Solution
For data and analytics teams at ratings agencies, banks, and research firms: statement line items mapped to one taxonomy at model-grade quality, across millions of pages.
The problem
The same economic line item appears under wildly different descriptions from bank to bank — and all of it must map to one taxonomy.
More than a hundred trained analysts spent their days mapping line items manually, on output that feeds live financial models.
After extraction, values still need normalization and transformation before any downstream model can use them.
The product, not a promise
How it works
Annual reports load in bulk — every reporting format and style, 10+ languages.
Pre-trained table and financial statement models read the tables and the notes.
Line items map to one custom taxonomy despite wide variation in wording.
Values are normalized and transformed into the shape downstream models need.
The client's SMEs correct results point-and-click; accuracy climbs with every fix.
Who it's for
Financial data SME
Data & analytics lead
Model risk / audit
A large American financial services and rating agency had to process roughly 200,000 annual reports from banks — around 2 million pages, in more than 10 languages, across a spread of reporting formats and styles. The core difficulty was reconciling the pages, beyond merely reading them: the same economic line item appears under wildly different descriptions from bank to bank, and all of it had to map to a single taxonomy. After extraction, values still needed normalization and transformation before use. More than 100 trained analysts were doing this tagging manually, and quality could not slip, because the output feeds financial models the business depends on.
Botminds started from its pre-trained table understanding and financial statement understanding models, which is why the first working version of the solution shipped within days rather than months. The platform reads the statements and the notes, maps line items to the client’s taxonomy despite the variation in wording, and applies the normalizations and transformations downstream systems expect.
Just as important, the client’s own subject matter experts were trained on the platform and used its point-and-click interfaces from day one to correct results — so accuracy improved continuously under the supervision of the people who know the data best. Custom upstream and downstream integrations tied the platform into the client’s existing workflow instead of forcing a new one.
When extracted financials feed live models, a silent error propagates. Every mapped value stays traceable to its source line in the original report, corrections come from named experts rather than anonymous retraining, and quality is measured continuously rather than assumed. That is what makes machine-speed spreading — 5× faster than manual — safe to build a ratings business on.
Objections, answered
Every mapped value stays traceable to its source line in the original report, ambiguous mappings route to expert review instead of guessing, and quality is measured continuously in production. A silent error propagates, so the design assumes verification rather than trust.
Yes — line items map to your custom taxonomy despite the variation in wording between banks, and your normalizations and transformations apply before anything reaches downstream systems. The output lands in the exact shape your models already consume.
Your own subject matter experts. They correct results through point-and-click interfaces from day one, every correction is attributed to a named expert, and the models improve under the supervision of the people who know the data best.
The solution starts from pre-trained table understanding and financial statement understanding models, which is why the first working version for this client shipped within days rather than months. Your documents and taxonomy tune it from there.
Watch line items map to your taxonomy — notes included, values normalized, every mapping traceable to the source line.
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