Process thousands of checks daily with AI that parses MICR lines, validates routing numbers, and feeds structured data into clearing and fraud detection systems.
Drag and drop files, connect a cloud drive, or set up email auto-forwarding. Any file format works—PDF, JPEG, PNG, TIFF, or digital documents.
The AI identifies fields by context and meaning, not fixed coordinates. Names, dates, amounts, and custom fields are extracted automatically.
Get structured output in Excel, Google Sheets, CSV, or JSON. Use the REST API for direct integration into your systems.
“Our lockbox processes 3,000 checks per day. The previous OCR system needed a template for every issuing bank. This reads them all without any configuration.”
“Routing number validation alone saved us from dozens of rejected items per month. The system catches MICR misreads before they hit the clearing house.”
“We pipe the JSON output into our fraud rules engine. Duplicate check numbers and courtesy-legal amount mismatches are flagged automatically now.”
Audited controls over a sustained period, not a point-in-time check.
Bank-grade encryption at rest and TLS 1.2+ in transit.
Documents deleted within 24 hours. No copies retained.
Bank check OCR is the application of optical character recognition and AI extraction specifically to the workflows that banks and financial institutions use to process checks at scale—lockbox operations, teller capture stations, remote deposit capture, and interbank check clearing. Unlike general-purpose document OCR, bank check OCR must parse the MICR line into its component routing number, account number, and serial number fields, cross-verify the courtesy amount against the legal amount line, and output structured data that integrates with core banking platforms and clearing house systems.
Lockbox processing is the highest-volume use case. Banks operate lockbox services where customer payments are mailed to a central address, opened in bulk, and deposited on behalf of the payee. Each check must be imaged, its data extracted, and the payment posted to the correct account—often within the same business day. Legacy lockbox OCR systems relied on MICR readers for the magnetic ink line and template-based OCR for the printed fields. When checks arrived from unfamiliar banks with non-standard layouts, these systems required manual intervention or template creation, slowing throughput.
AI-powered check OCR eliminates the template dependency by reading each check contextually. The model identifies the payee, amounts, date, and MICR data based on spatial relationships and label text rather than fixed coordinates. This means a cashier’s check from one bank and a personal check from another are processed by the same engine with no configuration changes. Lido provides this layout-agnostic extraction with a REST API that returns structured JSON, making it straightforward to connect to existing core banking middleware or fraud detection systems.
Fraud detection benefits directly from structured check data. When every field is extracted and labeled, automated rules can flag duplicate check numbers within an account, mismatches between the courtesy and legal amounts (a common alteration indicator), routing numbers that fail ABA checksum validation, and payee names that differ from expected patterns. Moving these checks from manual visual inspection to automated data-driven rules improves both detection rates and processing speed.
Bank check OCR for lockbox operations processes thousands of check images per day by accepting batch uploads, cloud drive connections, or email auto-forwarding. Each check is extracted independently and results populate a single structured dataset with one row per check. Lido’s platform handles up to 360,000 pages per year on Scale plans, with enterprise tiers for higher volumes.
Yes. After extracting the MICR line, the system parses it into routing number, account number, and check serial number. The nine-digit routing number can be validated against the ABA checksum algorithm to confirm it is structurally valid. Lido flags routing numbers that fail the checksum so they can be reviewed before entering downstream systems.
Check OCR contributes to fraud detection by digitizing check fields into structured data that can be cross-referenced against known fraud patterns. Extracted data enables automated checks for duplicate check numbers, mismatched amounts between courtesy and legal lines, altered payee names, and routing numbers associated with flagged accounts. Lido outputs structured JSON via its REST API for integration with fraud rules engines.
Yes. Personal checks, business checks, cashier’s checks, and certified checks all have different layouts, fonts, and security features. AI-based bank check OCR reads each check contextually rather than relying on fixed templates, so it handles the full range of check types that a bank encounters without separate configuration.
Lido provides a REST API that returns extracted check data as structured JSON with field-level confidence scores. This API output can be consumed by core banking platforms, check clearing systems, or custom middleware. For teams that prefer file-based integration, CSV and Excel exports are also available. Enterprise plans include custom integration support.
Start free with 50 pages. Upgrade when you’re ready.
Built on Lido’s OCR engine
Built on Lido’s OCR engine
Built on Lido’s OCR engine