Halo-Based Fraud Detection Eliminates Fraud Losses Video Transcription
Almost all fraud detection systems require insurance companies to experience significant losses before fraud can be detected. This is what’s referred to as labeled data, or identifying which claims are fraudulent or not fraudulent in historical data.
Why Aren’t Predictive Modeling Technologies Enough?
Using only this type of predictive modeling technology leaves insurers open to experience significant losses before they can be avoided. At Daisy, we use reinforcement learning to detect new fraud patterns the first time those patterns occur. We call this identifying the Halo, claims which are out of the ordinary. Halo-based fraud detection eliminates fraud losses before they occur. At Daisy, we see what others don’t.