Bank statement converter for restaurants

What makes restaurant books a chore

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Reconciling card settlements to your POS

The defining restaurant reconciliation is the daily card deposit. Your processor batches a day's card sales, subtracts its fee, and deposits the net a day or two later — so the bank figure never matches the POS sales figure on its own. With every deposit converted into a labelled row, you can line each settlement up against the matching day's POS total and the processor fee, and confirm the money landed as expected.

FlowParse balance-validates each statement, so no settlement or fee line is dropped, and the reconciliation engine helps match the bank deposits to your sales records. Do this regularly and a skimmed batch, a missing deposit or a fee creeping up becomes obvious instead of invisible.

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Tracking food, beverage and labour cost

Prime cost — the sum of cost of goods (food and beverage) and labour — is the number restaurants live and die by. It usually has to sit around 60% of sales, and tracking it means pulling supplier payments and payroll out of the statement and totalling them against revenue. Buried in a PDF, those costs are invisible; as structured rows they're a category total.

Once transactions are clean rows, categorising them — food suppliers, beverage and liquor, payroll, utilities, rent — is fast, and the food-and-labour total against sales gives you prime cost for the period. Pair it with a cash-flow view and you can watch the metric that actually governs whether the restaurant makes money.

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Keeping on top of supplier payments

A busy kitchen pays a lot of vendors — produce, meat, dry goods, beverages, linens, services. Across a month that's a long list of payments, and without structured data it's hard to see how much went to whom or whether a price crept up. Converting the statement turns that list into a sortable, totalable record you can analyse by vendor.

That visibility is practical: spotting that beverage cost jumped, that a supplier was paid twice, or that a recurring charge you forgot is still running. Each payment keeps its description, so grouping by vendor is straightforward, and the totals feed both your COGS tracking and your cash-flow planning for the slower weeks.

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Handling tips, cash and payroll cleanly

Tips and cash add a wrinkle restaurants know well. Cash sales are deposited in batches that must be separated from card revenue; tips may flow through payroll or as separate distributions; and payroll itself is one of the largest, most regular outflows. Mixed together in a statement, they distort both revenue and labour cost.

Structured rows let you keep each stream distinct — cash deposits as their own line, tip distributions categorised separately, payroll runs clearly labelled — so taxable sales, labour cost and owner draws don't bleed into each other. That separation is what makes the prime-cost and tax numbers trustworthy rather than approximate.

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From a drawer of statements to a clean spreadsheet

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Bringing in card spend and multiple locations

Many operators put supplies and small purchases on a business card, and run more than one location. The credit card statement converter brings card spend into the same dataset line by line, so a delivery paid on the card and a supplier paid from the bank live together. For multi-site operators, converting every location's accounts into the same columns is what lets you compare them fairly.

That uniformity matters when you want to ask which site runs the best food cost or where labour is heaviest. Once each location's statements are the same structured shape, the comparison is a pivot table rather than a manual slog through separate PDFs.

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A whole year, every account, at once

A year of high-volume restaurant statements is a serious stack. Combine bank statements into one Excel consolidates up to 100 PDFs with duplicate detection and a source reference on every row, so a full year across the bank, the card and a second location becomes one workbook ready to categorise and analyse.

Consolidation is what makes seasonality visible — the summer rush against the January lull, this year against last — and what your accountant wants at year end instead of a box of paper. Convert once, and the relentless transaction volume of hospitality becomes a single, sortable dataset.

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Spotting cost creep before it hurts

In a business running at single-digit net margin, small increases are dangerous precisely because they're easy to miss. A produce supplier nudges prices up, a delivery fee appears, a software subscription renews higher, a processor's rate ticks up a fraction per swipe. None is alarming alone, but together they quietly erode the margin a restaurant works hard to protect — and in a drawer of PDF statements, they're invisible.

Converted, categorised data makes creep visible. When food cost, beverage, processor fees and recurring services each have their own running total month over month, an upward drift stands out as a line on a chart rather than a vague sense that things feel tighter. That early warning is the difference between renegotiating with a supplier now and discovering the problem in a year-end account.

