and APAC, term sheets are often written in local languages, requiring specialized models to interpret them accurately. 
3. Content Interpretation – Local market conventions impact how certain terms are expressed or omitted, necessitating domain expertise to ensure accurate data extraction.

Bloomberg’s approach to automation factors in these variations by combining machine learning outputs with business logic and market expertise. The system is designed to identify crucial details that are missing—such as the bond issuer, currency, or denominations—and apply appropriate derivations based on the specific market context.  

“This knowledge has been gathered over decades of working with market participants and has been coded into our automation pipelines,” explains Papadimitriou. “Our pipelines make use of AI-driven content extraction while incorporating domain-specific rules to ensure accuracy.” 

Combining AI and human expertise to drive innovation 

While AI enables the rapid processing of large volumes of term sheets, human expertise ensures that the extracted data is accurate, complete, and contextually appropriate. Bloomberg’s domain experts collaborate closely with the firm’s AI engineers to refine models and evaluate their outputs, ensuring the solutions they are used in meet the high standards expected by clients.” 

“Stronger foundation models have been a game-changer: instead of spending months building and training bespoke systems, we can now deliver high-quality results in weeks — as long as we invest in clear task definitions, robust evaluation datasets, and tight feedback loops,” shared Shefaet Rahman, Global Head of AI Services in Bloomberg’s AI Engineering group. “Our collaboration with the Data department has been critical in turning these into durable assets that scale across workflows, enabling faster innovation without sacrificing precision.” 

The partnership between Bloomberg’s Fixed Income Data team and Engineering has been instrumental in driving this innovation. “We have people who understand the technology stack and how to work with Engineering,” Papadimitriou emphasizes. 

Freeing up time while safeguarding data quality 

This approach has transformed Bloomberg’s fixed income data management. By combining the speed of AI with human oversight, the team has improved the accuracy of its data extraction pipeline while freeing up time to expand coverage into new and emerging markets, such as the Baltics and other Eastern European regions. This allows Bloomberg to anticipate customer needs and provide traders with critical data when they need it, helping them derive insights faster.  

Linn O’Connor, Global Head of Data Securities, highlights how analysts are now able to focus on higher-value tasks: “It just makes the job more interesting. The tasks are different, and it becomes much more project-based. People are able to do much more to an even higher standard of quality.” 

Looking ahead 

As Bloomberg continues refining its AI systems and enhancing its data management processes, subject matter experts remain essential in ensuring that the data provided is accurate, relevant, and aligned with clients’ needs.  

“AI and automation are powerful tools, but it’s the people behind the technology who make the difference. By working together, we can create innovative solutions that not only meet the demands of today’s market but also prepare us for the challenges of tomorrow,” Papadimitriou notes. 

 By blending cutting-edge technology with human expertise, Bloomberg isn’t just keeping up with the data-driven financial world—it is shaping its future. 

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