Why Bank-to-GL Data Isn't a People Problem

TL;DR: Ledge's 2025 survey of 100 finance professionals found that understaffing is the least-cited blocker to a faster close, at just 37%, well behind Excel dependency (50%) and cross-department dependency (56%). Half of finance teams still take six or more business days to close, and cash reconciliation alone eats 20 to 50 hours a month. The real bottleneck is that bank data arrives unstructured and has to be manually mapped to the chart of accounts every cycle. Bank reconciliation automation, not additional headcount, closes that gap.

Why Bank-to-GL Data Isn't a People Problem

When close drags past a week, the instinct in most finance organizations is to reach for headcount. Add an accountant, add a temp during peak season or push the controller to run a tighter calendar. The data from Ledge's 2025 survey of 100 finance professionals says that instinct is aimed at the wrong target.

Asked to select every blocker preventing a faster close, respondents ranked understaffing dead last among the major factors, cited by just 37%. Dependency on other departments and regions came in at 56%, over-reliance on Excel came in at 50%, legacy systems that don't integrate came in at 40%, and high transaction complexity, at 39%, even edged out staffing gaps. Ledge's survey makes the pattern hard to miss.

Getting close done fast and efficiently is clearly an architecture problem, and bank reconciliation automation is where that architecture starts.

How slow is slow?

According to the same survey, half of finance teams surveyed take six or more business days to close, and 27% take more than seven. Only 18% manage the three-day close that's become something of an industry benchmark to aspire to. That gap between aspiration and reality isn't closing on its own, and more bodies in the process haven't been enough to close it.

The report's ranking of where time actually goes points squarely at reconciliation, with account reconciliation, banks, credit cards, and payment processors, coming out on top by frequency of mention, ahead of accruals, data hygiene, and variance analysis.

Cash reconciliation specifically consumes 20 to 50 hours a month for many teams, and most teams need three to five separate systems to get through it. One finance manager in SaaS told Ledge cash reconciliation alone eats 30-plus hours a month, and that a single delayed source pushes back the entire close.

Clearly, such a widespread challenge can't be put down to a training or people problem. Instead, it's a symptom of the same manual reconciliation work having to happen every single cycle, because the underlying data never arrives in a state that's ready for the ledger.

Excel is covering for missing structure

94% of teams surveyed still use Excel somewhere in month-end close, and 50% cite it directly as a reason their close runs slow. It would be easy to read that as the need to reduce the use of spreadsheets, but that framing gets the causality backwards. Teams are having to rely so heavily on Excel because bank data doesn't arrive pre-mapped to the chart of accounts, and someone has to build the bridge between what the bank sends and what the GL needs, every month, by hand.

One senior accountant in healthcare described the actual mechanics to Ledge directly, exporting data from three separate systems just to match it in a spreadsheet. That shows what happens when the source data is unstructured and the mapping logic lives in someone's head, or in a spreadsheet formula nobody else fully understands, rather than in the system itself.

Add headcount to that process and the bottleneck just gets one more person doing manual mapping work in parallel with everyone else already doing it.

What "good" actually looks like

Ledge also asked respondents what they consider the gold standard for close timelines. Three to five business days was the most common answer, with some high performers aiming for two to three days. What's notable is that nobody in the survey framed a faster close as a staffing exercise.

One director of finance in eCommerce told Ledge that five days gives a team just enough time to be accurate without slowing down the business, and a senior accountant in SaaS pointed to three days as a discipline that keeps everyone accountable, not as a target that requires a bigger team to hit.

The teams closest to a genuine three-day close tend to have the most automation coverage, regardless of department size. A team automating the bulk of its reconciliation work is working from data that's already sorted into the right accounts by the time anyone opens a spreadsheet, so there's simply less manual translation left to do. A team still doing that translation by hand can add analysts every quarter and the close will keep landing on day six or seven, because the work that's actually slow was never the kind where more people speed up.

