(Image source: Equian product video.)
Equian, an Indianapolis-based provider of property/casualty payment integrity solutions has announced that its Data Analytics Division has created a natural language processing (NLP)-based AI platform, EquianAI, built exclusively for the P&C subrogation market. Key automation features of the solution’s data engine include digital robotics and machine learning, predictive analytics, and mobile technology, according to the vendor.
Calling EquianAI the first such NLP- and AI-based solution of its kind, Equian reports that, after piloting and testing the platform, it has proven that it can:
- eliminate leakage associated with subrogation identification,
- reduce false positives up to 36 percent,
- enable productivity gains up to 20 percent,
- and improve cycle time over 30 percent.
Equian’s announcement on the new platform asserts that EquianAI’s greatest impact is on improving operational performance and recovery cycle times. The vendor says the application easily integrates into existing workflows with minimal technology resources required. Equian says its subject matter experts worked with its Analytics Division to develop the model. The vendor’s Analytics Division utilized Equian’s data repository of property/casualty subrogation data to formulate a unique set of algorithms to predict outcomes and improve the existing process, according to Equian’s announcement.
Property/causalty organizations have unique challenges when it comes to subrogation, notes Sam Cooper, Senior VP, Client Solutions, Equian. “Most carriers rely on manual evaluation of free text with some predictive analytics on a limited amount of structured data to identify losses with subrogation opportunity,” he says. “EquianAI provides automation that removes tedious human intervention by analyzing the free text, the structured data, and the unstructured data simultaneously.”
Critical Part of the Subrogation Payment Integrity Value Chain
Equian’s technology performs in-depth analyses that result in the ability to prioritize claims by scoring recoverable files as High, Medium or Low based on recoverability, Cooper adds. Rules based technology then directs the scored cases to appropriate investigation and recovery experts.
“As a result of our experts’ deep dive, we found significant areas of improvement could be made by deploying technology logic as a critical part of the subrogation payment integrity value chain,” comments Gary Liter, President, Subrogation Division, Equian. “Key automation incorporated into our data engine that drives our successful outcomes includes Artificial Intelligence, Digital Robotics and Machine Learning, Predictive Analytics, and Mobile Technology.”