Solutions

Data annotation for insurance

Xana labs offers insurance innovators a solution to improve their claims processing and customer service through high-quality medical data annotations. With unparalleled speed and scale, Centaur Labs provides efficient and accurate annotations that can streamline the reimbursement process and ultimately enhance the overall customer experience.

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Use Cases

Leveraging AI to reduce cost for insurance providers, and save time and cost for covered entities.

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Claims processing

AI-enabled claims processing saves cost for insurance providers, and improves the customer experience by accelerating reimbursement.

  • tick Image analysis
  • tick Claims adjudication
  • tick Preauthorization
  • tick Fraud identification
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Underwriting

New information from wearables and edge devices allows insurance providers to expand insurability, and improve services and pricing.

  • tick Risk assessment model improvement
  • tick Expanding insurability
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Customer service

AI-enabled workflows can accelerate and route customer questions, and offer recommendations to improve their coverage.

  • tick Chatbots
  • tick Question routing and escalation
  • tick Customer recommendations

Why Xana?

XANA accelerates AI development with speedy access to millions of diverse data points. Rooted in quality, it ensures consistent performance, advanced insights, and top-notch privacy across various industries and data types.

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Scale data annotation exponentially

Stop annotating data slowly with in-house teams or outsourcing to teams whose quality you can't control.
Access the Centaur Labs network of thousands of medical doctors, professionals, researchers and students for skilled annotation

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Take control of annotation quality

You define quality. We rigorously measure and manage it.
Incentivize labeler performance with small batch, mobile-first competitions. Labelers are only paid for performance and give 100% of their effort on every case

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Master your dataset

Proactively understand the nuances of your dataset, so your data works with you, not against you.
Leverage case-level insights to inform model development e.g. precision-recall curves, label distribution, labeler agreement

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