Exploring Advanced Techniques in Natural Language Processing and Machine Learning for In-depth Analysis of Insurance Claims

Insurance sector has been growing dynamically within the last few years. Premium calculation may be a challenge for insurance industry because it is main a part of financial function and financial and monetary flow. Correct calculation of net premium, loss prevention and risk premium may be a challenge for every actuary. Heavy regulations and sophisticated legacy systems are the most hurdles to adoption of digital processes. An insurance policy chat bots is introduced which processes both structured and unstructured information associated with healthcare insurance policy. The application makes use of a natural language processing (NLP) engine, together with application-specific knowledge, written in a concept specification language and machine learning. The complexity of policy wordings the contract coverage, endorsements, exclusions, terms and conditions embedded within contracts has grown exponentially. Clarity in policy terms and conditions that reduce disease coverage ambiguity, suspected fraud, reduce costs and save time to acquire policy for both insured and policyholders. The NLP and Semantic Analysis proposes extracting semantic information by using templates, in which fields can be filled using a simple common name is expected while in others, a sentence or adjectives. In Bag of Words, representation of the text consists in representing each document by counting words occurrences according to a dictionary. The resulting counting vectors dimensions then represent a word from the dictionary. The idea consists in simply summing the canonical vectors related to the words making up the sentence. But it has the disadvantage of not presence of sparse and articles, linking words or stop words are often over-represented by this method.