The purpose-built AI platform can detect complex subrogation recovery opportunities that can slip through a regular triaging process.
Any claim dataset. Any level of complexity. Any stage of transformation.
To succeed at an end-to-end digital claims closure without escalating costs, insurance companies need a strong functional system that allows seamless data ingestion throughout the claims lifecycle to detect recovery potential and potential subrogation. Our AI is trained to extract relevant facts, apply applicable state laws, and then leverage a deep learning model to predict damage severity and subrogation.
learn the idiosyncrasies of
your claims data, so the predictions
are more accurate for your business
in our private secure cloud
environment and do a one-time
connect to your claims data
receiving predictions and scores for
your claim, with minimally invasive
integration to your existing systems
predictive scores. Optionally, login to
our secured portal to gain more insights.
Digital Subrogation Prediction in Three Effortless Steps
historical and real-time data
to separate claims into groups
with similar characteristics
at the FNOL.
Insights driven Subrogation
Detection and Recovery
related documents using the
rule-driven model to detect
subrogation opportunities
without any manual effort spent.
Unstructured Data Discovery
enabling Subrogation
Detection and Recovery
prioritize and transmit these
claims to subrogation
specialists for faster and
timely closure.
Segmentation
Processing
Settlement
Our advanced, multi-pronged deep learning approach uses image recognition in incidents, pattern detections in structured and unstructured data and uses supervised and unsupervised learning for initial training and then reinforcement learning for continuous training for enhanced predictions over time.
