Natural language processing (NLP) is the technique to provide semantics to information extracted from optical character recognition engines and documents. In this report, we progress from understanding the mechanics of extracting data from unstructured documents with image recognition towards a deeper understanding of information understanding through NLP. We will look at the use cases in insurance, challenges, and tools and application.

This report will also explore the application of innovative research in NLP, such as pre-trained language model Bidirectional Encoder Representations from Transformers (BERT) and Generative Pretrained Transformer (GPT).

NLP is further propagated in natural language understanding (NLU) and natural language generation (NLG). NLU is a topic of artificial intelligence (AI) that uses computation to understand input, the form of sentences in text or speech format. NLU enables a more intuitive human-computer interaction (HCI) experience by allowing humans to speak to a computer directly. NLG is the computer’s understanding of spoken or written input into useful answers, presented in a manner coherent to the user.

NLP has potential in providing improved customer experience through applications such as text classification and virtual customer assistants. We can expect further innovation in a conversational chatbot that is able to understand specific domain terminology, such as financial concepts. This will help provide relevant personalization to the end user and showcase opportunities for applying a new approach in NLP to new or existing problems in insurance.

An example of an NLP Language Model BERT architecture: