Action Guide: Classify for Bank Statements

Bank statements are one of the most critical documents MCA brokers and funders receive. They reveal transaction history, cash inflows, and spending patterns that define a merchant’s financial profile.

Before these statements can be analyzed, they must be accurately identified, labeled, and linked to the correct merchant record. Doing that manually is slow, repetitive, and prone to errors that disrupt the entire underwriting process.

Heron automates this classification step entirely. When bank statements arrive through shared inboxes, portals, or APIs, Heron’s system identifies them instantly, tags each document with key information, and routes it into the correct queue.

No manual downloading, renaming, or sorting is required. Every submission is automatically ready for the next phase of review.

For MCA brokers handling hundreds of submissions per day, automated classification transforms how teams work. Instead of spending hours sorting and labeling, operations teams can focus on underwriting, broker communication, and deal movement.

Heron makes the process fast, consistent, and scalable, even during peak submission periods.

Use Cases

• Detect bank statements automatically: Heron distinguishes bank statements from other files, identifying them with precise pattern recognition.
• Identify merchant and account details: The system reads merchant names, account numbers, and bank identifiers, tagging them correctly within the CRM.
• Separate multiple accounts: When submissions contain several statements, Heron separates them by account and institution for accurate filing.
• Tag date ranges: Heron reads the date period on each statement and labels it clearly, such as “March 2025” or “Q1 2025,” making future retrieval simple.
• Mark completeness status: The software checks if all months or pages are present and marks each packet as full or partial, helping teams follow up faster.
• Route to the correct queue: After classification, the system automatically directs each file to the right step, whether scrubbing, underwriting, or review.

Heron’s classification use cases eliminate bottlenecks caused by human sorting errors, making every document accessible, consistent, and ready for further processing within seconds.

Operational Impact

Automated classification produces measurable gains across operational efficiency, accuracy, and turnaround time. Manual sorting often represents one of the largest time drains for operations teams. By replacing it with automation, Heron reduces those costs immediately while increasing precision.

Teams experience faster handoffs between intake and underwriting because bank statements arrive pre-labeled and complete. With classification automated, brokers and funders spend less time verifying and more time making funding decisions.

Operational KPIs most affected:

• Turnaround time: Decreased by up to 90 percent.
• Touches per submission: Reduced to near zero because no manual tagging is required.
• Exception rate: Lowered significantly by catching misplaced or incomplete files early.
• Backlog reduction: Queues clear faster, even during high-volume submission days.
• Cost per submission: Reduced due to fewer manual processing hours.
• FTE savings: Teams can manage two to three times more submissions with the same staff.

When classification runs smoothly, the downstream workflow accelerates. Underwriters get clean data faster, compliance reporting becomes easier, and funding times improve without increasing headcount.

Quality Controls and Exception Handling

Heron’s accuracy comes from multiple validation layers that confirm every document type and route. This quality control ensures reliability across all incoming submissions.

• Adaptive learning models: Heron continuously learns from every submission to recognize new bank layouts automatically.
• Confidence scoring: Each classification includes a confidence percentage. Low-confidence items are routed to a review queue for human confirmation.
• Exception routing: Reviewers receive clear labels and reason codes to fix issues quickly, such as “low image quality” or “ambiguous layout.”
• Feedback loops: Every correction feeds back into Heron’s model, improving accuracy on future classifications.
• Audit logging: Each action is logged with timestamps and user IDs for transparency and compliance.
• Monitoring dashboards: Teams can see classification speed, accuracy, and review rates to track performance over time.

Heron’s classification process balances automation with accountability, keeping quality high even as volume increases.

Configuration and Integration

Heron fits into existing systems seamlessly, meaning teams can automate classification without changing how they work.

• Inbox integration: Heron connects directly to shared mailboxes like submissions@ or underwriting@, classifying attachments in real time.
• Portal and API connection: Submissions from partner portals or APIs flow into Heron instantly, ready for classification.
• CRM synchronization: Each document attaches to the correct merchant or deal record, keeping data consistent across platforms.
• Routing logic: Operations leaders can set specific destinations for each classification result, from underwriting to secondary review.
• File naming standards: Files are renamed automatically using templates such as Merchant_Bank_MonthYear.pdf, ensuring consistent organization.
• Scalability: Whether processing hundreds or thousands of statements per day, Heron maintains consistent performance and accuracy.

By connecting classification directly to intake, Heron creates a single, continuous workflow from submission to underwriting-ready documentation.

Implementation Best Practices

To get the most out of Heron’s classification system, operations teams should follow a structured rollout that combines setup, monitoring, and refinement.

• Define routing paths: Decide which queues receive classified documents and ensure these match your underwriting flow.
• Start with core document types: Focus on your most frequent bank templates before expanding to secondary or less common formats.
• Monitor confidence thresholds: Track which documents fall below your confidence benchmark and adjust parameters accordingly.
• Establish feedback protocols: Have reviewers tag misclassifications immediately so Heron can retrain its model.
• Encourage consistent submissions: Communicate with brokers about preferred formats and clear scans for faster, more accurate classification.
• Align file naming conventions: Use consistent naming that mirrors internal systems for easy retrieval and audits.
• Perform routine audits: Review classified documents periodically to confirm accuracy and catch edge cases early.
• Track performance improvements: Measure turnaround time, exception rate, and rework frequency to calculate operational ROI.
• Share results with teams: Transparency builds trust in automation, helping staff understand its impact.
• Scale gradually: Once accuracy stabilizes, expand classification coverage to new document types like financial statements or tax forms.

Following these practices ensures Heron’s classification process stays accurate, efficient, and continuously improving over time.

Benefits of Using Heron for Classifying Bank Statements

• Speed: Classification happens in seconds, replacing hours of manual effort.
• Accuracy: AI pattern recognition ensures documents are identified correctly across multiple bank formats.
• Scalability: Teams manage large submission volumes without hiring additional processors.
• Consistency: Every document follows the same labeling, routing, and naming standards.
• Reliability: Classification data is logged, traceable, and audit-ready for compliance.

Heron transforms classification from a repetitive, error-prone task into a dependable system that strengthens underwriting efficiency across the organization.

FAQs About Classify for Bank Statements

How does Heron recognize bank statements automatically?

Heron reads document structure, layout, and financial terminology to identify bank statements accurately. It scans for recognizable features like account summaries and transaction tables to confirm the file type before routing.

Can Heron handle new or unusual bank formats?

Yes. The system’s learning model adapts to new templates. When reviewers confirm a new layout, Heron automatically updates its recognition rules to classify it correctly next time.

What happens if a file is misclassified?

Each classification receives a confidence score. Files below the confidence threshold go to a review queue for confirmation. Any corrected labels improve future recognition accuracy.

Is Heron compliant with data security standards?

Yes. Heron follows SOC 2 Type II security standards. All documents and logs are encrypted, and user access is fully controlled and recorded.

How much time can operations teams save with classification automation?

Teams typically cut manual sorting time by 85 to 90 percent. Staff who once spent hours per day labeling files can now process several times more submissions with the same resources.