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Driving Insights and Profitability through Automated Policy Docket Validation

Driving Insights and Profitability through Automated Policy Docket Validation

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Driving Insights and Profitability through Automated Policy Docket Validation

GOAL

Most mid-sized and large insurance companies process policy data valued at nearly USD 1 billion annually. This involves high document volumes during the insurance life cycle from quotation to final claim. The IT environment to support this typically involves document management platforms that are tightly integrated with core platforms to facilitate policy administration, billing, and claims.

These document solutions are essential to generate large policies, claims, and billing dockets. However, the manual process of policy document verification is onerous given the numerous documents involved. This adversely influences policy rates, complexity, and the verbiage used. The unique challenges faced by insurance companies are:

  • Zero room for error and the criticality of testing due to the customer-facing nature of the policy data
  • Tedious and error-prone manual processes of handling policies running into thousands of pages
  • Need for manually indexing relevant pages due to non-searchable text
  • Repetitive need for regression testing of policy

SOLUTION

A cognitive automation approach using SLICE (Self-Learning Intelligent Content Extractor) specifically leverages AI/ML-based content extraction methodology applied to diverse documents including policy dockets, images, signatures, and others. The information extracted from these key insurance documents is then manually verified for legal, operational, and regulatory correctness. The SLICE-based solution optimizes the product quality and streamlines the release process resulting in overall improvement of operational efficiency.

Foundational Steps for Content Extraction and Storage

Foundational Steps for Content Extraction and Storage

Solution Snapshot

Solution Snapshot

  • Use of Open Source technologies for cost optimization
  • Flexible modular solution that can be tailored for growth with changing TTC requirements
  • Proprietary Image Classification engine - DICE
    • Deep Learning / SVM-based models for supervised machine learning
    • Noise Removal, image quality improvement and Image Augmentation
    • Feedback capture mechanism to fine-tune the trained model

Business Process Flow

Business Process Flow.png

Technology Landscape

  • Python, Open CV, Google Tesseract, pyPdf
  • Image Classification using DICE framework

SUCCESS

  • > 30% effort optimization due to automation of the manual effort of sifting through several documents multiple times and extracting relevant insights
  • Ability to be integrated with any COTS (Commercial Off-The Shelf)-based core platforms
  • Flexibility to execute multiple automated runs in case of bringing in quick enhancements
  • Solution configurability for multiple business problems and > 2x cost savings from USD 8/trans- action

Resource Library

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