case study

Auto Claims Adjudication

Insurance

Business Impacts

Reduced turnaround time to process claims within one day

Cost Saving and Operational Efficiency

Improved Customer Satisfaction

Customer Key Facts

  • Location : North America
  • Industry : Insurance

Problem Context

Customer, an American insurance company providing financial protection to more than 50 million customers worldwide, processes ~13M claims annually. The need for manual review of unlabeled and semi-structured documents made the data gathering process slow, inconsistent, and difficult to align with business priorities.

Challenges

 

  • Information extraction from unstructured documents
  • Input data preparation from legacy databases

Technologies

Cloud Storage

Cloud Storage

ML Engine

ML Engine

BigQuery

BigQuery

Tensorflow

Tensorflow

Solution

Quantiphi built an end-to-end Claim Adjudication Platform to extract, classify, annotate and index the documents submitted for proof of loss and other relevant information. The platform further leverages predictive analytics for claims decisioning and conversational AI for customer communication.

Result

  • Simplified and Digitized Claims Capture
  • Reduction in Error Rates across Claims Paid

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