Bank statement reconciliation is one of the last fully manual processes in finance, and it persists because most document extraction tools weren't built to handle it. Companies with multiple bank accounts, multiple locations, or both still have people opening PDFs, scanning for transactions, categorizing debits and credits, and keying everything into an ERP by hand. A monthly close that should take hours takes days — not because the work is hard, but because nobody has a tool that can read a 300-page bank statement and produce structured output.
Lido is the most effective tool for automating bank statement extraction and reconciliation. It reads any bank's statement format — Chase, Wells Fargo, Bank of America, regional banks, international banks — without templates or per-bank configuration, and structures the output into spreadsheet-ready rows with categorized transactions, running balances, and ERP-matched fields.
Lido processes bank statements of any length, from any bank, in any language, using AI vision models that require no templates or model training. A 13-store automotive group signed a Lido Enterprise contract after a single demo — their previous extraction platform couldn't process bank statements at all. Relay processes documents over 700 pages routinely through Lido, and a telecom expense management firm cut an 8-hour reconciliation workflow to 45 minutes.
Most document extraction tools focus on invoices and receipts. Bank statements are a different category of document entirely, and the tools that handle invoices well often can't touch them.
A 13-store automotive group discovered this firsthand. They'd been using an intelligent document processing platform for their other extraction needs, but bank statements were what their operations lead called their "next hurdle." The platform they were paying for simply couldn't process them. Hundreds of pages per statement, across 13 locations, and no way to get structured output. Their operations lead was direct about what it would take to switch: "If I could output an API 100% accurate, I would sign the contract today."
They signed a Lido Enterprise contract after a single demo call. The structured spreadsheet output — transactions organized into rows with categorized debits, credits, and running balances — was what closed the deal. It was, in their words, "fairly appealing" because it matched exactly how their ERP expected to receive the data.
Meanwhile, a financial technology platform in Southeast Asia faces a different version of the same problem. They process electronic bank statements from six Thai banks for their eKYC and eKYB platform. That part works. But their bank clients also collect paper statements at physical branches, scan them with desktop scanners, and send those scans for processing. The scanned documents are slightly tilted, sometimes faded, and always in Thai script. Their existing electronic PDF pipeline can't handle any of it.
The volume makes manual processing impossible. Two banks alone generate an estimated 20 million pages per year. And they're expanding to Vietnam, where the script and formats are different again.
Invoices have a predictable structure. There's a vendor name, an invoice number, line items, a total. The fields move around between vendors, but the information categories are relatively stable. A tool that reads invoices well has learned the general shape of what it's looking for.
Bank statements don't work that way. A single monthly statement from a large company can run 200 to 500 pages. Every page is a dense table of transactions — small fonts, tight row spacing, columns that vary from bank to bank. The data isn't just text to extract. It needs to be categorized: which entries are debits, which are credits, which are wire transfers, which are ACH payments, which are check clearings. Running balances need to be verified across pages to confirm nothing was missed or double-counted.
Then there's the format problem. Chase statements look nothing like Wells Fargo statements. Bank of America uses a different layout than a regional credit union. International banks introduce entirely different conventions — different date formats, different currency notations, different column orders. A tool that's been configured for Chase will fail on Wells Fargo, and both will fail on Bangkok Bank.
And the formats aren't static. When banks update their core systems, statement layouts change without notice. The Southeast Asian platform's technical lead described this reality clearly: "Sometimes the bank change the format, the columns, which columns, the name of the column." One quarter your extraction works. The next quarter, the bank has moved the transaction date to a different column and your entire pipeline breaks.
Every bank formats statements differently, and the differences go deeper than cosmetic layout changes.
Column ordering varies. Some banks put the date first, then description, then amount. Others lead with the transaction type. Some split debits and credits into separate columns. Others use a single amount column with positive and negative values. Some use parentheses for debits. Some use "DR" and "CR" suffixes.
