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