We Tested Whether 50,000 Broken Bond Records Could Actually Be Fixed. Here's What Happened.

01 / The cabinet

The data that nobody wanted to open

Somewhere inside a global fixed income data platform sits a filing cabinet nobody wants to open. The instinct is the same, whether it is a drawer or a database.

Fifty thousand historical bond deals built up over years, each one theoretically holding everything any research analyst needs: pricing, guidance, coupon dates, issuer identity, deal structure and another 100+ data points for each bond issue. In practice, holding less than half of that.

We didn't go looking for this problem. Our client brought it to us the way most data problems arrive. They weren't asking "why is our data incomplete", somewhere they already knew that. What they wanted to understand was why their downstream analytics and endpoint users kept producing numbers that didn't quite hold up, and why "let me check the source document" had become a permanent fixture of daily operations.

Pull that thread far enough and you land in the same place every time: missing data points.

Not catastrophic gaps. Just enough missing, in enough places, that nothing could be fully trusted.

02 / The assumption

The assumption everyone had made peace with

Here's the uncomfortable part. Everyone already knew this. Data quality problems in fixed income aren't a secret; they're closer to a shared inheritance, something every desk quietly works around. What was missing wasn't awareness. It was a repeatable, provable way to actually close the gaps instead of just living beside them. New deals kept getting processed under the same old logic: publish fast, backfill later, "later" never quite arrives.

So, we built a hypothesis, and it was a plain one. We believed the gaps were fixable, not through more headcount thrown at spreadsheets, but through a structured, benchmarked approach towards standardizing and enriching the underlying issuer and bank master data.

And we believed in something a little more provocative: that speed and completeness didn't actually have to trade off against each other. That was the assumption everyone in the business had made peace with. We weren't sure it was true.

03 / The method

Picking up a scalpel instead of a soapbox

To begin with, we ran a proof of concept across 5% of historical bond deals, scored against 52 priority fields, approximately 130,000 individual field instances, each one checked, sourced, and either confirmed or filled. This sat inside a wider mandate: standardizing and enriching issuer and bank master data across a universe of roughly 20,000 issuer records and 5,000 bank records, pulling first from the client's own backup repositories, and only reaching for public sources when the internal well ran dry.

04 / Where AI helped

Where AI did the heavy lifting and where it didn't

This is the part we'd normally undersell, because "we used AI" has become a sentence that means almost nothing on its own. So, here's the specific version.

The bulk of the field instances didn't get filled by someone manually retyping numbers off a PDF. We built a machine-learning-assisted extraction layer that read through the indenture documents, term sheets, and backup files, and proposed values for missing fields, including pricing benchmarks, coupon dates and deal structure attributes, along with a confidence score for each proposal. High-confidence matches flowed straight through. Anything below the threshold got queued for an analyst to look at, rather than silently accepted or silently dropped.

We also leaned on it for entity resolution, matching an issuer or bank name on a fifteen-year-old deal record to its current legal entity, through however many mergers, rebrands, or spin-offs sit in between. That's a genuinely hard matching problem, and a language model comparing names, addresses, and historical aliases got us most of the way there, considerably faster than a manual lookup ever could have.

05 / What broke

The part that didn't work

It did not sail smoothly the first time through. Our early sourcing logic, model included, implied that if an issuer's name had changed through a merger, a rebrand, or a legal restructuring, public records and the model's own matching would reflect the current entity cleanly enough to cross-reference against the historical deal. They didn't, not consistently.

Legacy issuer names on older deals sometimes matched three different corporate descendants depending on which database or which pass through the model you asked, and more than once we enriched a field with confidence, only to find it pointed to the wrong lineage entirely.

We had to slow down, tighten the confidence threshold and the model logic considerably, and build a genuine human-in-the-loop layer around historical name changes and such corporate actions. We accepted that some percentage of "enrichment" was always going to be closer to careful detective work than automation, however good the underlying model was. It cost us time we hadn't budgeted for.

06 / The results

The results that surprised us

What we found on the other side of this exercise surprised us more than we expected.

Overall data availability across the priority field set moved from 45.78% to 88.91%, essentially doubling completeness through a single structured pass. That alone would have been a reasonable result. What actually surprised us was the shape of the improvement. Identifiers, dates and deal structure, and cashflow-related fields, the fields that most directly drive analytical and legal reliability, didn't just improve, they hit full completeness. Pricing and guidance, historically one of the messiest categories in any bond dataset, went from under 16% availability to over 90%.

The remaining gaps weren't scattered randomly across the dataset the way we'd assumed incomplete data always is. They were concentrated, almost entirely, in syndication-related fields and traceable to a specific, known source: information that lives in email backups rather than structured systems, exactly the kind of task a language model is well suited to mine next.

07 / The lesson

The real lesson wasn't about bond data

The lesson we took away wasn't really about bond data specifically. It was about the false trade-off itself. Completeness and speed only compete with each other when we treat every field the same way.

The moment we separate time-critical fields from enrichment-driven ones and let AI models carry the volume while analysts make the judgment calls where necessary, we can capture the former in real time and enrich the latter without ever slowing down live processing. That single distinction is what turns a one-time cleanup exercise into a dependable operating model.

Moreover, every financial institution sitting on historical bond deals has potentially a tidy percent of data efficiency waiting to be unlocked. Every dataset carries its own version of the legacy problems, its own inherited mess. But the instinct to treat incomplete historical data as a permanent cost of doing business, rather than to resolve it with the right mix of AI and judgment, is one we'd push back on hard.

Here's the part every fixed income data team should sit with. The gap in your historical data is very likely smaller, more specific, and more solvable than you've been assuming, and the tools to close it are more capable than they were even two years ago with the advent of AI.