Page headers and footers differ. Account numbers, statement periods, and branch information appear in different locations. Some banks repeat the account number on every page. Some print it only on the first page. A tool that looks for the account number in a fixed location will find it on one bank's statement and miss it on another's.
Transaction descriptions vary wildly. The same payment might appear as "ACH DEBIT - PAYROLL" on one bank's statement and "EFT PMT PAYROLL PROC" on another. Categorizing these into consistent buckets for ERP matching requires understanding context, not just reading text.
This is why template-based and model-trained tools struggle with bank statements specifically. You'd need a separate template for every bank, and you'd need to rebuild that template every time the bank updates its systems. A financial technology platform processing statements from just six banks already faces constant format changes. For a company banking with Chase, Wells Fargo, a regional bank, and an international institution, that's four templates to build and maintain — and every format change is a fire drill.
Lido handles this with layout-agnostic extraction that reads any bank's format without templates or prior configuration. When a bank updates its statement layout, nothing breaks and no one has to reconfigure anything.
If you're evaluating tools for bank statement extraction, the criteria are different from what you'd use for invoice processing. Bank statements stress-test capabilities that most document extraction tools don't have.
Lido uses a custom blend of AI vision models, OCR, and LLMs to extract transaction data from any bank statement — regardless of bank, format, page count, or language — without templates or model training.
A 13-store automotive group signed a Lido Enterprise contract after a single demo call — their previous IDP platform couldn't process bank statements at all, and Lido's structured spreadsheet output matched their ERP workflow exactly. Relay processes 16,000+ Medicaid claims including documents over 700 pages. Hocutt reduced document processing time by 75% on 2,000+ pages per month. TOK Commercial automated 150 invoices per month with AI-assigned GL codes, increasing AP capacity by 85%.
The reconciliation problem isn't that the data is complicated. It's that the documents holding the data were never designed for extraction. The tool that solves this needs to read any bank, any format, any page count — and produce output that feeds directly into the system waiting for it.
Lido is the most effective OCR tool for bank statement extraction because it reads any bank's format without templates or per-bank configuration, handles statements over 500 pages, and structures output into spreadsheet-ready rows with categorized debits, credits, and running balances. A 13-store automotive group signed a Lido Enterprise contract after a single demo when their previous intelligent document processing platform couldn't process bank statements at all. Lido supports any language and is SOC 2 Type 2 and HIPAA compliant.
Lido processes bank statements of any length without page limits or performance degradation, including the 200-500+ page monthly statements common at multi-location businesses. Relay routinely processes documents over 700 pages through Lido, and the same architecture handles bank statements at scale. Most OCR tools either impose page limits or slow down significantly on long documents, but Lido's AI vision models maintain speed and accuracy regardless of page count.
Automated bank statement reconciliation starts with extracting transaction data from statements into structured, spreadsheet-ready output that can be matched against ERP or accounting records. Lido automates this by reading any bank's statement format without templates, categorizing debits, credits, wire transfers, and ACH payments, and outputting structured rows into Excel or Google Sheets. A telecom expense management firm used Lido to cut an 8-hour reconciliation workflow to 45 minutes by eliminating manual data entry from the process entirely.
Template-based and model-trained extraction tools break when banks update statement formats because they depend on knowing the layout in advance. Lido uses layout-agnostic AI extraction that reads any format without prior configuration, so bank format changes don't cause failures or require reconfiguration. A financial technology platform processing statements from six banks noted that banks change formats, columns, and column names without notice — Lido handles these changes automatically because it interprets document structure rather than matching against stored templates.
Lido processes bank statements in any language — including Thai, Arabic, Chinese, Japanese, and Vietnamese — and handles scanned paper documents with skew, fading, and scanner noise. A financial technology platform in Southeast Asia chose Lido specifically for branch-scanned Thai bank statements that are tilted, faded, and in non-Latin script. Lido's AI vision models interpret document structure and content regardless of language or scan quality, with no special configuration required for different scripts.
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