Health Insurance & Payers
Reducing claims fraud, improving risk models, and automating prior authorisation with AI
Industry Challenge
Health insurers and payers manage enormous volumes of claims, prior authorisation requests, and medical records. AI can automate claims processing, detect fraud, improve risk adjustment, and accelerate prior authorisation — but these models need accurately labeled training data drawn from clinical and administrative records, annotated by professionals who understand both clinical coding and insurance processes.
How SCILabel Serves This Industry
Data Collection
We source de-identified claims datasets, prior authorisation records, explanation-of-benefits documents, and appeals correspondence from insurance research partnerships. All data is handled under HIPAA Business Associate Agreements.
Data Annotation & Labeling
Our clinical coders and health informatics specialists annotate claims with ICD-10-CM, CPT, HCPCS, and DRG codes, flag potential upcoding or unbundling errors, label prior authorisation clinical appropriateness, and annotate fraud indicators in claims patterns.
Data & Model Evaluation
Evaluators benchmark claims AI model coding accuracy against certified coder ground truth, assess fraud detection precision and recall against known fraud case sets, and evaluate prior authorisation model clinical appropriateness against physician reviewers.
Annotation Types & Formats
- ICD-10-CM, CPT, HCPCS, and DRG coding annotation
- Claims error and upcoding flag annotation
- Prior authorisation clinical appropriateness labeling
- Fraud indicator annotation on claims patterns
- Explanation-of-benefits document classification
Specialist Workforce Tracks
Track 1 (Medical NLP): Clinical Coders, Health Informatics Specialists, Nurses, Pharmacists with coding experience.