Deals that are exceptional enough get assigned to a risk officer that deals with it as a case (sidenote: they use a lott of selfmade Excel, VB and low-code tools as they never get IT priority for these cases). There is not enough uniformity as well as a decreased tolerance for inaccuracy in those to warrant extensive automation.
But still, the one building the LLM-integration is 4x as expensive as the one manually entering the data. It's all about TOC, scale and risk perception. I also love that "risk people" (in the banking context, I'd say: model people) think their data quality should be exceptional and then use end user computing MacGyver style models. Spit and popsicle sticks.
The choice they have is submit a formal request to IT, be rejected 95% of the time with the remaing 5% being put in the planning with an eta 2-5 years in the future, or, DIY it with tools at hand. In an ideal world this would not be needed, in reality it is DIY or nothing.
Our customers are asking for integration with a lot of their systems (say HR / patrolling), but never ever offer to hook up their accounting system. If we want financial data, we either get a PDF with their audited financial statement or in exceptional cases a custom audited statement (you know, the one where a print of a part of the ledger gets a signature from the CPA for a not insignificant bill).
So I am enthusiastic from a data science point of view. Financial data processing of customer data is / was scarce since limited to what was feasible to manually process. That is nearly in the past.