It also catches the charges everyone forgets: a trial that became a paid subscription, a piece of equipment still being financed, an insurance premium that crept up at renewal. Seeing every recurring outflow as a clean row is the simplest audit a restaurant can run, and it routinely pays for itself the first time it surfaces a cost that should have been cancelled months ago.

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Month-end close in a fraction of the time

Restaurant month-end is heavy: reconciling weeks of daily card settlements, totalling supplier payments, separating cash and tips, and getting it all in front of an owner or accountant while the next month is already running. Done by hand from statements, it's the kind of job that slips, and a late close means decisions get made on stale numbers.

Structured statement data compresses that work dramatically. With settlements already matched to POS days, costs categorised, and the whole month in clean rows, the close becomes a review rather than a reconstruction — confirm the reconciled figures, glance at the flagged exceptions, and export. The same dataset feeds the owner's flash report and the accountant's books without anyone re-keying it.

Doing the close quickly and on time changes what it's for. Instead of a backward-looking compliance chore, a fast month-end gives an operator current numbers to act on — adjust ordering, tweak the menu mix, address a labour overrun — while it still matters. That timeliness is one of the most practical returns on converting statements rather than typing them.

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Cash control and a clean audit trail

Cash is both a feature and a risk in hospitality. Cash sales have to be deposited, counted and reconciled, and the gap between what the POS rang up and what reached the bank is exactly where shrinkage hides. Converting statements gives you the bank side of that comparison as clean data, so cash deposits can be checked against expected takings rather than taken on trust.

A complete, structured record is also what every external party eventually asks for. A tax authority, a franchisor, a lender or a prospective buyer all want to see consistent, traceable financials — and a restaurant that can produce a reconciled, balance-validated dataset for any period is in a far stronger position than one offering a box of statements. Every figure tracing back to its source line is what makes the books defensible.

That audit-readiness compounds over time. Keeping each month in convertible, validated shape means there's never a scramble when a review lands — the history is already clean, already reconciled, and already exportable to whatever format the reviewer needs. For a sector where ownership and financing change hands often, that's a quiet but real advantage.

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Accurate extraction you can trust

When margins are this thin, a misread settlement or a dropped supplier payment matters. FlowParse reads statements with around 98% field-level accuracy on standard layouts, joins wrapped descriptions, keeps the sign on every amount, and balance-validates each statement so a missing or duplicated line is caught before it skews prime cost. Low-confidence fields are flagged for a quick glance rather than buried in the volume.

Scanned or photographed statements convert too, via OCR with confidence scoring, so even a statement you only have on paper becomes clean data. Because every figure traces back to its source line, the cost and tax numbers you build are defensible the day an accountant or auditor asks how you got them.

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Export to your tool of choice

Restaurants keep books in everything from a spreadsheet to QuickBooks, Xero or restaurant-specific accounting tools. Convert once and pick the output that fits your back office.

What clean books are worth to an operator

Restaurants fail more often from poor visibility than from poor food, and visibility starts with the numbers. The time saved by not typing statements is real, but the bigger prize is having current, trustworthy figures — prime cost, settlements, supplier totals — early enough in the month to act on them while the month is still running.

There's also a hard error cost to the alternative. In a business at single-digit margin, a dropped deposit or a miscategorised cost can flip a month from profit to loss on paper, and chasing that down later eats hours and confidence. Balance-validated, traceable data means the figures you act on and report are right the first time, with every line tracing back to the statement.

And clean books are worth money when the restaurant changes hands or seeks finance. A lender, a landlord assessing a lease, or a buyer all pay more attention — and often a better price — to an operator who can produce consistent, reconciled financials on demand. Converting statements as you go is what makes that record exist when you need it.

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Your financial data stays yours

Your sales, supplier and payroll data is sensitive, so it's handled with care. Uploads run over TLS on EU-hosted infrastructure, the original PDF is deleted right after processing, data is isolated per user, and documents are never used to train AI models. You keep the structured output; the source statement doesn't linger.

For groups automating their books, the document extraction API keeps the same processing inside your own flow with per-key authentication and usage logging. Whether you convert in the browser or over the API, the handling is bank-grade — appropriate for the numbers a restaurant runs on.

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Frequently asked questions

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