Accelerate Decision-Making With Intelligent Technologies
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
