Sagility delivered an intelligent machine learning solution to help improve the client’s Star rating, specifically by improving grievances and appeals efficiencies. The team addressed the entire workflow with a cognitive content processing solution, leveraging an image analytics engine to process the source. The solution featured a natural language processing (NLP) engine to analyze keywords and context in client grievances and appeals correspondence.
Documents were classified as expedited or non-expedited on the basis of rules or associate judgment. The solution leveraged an NLP engine to minimize subjectivity and improve process efficiency and addressed all types of text—unstructured, semi-structured, and fully structured. The NLP engine sent the documents to the machine learning text classifier, which tagged and queued documents for further processing. With self-learning, this cognitive solution recognized patterns of subjectivity and replicated human-associated decision-making over time.
The process re-engineering solution included the following:
- Intelligent content processing through an Sagility-developed internal image analytics engine for source and format-agnostic extraction and processing and processing of unstructured, semi-structured, or fully structured documents
- NLP engine to analyze keywords, context, and intent in the documents
- ML engine to classify the documents based on image analytics and NLP to push the documents into the required queue
After deploying the analytics and insights as-a-service solution, the innovation workflow was defined as below: