Balance sheets are key financial statements that show a company’s assets, liabilities, and equity at a given point in time. For MCA brokers and funders, they reveal whether a business can support new advances or loans.
Yet, parsing these documents manually (pulling numbers into spreadsheets or CRMs) is one of the most tedious and error-prone parts of the underwriting process.
Heron automates the parsing of balance sheets. The platform reads the file, identifies line items such as cash, accounts receivable, current liabilities, and retained earnings, and converts them into structured data.
This data flows directly into the CRM, creating a clear snapshot ready for underwriting. Parsing turns static financial documents into decision-ready data fields without requiring human intervention.
Use Cases
- Extract key financial metrics: Heron reads balance sheets to identify total assets, total liabilities, and equity automatically.
- Normalize field names: Different accounting formats use varied terminology (e.g., “Payables” vs. “Accounts Payable”); Heron standardizes them.
- Handle scanned documents: The system parses both digital and scanned PDFs, converting them into structured text.
- Support historical analysis: Parsed values from multiple balance sheets feed trend and ratio analysis automatically.
- Detect data gaps: When fields such as total assets or retained earnings are missing, Heron flags the record for review.
- Connect to underwriting workflows: Parsed values instantly populate CRM records, supporting eligibility and risk scoring.
These use cases help teams eliminate repetitive data entry, maintain consistency, and gain faster visibility into business health.
Operational Impact
Automated parsing delivers tangible improvements across the underwriting process.
- Speed: Balance sheets are converted to structured data in seconds, reducing turnaround time dramatically.
- Accuracy: Automated data capture removes the human error common in manual rekeying.
- Scalability: Teams can process hundreds of submissions per day with no added headcount.
- Consistency: Field names and values follow the same format across brokers and submissions.
- Auditability: Every parsed field links back to its document source for validation.
Parsing automation ensures data accuracy while enabling funders to focus on analysis and decision-making rather than clerical work.
Parsing Logic and Workflow
Heron’s parsing process uses advanced document understanding models built specifically for financial workflows.
- Document intake: Balance sheets arrive via email, portal, or API.
- Document recognition: The system identifies the file type as a balance sheet using structural and textual cues.
- Field extraction: Heron parses critical sections, such as assets, liabilities, and equity, pulling totals and subcategories.
- Normalization: Extracted data is reformatted to match the CRM and underwriting schema.
- Validation: Totals are cross-checked (assets must equal liabilities plus equity), and discrepancies are flagged.
- Write-back: Verified data is written into the CRM under the merchant’s record with timestamps and field mapping.
This structured pipeline guarantees clean, decision-ready data for every deal.
Governance and Data Quality Controls
Parsing automation maintains data integrity and transparency throughout the process.
- Source traceability: Each parsed field links to its location in the original document.
- Validation rules: Numerical relationships (e.g., assets = liabilities + equity) confirm parsing accuracy.
- Version tracking: Any re-parsed document retains its prior data for audit comparison.
- Error flagging: Fields that fall below confidence thresholds are marked for human review.
- Compliance alignment: All parsing activities are logged, encrypted, and SOC 2 compliant.
- Audit reporting: Teams can export detailed parsing summaries for internal or regulatory review.
Heron combines automation speed with strict quality assurance to maintain full accountability.
Integration and Configuration
Heron’s parsing feature plugs directly into lending and underwriting workflows.
- CRM sync: Parsed fields populate merchant or deal records automatically.
- API access: Partners can pull parsed data for analytics or secondary systems.
- Custom field mapping: Teams decide which extracted fields appear in CRM records.
- Queue management: Low-confidence parses are routed automatically to an exception queue.
- Notification setup: Users receive alerts for completed parses or flagged items.
- Scalable performance: Handles thousands of pages daily without latency.
This flexibility allows Heron to fit seamlessly into any funding organization’s tech stack.
Advanced Features and Analytics
Beyond simple data extraction, Heron’s parsing models support richer insight generation.
- Trend calculation: Multiple parsed balance sheets feed year-over-year comparisons.
- Ratio generation: Automatically calculates debt-to-equity, current, and quick ratios.
- Cross-document validation: Compares parsed values against profit and loss statements for consistency.
- Anomaly detection: Highlights outliers such as sudden drops in equity or unexplained asset spikes.
- Portfolio benchmarking: Aggregates parsed data across merchants to surface industry insights.
- Custom exports: Parsed results can be exported for reporting or third-party review.
These capabilities turn static PDFs into dynamic financial intelligence.
Implementation Best Practices
Teams can maximize parsing accuracy and adoption by following several best practices.
- Standardize broker submissions: Encourage brokers to submit high-resolution or digital copies instead of scans.
- Define parsing priorities: Identify the key financial fields most important for underwriting first.
- Review confidence reports: Early audits help tune models and improve accuracy rates.
- Align with data owners: Underwriters should confirm field mappings and ratios used in credit models.
- Automate feedback loops: Feed exception resolution data back into Heron to refine accuracy over time.
- Track time savings: Record how much faster underwriting proceeds once parsing is automated.
Following these steps ensures smooth rollout and lasting operational gains.
Benefits of Using Heron for Parsing Balance Sheets
- Speed: Extracts and structures balance sheet data instantly.
- Accuracy: Reduces rekeying errors and manual input.
- Scalability: Handles large submission volumes without additional staff.
- Transparency: Links parsed values directly to their source.
- Compliance: Maintains audit trails and validation checks for every record.
Heron converts unstructured balance sheet documents into clean, usable data that drives faster, smarter funding decisions.
FAQs About Parse for Balance Sheets
How does Heron parse balance sheets automatically?
Heron reads the document, identifies structural patterns, and extracts financial fields like assets, liabilities, and equity. It validates totals and writes structured data directly into your CRM.
What happens if a value is unreadable or missing?
If Heron detects missing or unclear data, it flags the issue and routes the file to an exception queue. Operations teams can review and correct the record manually.
Can parsing handle handwritten or scanned documents?
Yes. Heron’s models can interpret scanned or slightly distorted files, though higher-quality submissions yield better accuracy.
How is parsed data verified for accuracy?
Heron applies validation logic, such as ensuring assets equal liabilities plus equity, and highlights discrepancies for review. Each parsed field carries a confidence score for transparency.
What improvements can teams expect from automated parsing?
Most Heron users report reducing manual entry time by over 85 percent and achieving 98 percent accuracy on parsed data. Underwriters receive ready-to-analyze balance sheets without manual prep.
