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SoluLab builds AI claims automation for carriers, MGAs, and insurtech startups β from ACORD extraction to audit trail, with a human sign-off on anything low-confidence or fraud-flagged.
Our clearest published result from an insurance AI engagement is from Ambetter Health Insurance: automated underwriting processes improved accuracy by 90%, and the same engagement prevented 85% of fraudulent claims. That's a real, verifiable result β and it's an underwriting-accuracy and fraud-detection figure, not an ACORD-extraction-specific benchmark, and this page isn't going to blur that distinction.
Independent benchmarks and vendor-reported figures across the ACORD-extraction industry show a consistent pattern: accuracy depends heavily on document quality and field type, not a single flat number.
| Document / Field Type | Typical Accuracy | Context |
|---|---|---|
| System-generated ACORD forms (clean PDFs) | 99%+ | Consistent across multiple independent sources |
| Simple text fields (names, addresses) | 97β99% | Field-level, production data |
| Numeric fields (limits, deductibles) | 95β98% | Field-level, production data |
| Dates | 92β96% | Varies by format consistency |
| Handwritten annotations / faxed copies | 70β85% | Highly variable by legibility and scan quality |
| Overall blended field-level accuracy in production | 93β96% | After a system has processed a few hundred forms |
A realistic, defensible configuration β not a specific SoluLab-proprietary number, but the industry-standard pattern we build toward β routes by confidence score:
Auto-accepted, flows straight through to validation without a manual touch.
Flagged for review β a human confirms or corrects before the field moves downstream.
Escalated immediately to manual entry or review rather than risking a wrong value entering the system.
The same logic extends downstream: any fraud-flagged claim, any high-value submission, and any case where extraction confidence was low gets a human decision-maker β extraction and automation compress the work around that decision, they don't remove it.
SoluLab partnered with Ambetter Health Insurance to automate claims processing and strengthen fraud detection within their existing insurance operations.
Book a technical discovery call. We'll walk through your actual submission or claims pipeline, where extraction and automation realistically help, and where a human should keep the final call.
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See how CBSE schools can benefit from AI-powered learning ecosystems to improve teaching, personalize learning, automate tasks.
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