Claims processing, document extraction, fraud detection, and financial document annotation powering next-generation InsurTech and FinTech AI models.
Document types cataloged (claims, policies, invoices, KYC, statements); extraction schemas defined per document type with field names, data types, validation rules, and cross-field dependencies.
Automated PII detection (SSN, account numbers, DOB, addresses) applied before annotation. Sensitive fields masked per compliance requirements; annotators access only necessary data.
Document regions classified (header, table, signature, stamp, handwritten notes). Multi-page documents linked with page-level and document-level annotation consistency.
Named entities extracted: policy numbers, claim amounts, dates, names, addresses, medical codes (ICD-10, CPT). Values normalized to standardized formats with confidence indicators.
Suspicious patterns annotated: document tampering indicators, inconsistent signatures, duplicate claims, unusual transaction patterns. Fraud taxonomy covers 25+ indicator types.
Labeled data delivered with PII handling documentation, SOX/PCI-DSS compliance reports, and audit trails. Data retention and destruction policies enforced per regulatory requirements.
Generic annotation vendors can label data. Domain experts label it correctly. Here's why the difference matters in your industry.
Insurance policies span 20+ pages with tables, riders, endorsements, and handwritten annotations. Our annotators understand document structure — distinguishing a premium table from a coverage exclusion, a co-pay from a deductible. This structural understanding is what separates 98.7% accuracy from 85%.
Document tampering, inconsistent signatures, and duplicate claims follow patterns that generic annotators miss. Our fraud labeling taxonomy covers 25+ indicator types developed with insurance fraud investigators — from pixel-level manipulation detection to cross-document inconsistency flagging.
Financial data is governed by SOX, PCI-DSS, GDPR, state insurance regulations, and banking laws. Our workflows include jurisdiction-specific PII handling, data retention policies, and compliance documentation — ensuring your AI training data meets regulatory requirements across markets.
See how our domain-specific capabilities compare to generic annotation services.
| Capability | UTL Data Engine | Typical Vendor |
|---|---|---|
| 30+ document type extraction schemas | Per-type schemas | 5–10 generic types |
| Handwritten text + degraded document support | Multi-script OCR | Printed text only |
| PII detection & masking before annotation | Automated + manual | Manual only |
| Fraud pattern taxonomy (25+ indicators) | Domain-developed | Basic fraud flags |
| SOX & PCI-DSS compliance documentation | Included | Not available |
| Multi-currency & multi-language support | 20+ currencies, 15+ languages | English, USD only |
"UTL's team understood the nuances of insurance claim documents — from handwritten adjuster notes to multi-page policy forms. Their accuracy on entity extraction was outstanding."
Let's discuss your specific data challenges and build a tailored annotation pipeline.
