Unlocking efficiency: Transformed claims processing with generative AI (MAM228)

Unlocking Efficiency: Transforming Claims Processing with Generative AI

Introduction

  • Doug Armstrong, Head of Data Science at Securian Financial, and Prithvi Mazumder, Technology Fellow at Deloitte, present a collaborative effort to improve claims processing using generative AI.
  • The goal is to share the story of how they identified, understood, and solved the problem, as well as the technology considerations and benefits of the solution.

The Problem: Manual Claims Adjudication

  • Claims adjudication is a highly manual process, particularly for a product like hospital indemnity insurance.
  • The process involves several steps:
    1. Claimant submits claim and uploads documents
    2. Examiner reviews documents to capture key data points
    3. Examiner compares data to rules and approves/rejects claim
    4. Decision is communicated, and payment is issued

Data Challenges

  • Data is captured in various random formats (e.g., tilted, rotated images)
  • Data context varies across different hospital/provider documents
  • Documents can be in different languages
  • Inconsistent scanning and conversion to images

Technology Selection and Implementation

  • Collaborated with Deloitte and internal experts to identify core capabilities needed:
    1. Ingestion
    2. Text extraction
    3. Insight derivation using large language models
    4. Applying rules and making recommendations
  • Leveraged AWS services to build a secure, scalable solution.
  • Focused on maintaining a "human in the loop" approach, with the AI providing summary information to the claims adjudicator.
  • Designed the solution to be repeatable and reusable across the enterprise.

Benefits and Lessons Learned

  • Reduced document completeness check time from several minutes to under 5 minutes.
  • Improved customer satisfaction, with claims adjudicators now "disappointed" when they don't see the AI-generated summary.
  • Enabled potential for triage and automation of simple claims in the future.
  • Importance of collaboration, communication, and including all stakeholders in the process.
  • Significance of maintaining a "human in the loop" approach, with the AI supporting and enhancing the claims adjudicator's work.

Demo Simulation

  • Demonstrated a simulated claims adjudication process, highlighting the integration of the claimant statement and hospital discharge summary, automated data extraction, and application of exclusion rules.
  • Showcased a feature that allows the claims adjudicator to view similar historical claims and their resolutions, helping to train new adjudicators.

Next Steps and Conclusion

  • Continued exploration of opportunities to further automate and optimize the claims processing workflow.
  • Emphasis on maintaining the secure and privacy-focused foundation for all data and technology initiatives.
  • Overall, a successful collaboration and a compelling story of leveraging generative AI to transform a manual, labor-intensive process.

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