For decades, banks and financial institutions built their data infrastructure around one assumption: that the information they needed was already structured. Transactions lived in databases, client records lived in CRMs, and risk metrics lived in spreadsheets. The infrastructure was designed to move that structured data around efficiently, and for the most part, it worked.
The problem is that most of the information actually driving decisions in financial services has never been structured. Loan agreements, credit memos, earnings releases, regulatory filings, third-party research, due diligence packages, and insurance contracts contain the substance of financial work. Historically, they have always been processed the same way: manually, slowly, and inconsistently by human analysts.
Banks tolerated this inefficiency for years because there was no better option. Legacy OCR technology could digitize text, but it could not understand context, extract meaning, or normalize output across different document formats. The gap between "digitized" and "usable" remained wide. Filling it required armies of junior analysts doing work that was expensive, error-prone, and difficult to scale.
Large language models have changed the calculus, and the biggest banks have noticed. The earliest serious document intelligence deployments in financial services focused on exactly the kind of high-volume, high-stakes work where automation has the most to offer. This includes extracting structured data from commercial loan agreements, parsing hundreds of pages of regulatory filings, and compressing weeks of compliance analysis into hours. These are real results from real deployments, and they point clearly at the category of work where AI creates durable value for financial institutions.
But look more carefully, and a less encouraging pattern emerges.
The investments most banks are making right now are in employee-facing productivity tools like document summarization, drafting assistants, and internal search. These sit on top of whatever document infrastructure already exists. This approach generates early wins and is a defensible way to build internal confidence in the technology, but it means the outputs are only as good as the extraction layer feeding the models. For most institutions, that extraction layer is still largely legacy. Even the most technically sophisticated banks on Wall Street have publicly acknowledged that having data in the cloud is not the same as having it modernized and usable for AI. If the largest institutions are still navigating that gap, the picture at most regional and mid-market banks is considerably less advanced.
This matters because LLMs are extraordinarily sensitive to the quality of the data they receive. A model given clean, well-structured, accurate input will perform dramatically better than the same model given noisy, inconsistently formatted, or inaccurately extracted text. When the input is financial documents, the stakes of that performance gap are incredibly high.
A hallucination in a customer service chatbot is an annoyance, but a hallucination in a credit risk model or a regulatory compliance workflow is a material liability. The productivity tools banks are deploying today are largely insulated from this problem because the consequences of a bad summary are limited. The higher-value use cases of credit decisioning, regulatory analysis, risk surveillance, and due diligence automation are not. These are precisely the workflows where the document infrastructure gap will become impossible to ignore.
The institutions moving fastest toward those higher-value deployments are the ones treating document extraction not as a preprocessing step, but as a foundational layer of their AI infrastructure. Getting structured, accurate, LLM-ready data out of complex financial documents like loan tapes, offering memoranda, SEC filings, and analyst reports is not a solved problem. The vendors that can deliver on it reliably are becoming essential infrastructure partners rather than peripheral utilities.
Most banks are not there yet. The productivity tool wave currently sweeping the industry is real and valuable, but it is also a way of deferring a harder problem. The institutions that recognize that deferral now and invest accordingly will find themselves with a meaningful structural advantage when the next generation of AI use cases demands something better underneath.
