The CFO sees "save 3 percent on transactions" and the math looks obvious. Treasury sees continuous settlement at 3 AM and a reconciliation process built for batch. Before you switch to account-to-account, count the operating cost the savings slide leaves out.
Payments runs on documents - checks, wires, invoices, KYC files - and the OCR that reads them chokes on handwriting and damage. DeepSeek-OCR processes a page as roughly 100 visual tokens instead of a thousand text tokens, and it is open source. The headline accuracy comes with a compression ceiling worth knowing.
Train GPT-2 on Shakespeare and ask it the capital of France - it forgets Paris. That is catastrophic forgetting, one of several failure modes that surface the moment fine-tuning meets production, especially in regulated banking where "the model flagged it" is not an answer.
The vendor demo makes fine-tuning look like installing software - upload data, train, deploy. The real distribution of outcomes is brutal, most of the work is data preparation, and the model you trained for wire fraud can quietly forget how to read ACH. For financial services, guessing is not an option.
Most large models keep every parameter lit for every token, like a city that never turns the lights off. SpikingBrain borrows the brain's trick - fire only the neurons that matter - and reports order-of-magnitude efficiency on Chinese MetaX chips. The headline numbers are real, with conditions worth reading.
A clean virtual-account hierarchy looks simple until an HCM client asks you to reverse payroll for 200 employees across three states from three weeks ago. Multi-entity payroll is where account architecture meets 50 sets of wage law - and where instant rails do not yet fit.