Extract commission statements to structured JSON
Commission statements detail what an agency or broker earned on each policy. Carrier formats, calculation methods, and reporting periods vary widely. Sensible normalizes commission data into consistent JSON for reconciliation, accounting integration, and producer compensation.
Why commission statements defeat standard parsers
Carrier-specific formats, complex split tables, and inconsistent labeling make this a normalization problem.
Column headers, transaction codes, and calculation methods differ by carrier. One uses 'NB' for new business; another spells it out. Sensible normalizes these variations into a single validated schema.
Agency statements split commissions across producers, sub-agents, and override tiers. MGA reports add contingency bonuses and profit-sharing. Sensible's extraction config captures each tier with its rate, amount, and hierarchy level.
Statements include original commissions, adjustments, chargebacks, and overrides that reference previous transactions. The parent-child relationship between an original commission and its adjustment is implied by position or code, not explicitly structured. Sensible's extraction preserves these relationships so your reconciliation system can trace every adjustment to its source.
Fields we extract
Configure the extraction to match your reconciliation schema. Add custom fields as needed.
Policy number, insured name, effective date, line of business, carrier, premium, transaction type (new/renewal/endorsement/cancellation)
Commission rate, commission amount, override percentage, override amount, contingency bonus, net commission, payment method
Statement period, total premium volume, total commission earned, adjustments, prior balance, amount due, payment date
Internal agency report tracking commissions across carriers and producers.
Managing General Agent commission detail with override and contingency splits.
Monthly or quarterly commission report from an insurance carrier to an agency.
Supported commission formats
Sensible processes commission statements from any carrier, MGA, or wholesale broker. New formats can be mapped to your reconciliation schema using SenseML's hybrid extraction approach.
Direct carrier statements, MGA commission reports, wholesaler statements, program administrator reports
Summary statements, policy-level detail, transaction-level detail, producer hierarchy reports



Common Questions
Answers about commission reconciliation, carrier formats, and split tracking.
Sensible captures policy number, insured name, premium, commission rate, commission amount, effective date, transaction type, and payment details per line item.
Yes. Carrier commission statements vary in format and detail level. Sensible extracts commission data from any carrier format into a consistent output schema.
Yes. Sensible extracts agent, sub-producer, and override commission splits. MGA statements with multiple commission tiers are parsed into nested structures.
Yes. Sensible sends extraction results to your webhook endpoint when processing completes. You can also poll the API for status.
Yes. Sensible flags extractions with low confidence for human review. You can configure review thresholds and workflows.
Sensible is SOC 2 Type II certified and HIPAA compliant. Data is encrypted in transit and at rest.
Document data is stored indefinitely by default. Custom retention policies are available and can be configured for same-day deletion if needed.
Yes. Sensible offers a 14-day free trial on the Growth plan. No credit card required to start.
Sensible uses per-document pricing for predictable costs. No token-based billing or usage surprises. Volume discounts are available for higher throughput.
Sensible provides REST APIs and SDKs for Python and Node.js. Most integrations take a few hours. Webhooks, Zapier, and direct API calls are all supported.
Sensible processes PDFs (native or scanned), Microsoft Word (DOC, DOCX), spreadsheets (XLSX, XLS, CSV), single-page images (JPEG, PNG), multi-page images (TIFF), and email bodies with attachments.
Accuracy depends on document quality and configuration. Most production deployments achieve 95%+ accuracy with proper validation rules and confidence signals.
Processing speed depends on document size, page count, OCR requirements, and which extraction methods are used. Simple single-page documents process in seconds. Larger or more complex documents that use LLM-based extraction take longer.
