Case Study: BFSI
Subrogation is tedious waste of resources
Subrogation potentiality identification is a tedious, manual and expensive process if not automated.
For a leading end-to-end payment integrity organization operating in the healthcare insurance domain we automated their Subrogation Potentiality using Natural Language Processing (NLP) and Machine Learning Algorithm (ML) to understand the context and solve it through Subrogation Potential by answering What is damaged? What caused it? And Who is responsible?
This information is a Digital Format of the adjuster notes having concepts as Vectors embedded into a Document Vector (DV). The DV is further used to determine subrogation potentiality using ML which continuously identifies and learns new trends on existing and new datasets thereby increasing engine accuracy.
Automated workflow has increased the assessment speed, led to higher throughput, reduced manual intervention and brought down processing cost.