The proof-of-cash is one of the few procedures in the transaction accountant's toolkit that has not changed meaningfully in decades.
The concept is straightforward: Every dollar that appears in the general ledger should be traceable to a corresponding bank transaction, and vice versa. In practice, executing that reconciliation across multiple bank accounts, multiple periods and a general ledger that may or may not have been maintained with care is among the more tedious exercises in mergers and acquisitions due diligence. It is also, consistently, among the more revealing ones.
For lower-middle-market transactions — companies in the $5 million to $100 million enterprise value range — the proof-of-cash is often the first place material misrepresentations surface. It is also, for those same engagements, frequently where the most person-hours are consumed before a single analytical judgment is made. An analyst who spends the first two days importing bank statement PDFs, mapping account numbers and cross-referencing disbursement records is not exercising professional skepticism. They are doing data plumbing.
This is the context in which artificial intelligence tools are beginning to appear in transaction accounting workflows. It is worth being precise about what these tools do and do not do, because the marketing around them tends to blur the distinction in ways that are unhelpful to practitioners on both ends — those who expect too much, and those who have written the tools off without looking closely.
What these tools actually do
The current generation of AI tools applied to proof-of-cash work are primarily document understanding and data classification systems. They ingest bank statement PDFs across multiple institutions and periods, extract transaction-level data, and reconcile it against the general ledger — surfacing exceptions rather than requiring line-by-line manual comparison. A related set of capabilities handles the trial balance side: classifying nonstandard chart of accounts entries into GAAP-structured categories and generating normalized income statements and balance sheets from raw data.
None of this is magic. It is pattern recognition applied to financial documents at a scale and speed that was not previously practical. The underlying technology has improved considerably in recent years, driven by large language models trained on diverse financial document sets. But the output is not analysis. It is structured data, organized in a way that makes analysis faster.
CPAs evaluating these tools sometimes expect the AI to catch fraud or identify adjustments that require contextual knowledge of the business. When it does not, they conclude the tool failed. But that is not what the tool is for. The appropriate test is whether it correctly reconciles what can be reconciled, correctly flags what cannot, and does so with enough transparency that a reviewer can understand and verify its work.
On that narrower test, the better tools in this space are performing well. Models trained on large, diverse sets of real financial documents — including the messy, inconsistent books typical of owner-operated lower-middle-market companies — handle variability that would have defeated earlier automated approaches. The challenge for practitioners is that the marketing around these tools rarely distinguishes between systems trained on clean, standardized data and those built for the kind of books that actually show up in lower-middle-market diligence.
What to ask before you trust the output
When evaluating an AI tool for proof-of-cash work, a few questions cut through the noise. First, what is the exception rate, and how are unmatched items surfaced? Any competent system should produce not just a reconciliation but a clear accounting of what it could not match, categorized by type and flagged for human review. A tool that presents a clean reconciliation without a transparent exception report is, by definition, not telling you everything.
Second, what was the training data? The specifics matter more than the marketing language. A model trained on large, diverse sets of actual financial documents — across industries, entity types and accounting software platforms — handles lower-middle-market variability differently than one trained on standardized or synthetic data. This is not a detail you can usually find on a vendor's website, but it is worth pressing on in any serious evaluation.
Third, what does the human review step look like in practice? The appropriate role of AI in this workflow is to compress the time between data receipt and substantive review, not to replace the review. Firms using these tools effectively are spending more of their engagement hours on judgment-intensive work — not fewer hours overall. The technology changes what the experienced accountant is doing with their time; it does not change the fact that an experienced accountant needs to be doing it.
That last point is the one most likely to get lost in the current enthusiasm around AI in professional services. The proof-of-cash tells you whether the cash flows in the books are internally consistent. It does not tell you whether the revenue is repeatable, whether key customer relationships will survive a change of ownership, or whether the owner's compensation has been structured in ways that distort the reported economics of the business. Those determinations require experience, contextual knowledge and the professional skepticism that develops over years of seeing how these things go wrong. No AI system currently available is a substitute for that.
What these tools do is give the accountant with that experience more time to apply it — and less time reconciling PDFs. For firms doing significant transaction volume in the lower middle market, that is a genuine operational change. The firms that benefit most are not treating AI as a replacement for the accountant's judgment. They are treating it as infrastructure: the part of the engagement that should have been automated years ago, finally being automated.
The proof-of-cash is not going away. The question is how much of the first week of the engagement still needs to be spent producing it.
