Loss runs are key insurance documents that outline a merchant’s claim history and policy performance. For MCA brokers and funders, correctly identifying these files ensures that underwriting teams receive the right data at the right time.
Yet, brokers often receive insurance documents in mixed batches, including policy pages, certificates, and claim summaries. Manually distinguishing between these consumes valuable hours and introduces costly mistakes.
Heron automates classification for loss runs, instantly identifying each document type, tagging it accurately, and routing it to the correct workflow.
Whether loss runs arrive via shared inboxes, portals, or email attachments, Heron’s intelligent classifiers detect their structure, content, and context to ensure precise categorization.
This eliminates confusion, prevents misfiling, and allows underwriting teams to work with fully organized document sets from the start.
Use Cases
- Differentiate loss runs from other insurance documents: Heron identifies key terms and patterns like “claims summary,” “policyholder,” and “loss ratio” to classify files correctly.
- Detect carrier-specific templates: Recognizes layouts from major insurers even when formatting varies, ensuring accurate tagging across submissions.
- Separate multiple documents in a single PDF: When brokers combine policy documents and loss runs, Heron splits and labels them individually.
- Tag coverage periods automatically: Extracts and applies date-based labels (e.g., “2021–2023 Loss Run”) to simplify downstream tracking.
- Flag non-loss-run uploads: Detects unrelated documents, such as invoices or IDs, and routes them elsewhere.
- Auto-route to underwriting: Correctly classified loss runs move directly to underwriting queues or completeness checks.
Each use case reduces manual sorting, strengthens data organization, and ensures underwriting starts with the right files.
Operational Impact
Automated classification transforms operational workflows by replacing subjective judgment with consistent logic.
- Speed: Classifies hundreds of documents per hour, far beyond manual capacity.
- Accuracy: Machine learning models identify subtle textual and structural clues, keeping misclassification rates under 3%.
- Efficiency: Eliminates the need for human triage, freeing staff for higher-value review work.
- Transparency: Every classification decision includes confidence scoring and metadata for audit purposes.
- Scalability: Handles large submission surges without added headcount.
The result is a faster, cleaner document pipeline with traceable, reliable categorization.
Automation Process and Logic
Heron’s classification system relies on layered intelligence that balances structure, semantics, and confidence.
- File ingestion: Documents enter Heron through inboxes, portals, or API submissions.
- Content extraction: Optical and textual features are parsed, identifying insurer-specific headers, coverage phrases, and claim summaries.
- Classification scoring: Each file receives a confidence score based on language patterns, layout structure, and key field presence.
- Result validation: If confidence is below the threshold, the document routes to a human-in-the-loop queue.
- Tagging and routing: Confirmed loss runs are tagged, renamed, and sent to underwriting.
This process replaces tedious visual verification with machine-speed precision.
Data Integrity and Document Hygiene
Accurate classification not only speeds workflow but also safeguards data reliability.
- Consistent file naming: Heron applies standardized naming conventions across all carriers.
- Duplicate detection: Identical loss runs are recognized and suppressed before routing.
- Policy alignment: Coverage details are matched to merchant and program records, preventing cross-account errors.
- Clean archiving: Documents are stored in logically grouped sets for long-term retrieval.
Each layer of structure reinforces audit readiness and reporting consistency.
Cross-Team Collaboration
Properly classified loss runs allow seamless collaboration between brokers, underwriting, and risk management.
- Faster underwriting start: Underwriters can immediately analyze verified loss runs instead of searching through mixed uploads.
- Reduced email clarification: Clear tagging eliminates repetitive questions about which file belongs to which merchant.
- Improved broker experience: Brokers see their submissions organized instantly, giving them confidence that data is being handled properly.
- Unified document sets: All related materials are automatically grouped, simplifying downstream use for funding or audit.
Teams communicate more effectively when documentation is reliable and uniformly structured.
Analytics and Performance Measurement
Classification automation creates measurable performance improvements across operations.
- Turnaround time: Document triage time drops by over 80%.
- Accuracy rate: Correct identification rates typically exceed 97% after training on key templates.
- Exception rate: Less than 5% of documents require manual intervention.
- Processing throughput: High-volume queues process up to 5x more submissions per day.
- Operational savings: Staff hours once spent on sorting can be redirected to underwriting readiness and exception handling.
These metrics translate directly into shorter funding cycles and greater pipeline visibility.
Implementation Best Practices
Teams can maximize the impact of classification by setting up the right controls early.
- Curate sample datasets: Gather examples from major insurance carriers to train recognition accuracy.
- Define labeling standards: Align naming conventions with underwriting and CRM taxonomies.
- Adjust thresholds over time: Start conservatively, then expand automation as confidence increases.
- Integrate exception workflows: Route low-confidence classifications to a monitored queue for fast human review.
- Monitor classifier performance: Track precision, recall, and false positives weekly to sustain reliability.
These best practices turn automation into a stable, improving system that learns continuously.
Compliance and Security Considerations
Handling insurance documents involves sensitive financial and identity data. Heron safeguards it through robust compliance frameworks.
- SOC 2 Type II standards: Every document interaction is encrypted and logged.
- Access control: Role-based permissions restrict sensitive file visibility.
- Audit logging: Each classification carries a timestamp and a record of processing actions.
- Redaction support: Optional redaction filters remove claim numbers or policyholder details before external sharing.
Compliance alignment allows Heron’s automation to meet both operational and regulatory expectations.
Business and Strategic Outcomes
By automating classification, Heron builds a cleaner data ecosystem that accelerates every subsequent step in the deal process.
- Improved underwriting readiness: Correctly tagged loss runs reach underwriters faster.
- Cleaner CRM data: Automated tagging ensures consistent reporting fields.
- Scalable infrastructure: Automation handles growth without adding personnel.
- Predictable SLAs: With processing times reduced and accuracy stabilized, turnaround promises become reliable.
- Enhanced client trust: Brokers and partners experience faster, error-free processing every time.
Automation maturity compounds; each accurate classification contributes to higher-quality decisions downstream.
Benefits of Using Heron for Classifying Loss Runs
- Speed: Processes large document batches in seconds.
- Accuracy: Machine-learning models trained for insurance layouts maintain near-perfect reliability.
- Consistency: Every loss run follows uniform formatting and routing logic.
- Scalability: Handles spikes in carrier submissions without sacrificing accuracy.
- Transparency: Confidence scores and audit logs accompany each classification.
Heron transforms manual document sorting into a predictable, automated backbone for underwriting.
FAQs About Classify for Loss Runs
How does Heron recognize a loss run among other insurance documents?
Heron scans for carrier logos, claim history tables, and coverage date patterns that indicate a loss run. It uses both textual and layout cues, allowing accurate recognition even when file names are generic.
What happens if a document includes multiple sections or mixed formats?
Heron splits the file into segments, tagging each section separately. For example, a single PDF containing a loss run and policy declaration is divided into two correctly labeled documents.
Can Heron classify loss runs from unfamiliar insurers?
Yes. Heron’s models generalize based on shared insurance terminology and structural patterns. New templates are auto-learned over time through supervised feedback loops.
How does Heron handle low-confidence classifications?
Documents below the defined confidence threshold move to a review queue. Reviewers can correct and approve classifications quickly, improving model performance continuously.
How do automated classifications affect CRM updates?
Every classified loss run attaches to the correct record in the CRM with proper tags and metadata. This alignment ensures that underwriting and reporting systems always reflect accurate document counts and types.
