Linking AI Initiatives to Real-World Outcomes

Use Case

FRAUD DETECTION: MITIGATE RISK & BOOST CONVERSION

 
Client

A major payer.

Problem

The client was in need of identifying claims fraud using a systematic approach. They wanted to manage fraud and boost conversion with the accuracy of machine learning.

Methodology
  • Combination of Deep Learning based professional service and modularized AI components used according to organization’s cognitive maturity
  • Business transformation with smarter cognitive outcomes
  • Adding industry, business and customer insights to the value chain
  • Seamlessly integrate data across functions
Solution
  • Developed a fraud detection tool that:
 
  • Uses state-of-the-art machine learning techniques such as Bayesian Networks and Link Analysis
  • Uses Storm and Spark to run models on large data volumes
  • Predicts the likelihood of fraud for a claim
  • Deployment of real-time information sharing across physicians, clinics, members, and payer organizations, pattern identification to detect potential frauds towards claims.
  • Payer analytics used interoperability data analysis to track and mitigate potential frauds before realization
Benefits:
  • The payer increased fraud recoveries by over 30%
 
 

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