Description: Natural language processing (NLP) or natural language technologies (NLT) enable an intuitive form of communications between humans and systems. Specifically, NLP includes computational linguistic techniques aimed at parsing and interpreting (and sometimes generating) human languages. NLP techniques deal with the pragmatics (contextual), semantics (meanings), grammatical (syntax) and lexical (words) aspects of natural languages. The phonetic part of speech recognition is often left to speech- processing technologies, like speech to text.
Sample Providers: Amazon Comprehend; Google Cloud Dialogflow; IBM Watson Assistant; Microsoft Language Understanding (LUIS); Nuance; SAS
Range: Short (1 to 3 Years)
Gartner estimates that NLP use is also at 20% to 60% of the early majority target in insurance (see Note 1). Although a significant number of deployments have previously been observed, these are often for chatbots and speech recognition, and many of these deployments are likely to be narrow scope, decision-tree systems and not capable of true dialogue capabilities. NLP has been utilized and deployed inside P&C personal or commercial business units to extract content from large documents, while adoption in group life business, in particular, appears to be lagging. Content discovery for documents — including inspection reports or large policy documentation — is a rising use case that is gaining momentum in markets where AI adoption has typically been low. NLP has many emerging applications, too, in the contact center, for example, to assist in sentiment analysis. Specific use cases in the insurance industry include policyholder authentication, delivering policyholder assistance services, content extraction/discovery, and automating policy customization and personalization. In P&C commercial lines, NLP can be used to automate the process of reading property inspection surveys, which can run into hundreds of pages for complex specialty risks and global program business. This is a time- consuming aspect of the underwriting and rating process. Common challenges faced when using NLP include extracting semantic meanings in conversations, building vocabulary and generating dependency graphs from tagged parts of speech.
Mass: High
Gartner, Inc. | G00786204
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This research note is restricted to the personal use of abhishek.sharma@fractal.ai.
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