Auto Detection of Fraudulent Claims

Overview

There is significant benefit in identifying potential fraudulent insurance claims with accuracy during the initial evaluation stage.  This application addresses this need by leveraging machine learning and predictive analytics capabilities to provide improved fraud detection results when compared to the traditional statistics-driven approach. 

High costs incurred by traditional fraud detection techniques are due to:

• Heuristic models and checklist-driven fraud indicators
• Extensive manual dependencies
• The inability to easily create a persona
• Multiple verification steps
• No comprehension of hidden and implicit correlations in data

An AI-based platform using machine learning models for suggestion and prediction makes use of past knowledge and achieves:

• Lower turnaround time for fraud detection
• Reduction in manual handoffs
• Improved efficiency

Key Features 

• Leverages machine learning and predictive analytics capabilities for calculating a fraud score for the claim
• Enables real-time processing of claims
• Optimizes models by continuously adding data thus making predictions more accurate
• Helps drive automated fraud detection by leveraging self-learning and self-healing capabilities