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Automatically capture, validate, and route check data accurately. Standardize transactional data across formats and sources, and eliminate manual, paper-based check processing with intelligent workflows built for banking and finance operations.
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Despite the widespread adoption and availability of check imaging and processing technology, most organizations still struggle to process and validate checks at scale. This is because the software they use is often template-dependent and lacks the ability to adapt to complex requirements, which introduces systemic challenges that lead to delays and errors in check processing.
Traditional check processing systems operate on siloed data and do not centralize transaction information, resulting in fragmented handoffs across intake, review, reconciliation, and retrieval. This increases manual intervention and leaves workflows disconnected.
Template-dependent platforms struggle to accurately extract check data across varying formats and lead to frequent errors in amounts, routing details, and payee fields.
Checks enter through branch, ATM, merchant capture, and remote deposit without unified datasets, which causes systems to overlook duplicate presentment, altered checks, and other discrepancies associated with fraud.
Collatio Check Processing is SCRY AI’s intelligent check handling platform that automatically extracts, structures, and validates check data at enterprise scale. It combines Optical Character Recognition (OCR), Intelligent Character Recognition (ICR), and MICR parsing with AI-driven validation logic to read every field on a check with high accuracy. This includes the payee name, written amount, numeric amount, date, routing number, account number, and check serial number. The platform also reconciles captured data against configured business rules and linked payroll, cash management, and government datasets, then integrates verified data directly into downstream systems. Automated check processing helps organizations accelerate deposit clearing and reduce float time without relying on paper-based handling or complex template configurations.
Collatio check processing platform uses AI-powered proprietary algorithms and domain-trained models to process high volumes of checks with unprecedented speed and accuracy. It reduces manual effort, minimizes the risk of human error, and ensures reliable processing at scale.
Collatio reads and ingests checks through multiple channels, including remote deposits, scanned files, and batch uploads, irrespective of their format or layout. It captures each document with complete metadata so that every item maintains a consistent structure. The platform then preprocesses each check to correct skew, remove noise, and adjust contrast to enhance image quality.
The system employs AI-powered Optical Character Recognition, Intelligent Field Recognition, and domain-trained NLP models to parse and extract essential check data with high accuracy. This includes MICR line details, courtesy amount, legal amount, date, payee name, bank details, check number, endorsement presence, routing number, serial number, and transaction code. The system also interprets the semantics and contextual relationships between fields. It standardizes, sorts, and organizes the extracted data in a clean, structured manner for validation and reconciliation.
Every check carries two representations of the payment amount: the Courtesy Amount Recognition field, which contains the numeric value, and the Legal Amount Recognition field, which contains the written form. Collatio extracts both independently using separate recognition models, one calibrated for numeric recognition, and another using ICR for handwritten text interpretation. The two values are automatically cross-referenced against each other and relevant validation rules. Any discrepancies are flagged and routed for review through a human-in-the-loop workflow, where both values and the full context are displayed side by side for quick resolution.
The platform compares each presented check against the client’s issued check file, flagging any item where the payee's name, amount, or serial number does not match the authorized record. Each check image is also analyzed using a multi-layered fraud detection framework that evaluates ink consistency, paper texture variation, MICR line alignment, and indicators of field alteration. Signature verification compares the check signature against known reference samples using deep learning models. The system then assigns a risk score to every check before any settlement instruction is issued. High-risk items are routed for human review, with specific anomaly indicators highlighted in the review interface, while low-risk items proceed without delay.
All extracted and validated check data is delivered through configurable integration channels and formats and routed into ERP systems, accounting workflows, core banking environments, case queues, or archive repositories. The platform maintains a tamper-evident archive of all processed checks, including front and back images, and records a complete audit trail with timestamps for data extraction, validation events, exception flags, and review decisions. This ensures traceability, compliance, and operational transparency across the entire processing lifecycle.
Financial institutions deploying Collatio in production environments achieve measurable improvements in data accuracy, operational efficiency, and fraud risk detection.
field extraction accuracy across MICR, CAR/LAR, payee, check date, and other key fields
real-time fraud detection rate for check deposits
reduction in manual review queues
decrease in check processing time
Collatio captures check images from multiple input channels, including mobile deposit applications, remote deposit systems, scanned documents, and direct uploads. It supports a wide range of formats and layouts without prior standardization. The platform enhances each image through preprocessing techniques such as resolution adjustment, deskewing, contrast correction, and noise reduction. It processes both the front and back of the check to ensure complete data capture. These enhancements improve image clarity and consistency, which supports reliable recognition across varying input conditions.
The platform uses AI-powered Optical Character Recognition and domain-trained NLP models to parse and extract essential check data. This includes MICR details at the bottom of the check, such as the routing number, account number, and serial number, along with the issue date, payee name, memo, and bank information. The system also interprets each field and the contextual relationships within the document. This enables accurate extraction of values from the Courtesy Amount Recognition and Legal Amount Recognition fields, ensuring both numeric and written amounts are captured with precision.
The platform reconciles extracted check data to ensure accuracy and consistency before further processing. It compares values from the Courtesy Amount Recognition and Legal Amount Recognition fields and verifies alignment within a configurable tolerance threshold. It applies configurable business rules to validate key attributes such as payee name, amount, routing number, account number, check number, and signature against authorized records and reference datasets. The system also performs stale date checks based on defined aging limits and detects duplicate transactions. It further evaluates each check for signs of alteration or fraud, including inconsistencies in ink, font alignment, MICR line positioning, and paper texture. Any discrepancy or suspicious condition is flagged in real time and routed for review.
Collatio automatically identifies exceptions such as mismatches, missing fields, and validation failures, and triggers real-time alerts for the relevant teams. Flagged checks are routed to a centralized review workspace, where users can access extracted data, original check images, and validation results in a single, unified view. Through a governed human-in-the-loop workflow, finance teams can review, validate, and take appropriate action, including correcting data, approving exceptions, or escalating items for resolution. This approach ensures accuracy, accountability, and compliance while maintaining operational efficiency. Once resolved, checks move forward in the workflow without delays or manual handoffs.
Collatio integrates with existing banking and finance workflows, enabling cleared check data to flow into downstream systems in a structured format for direct consumption by core banking platforms, ERP systems, and general ledger applications. The output includes all relevant transaction details required for posting and reconciliation, allowing entries to be processed without manual intervention. The platform supports timely posting, reconciliation, and reporting.
Collatio can process all standard check documents and related records used across commercial, personal, and institutional payment workflows. It does not require layout-specific configuration for each document type.
Collatio’s automated check processing incorporates industry best practices to ensure that all check and transactional data remains secure, confidential, and compliant at all times.
End-to-end encryption, combined with ISO 27001 and SOC 2 compliance, delivers robust protection against unauthorized access and misuse.
Analyzes check data to identify potentially fraudulent activities in real time, supported by time-stamped audit trails that ensure complete transparency and traceability.
Role-based permissions govern access to check data, with detailed logs providing accountability and complete visibility into all user actions.
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