Published:
May 10, 2020
Categories:
AI, Machine Learning, Natural Language Processing, Data Analytics, Healthcare, Insurance
Client:
Afrocentric

Project Brief

D uplicate queries cause enormous backlogs as agents spend more time trying to validate queries and identify duplicates in order to avoid meaningless repetition of work on duplicate cases. The automated identification and deletion of duplicates ensure agents are not being assigned duplicate cases and utilize their time more effectively.

Project Outcome

We developed a duplicate tracking tool that leverages our Natural language Processing and machine learning framework to process millions of queries to identify duplicates and prevent new queries related to existing queries from being created.

Duplicate identification: The identification and removal of duplicates to ensure agents are not being assigned duplicate cases.

Duplicate Prevention: The identification and prevention of new duplicates being added to the queue for evaluation

The implementation of our duplicate tracker allows for more queries to resolved daily and leads to a better experience for both providers and customers thus contributing to both growth and retention.