The upstream problem

Bank statements and transaction feeds don't come coded to a chart of accounts. A payment description from a bank API or a BAI2 file might say something like 'ACH DEBIT 4471829 XYZ CORP' and give no indication of which GL account, cost center, or entity it belongs to. Someone in finance has to look at that line, decide what it is, and map it, every cycle, for every account, across however many banks and entities the business runs.

That mapping work is exactly what the 56% "dependency on other departments" and 40% "legacy systems that don't integrate" figures are describing from a different angle. Departments get pulled in because the finance team needs context the raw bank data doesn't carry. Legacy systems don't integrate because bank connectivity was never built to speak the same structured language as the ERP. The fix for both is data that's already structured and tagged by the time it reaches anyone's desk, so nobody has to chase down more context to make sense of it.

Standardize before the GL, not after

The teams that get closer to a genuine three-day close are the ones where bank data is normalized and coded against the chart of accounts automatically, as it arrives, rather than reconstructed by hand at month end.

This is the problem Trovata Cash and Trovata Data are built to solve directly, tagging and normalizing transactions against a company's chart of accounts as the data comes in from the bank, rather than leaving that mapping work for someone to redo every close.

It's a similar principle to the one behind Trovata's approach to automated bank reconciliation, matching cleared transactions against the ledger continuously rather than in a single manual pass at month end. When reconciliation starts from data that's already structured, the reconciliation step itself stops being the bottleneck the Ledge survey describes, and finance teams get back the 20 to 50 hours a month currently spent stitching systems together by hand.

The broader point holds regardless of which tool a finance team eventually picks. Close speed is a function of how much manual translation work sits between the bank and the ledger, not how many people are available to do that translation. Fixing it means moving the standardization work upstream, before the data reaches anyone's desk, rather than downstream, into the hours a stretched team spends reconciling every single month.

For finance leaders building the case internally, the Ledge numbers are a useful starting point precisely because they cut against the instinctive fix. A hiring request is easy to justify on paper and slow to show results, whereas a change to how bank data enters the ledger is a harder sell in a single meeting, but it's the one that actually moves the needle on the metric everyone's tracking.

To see how Trovata could simplify your close process, book a demo today.

Frequently asked questions

What is bank reconciliation automation? Bank reconciliation automation is software that matches bank transactions against the general ledger continuously, without someone manually exporting and comparing data in a spreadsheet each month. It typically also normalizes and codes incoming bank data against the chart of accounts as transactions arrive, rather than leaving that mapping work for someone to redo at close.

Why does month-end close take so long for most finance teams? Because reconciling bank data against the ledger is manual, repetitive work that has to happen every single cycle. Ledge's 2025 survey found cash reconciliation alone consumes 20 to 50 hours a month for many teams, and half of finance teams take six or more business days to close.

Is a slow close a staffing problem? No, the data says otherwise. In Ledge's survey, understaffing was the least-cited blocker to a faster close at 37%, well behind dependency on other departments (56%) and reliance on Excel (50%).

Why do finance teams rely so heavily on Excel during close? Because bank data doesn't arrive pre-mapped to the chart of accounts, so someone has to build that bridge by hand every month. Ninety-four percent of teams surveyed by Ledge still use Excel somewhere in close, and half cite it directly as a reason their close runs slow.

Does adding headcount speed up a slow close? Not on its own. Adding people to a manual reconciliation process just adds another person doing the same manual mapping work in parallel, rather than removing the work itself.

What does "good" look like for a month-end close? Most finance leaders surveyed by Ledge pointed to three to five business days as the standard to aim for, with some high performers closing in two to three. None of them described getting there as a matter of adding staff.

What should bank data look like before it reaches the GL? It should already be normalized and coded against the chart of accounts by the time anyone opens a spreadsheet, rather than reconstructed by hand at month end. That's the difference between reconciling structured data and re-deriving structure from scratch every cycle.

How does Trovata help automate bank reconciliation? Trovata tags and normalizes bank transactions against a company's chart of accounts as the data arrives, then matches cleared transactions against the ledger continuously instead of in one manual pass at month end. That removes the manual mapping work that drives most of the time spent on close.