This is like putting a smart security camera on all your insurance transactions. It watches events in real time, spots suspicious patterns that look like fraud, and alerts your team before money goes out the door.
Manual and rules-only fraud checks miss complex patterns and are too slow for high-volume, real-time insurance transactions. This framework centralizes data, monitors it continuously, and flags likely fraud so investigators can act quickly and reduce financial losses.
Tight integration of log/event data, search, and analytics in a single stack (Elastic) that becomes more valuable as an organization accumulates proprietary behavioral and claims data and embeds it into fraud detection workflows.
Early Majority
Fraud detection is implemented on top of a general-purpose search and observability platform (Elastic Stack), enabling organizations to reuse existing logging/monitoring infrastructure and skills instead of deploying a separate, specialized fraud detection product.
This is like giving an insurer a living, zoomable map of how cars and drivers behave in the real world, updated in near real time, and then using AI to spot risks, opportunities, and patterns that humans would never see by looking at tables and static reports.
This is like upgrading an insurer’s old spreadsheet-based risk calculator to a smart assistant that not only predicts which policies are risky more accurately, but also clearly explains which customer or policy features drove each prediction.
This is like an AI-powered detective and assistant that reviews insurance claims in the background, flags suspicious ones, and guides adjusters to make faster, fairer decisions.