Categoría: NLP

Currently, Natural Language Processing has been having a strong impact on various sectors. Looking at the insurance industry and its security, we’ll break down the most relevant use cases.

Let’s get started! 

NLP in Insurance

Insurance Claims Management

NLP can be used in combination with OCR (character recognition) to analyze insurance claims. For example, IBM Watson has been used to analyze structured and unstructured text data to discover the right information to process insurance claims and send it to a machine learning algorithm. This is responsible for labeling the data according to the sections of the claim request form.

2.Fraud detection

NLP can be combined with machine learning and predictive analytics to detect fraud and misinterpreted information from unstructured financial documents. 

For example, a 2010 study revealed that NLP language models could detect misleading emails, which were identified by «reduced frequency of first-person pronouns and signature words, and elevated frequency of negative emotion words and action verbs.» 

NPL in cybersecurity

1. Spam detection

NLP models can be used for text classification to detect spam-related words, sentences, and sentiments in emails, text messages, and social media messaging applications.

Likewise, spam detection NLP models usually follow these steps:

• Data cleaning and preprocessing: elimination of padding and empty words.
• Tokenization: sampling of text into smaller sentences and paragraphs.
• Part-of-speech (PoS): taggingTag a word in a sentence or paragraph to its corresponding part of a speech tag, based on its context and definition.

2.Prevention data exfiltration

 Exfiltration data is a violation of security involving copying or unauthorized transfer of data from one device to another. To exfiltrate data, attackers use cybersecurity techniques such as Domain Name System (DNS) tunnels.

What does this mean? DNS queries that reflect a request for information sent from a user’s computer (DNS client) to a DNS server. Also sending phishing emails that leads users to provide information to hackers. 

Finally…

Here at LISA insurtech we have acquired knowledge in NLP in order to offer the insurance industry the ability to operate effectively and offer an ideal service to its clients.

How do we do it?

• Through chatbots by allowing the complaint to be processed in a simple and fast way.
• We automatically translate all documents.
• We look for certain patterns within the texts that allow us to identify the critical data in the documents. 
• We detect the «feeling» found in the description of an event and the state of mind of the person who makes said description.
• We search based on keywords. And it is used in the health field to determine amounts to be compensated depending on the illness/injury.

Currently, Natural Language Processing has made a big difference in many companies that have opted for a heavier technology presence.

This article aims to teach the use cases of the most significant NLP for various industries.

Let us begin!

According to the article AIMultiple, these are the main uses of Natural Language Processing:

01. Translation

The first NLP-based translation machine was introduced in the 1950s by Georgetown and IBM. This was capable of automatically translating 60 sentences from Russian to English.

Translation applications today leverage NLP and machine learning to understand and produce accurate translation of global languages ​​in text and speech formats.

2. Autocorrect

It is used to identify a misspelled word by comparing it with a set of relevant words in the dictionary of the language used as the training set. 

The misspelled word is then sent to a machine learning algorithm that calculates the distance of the word from the correct words in the training set, adds, removes, or replaces letters in the word.

3. Autocomplete

Combines NLP with certain machine learning algorithms (for example, supervised learning, recurrent neural networks (RNN), or latent semantic analysis (LSA)). This is in order to predict the probability that a following word or sentence completes the meaning.

4. Conversational AI Conversational

AI is the technology that enables automatic conversation between computers and humans. In simple words, it is the heart of chatbots and virtual assistants like Siri or Alexa. 

Conversational AI applications rely on NLP and intent recognition to understand user queries, drill down into their training data, and generate a relevant response.

5. Automated speech/speechSpeech

Recognition, also known as automatic speech recognition (ASR) and speech-to-text (STT), is a type of software that converts human speech from its analog form (acoustic sound waves) into a digital form that can be recognized by machines. 

Today, smartphones integrate voice recognition with their systems to perform voice searches (for example, Siri) or provide more accessibility to text messages. 

Finally, Natural Language Processing is a technological technique that has a wide range of uses. This has favored a large number of companies and has made them more efficient and competitive.

Taking advantage of the technological wave and all the benefits that we can extract from it, at LISA insurtech we have acquired knowledge in NLP in order to offer the insurance industry the ability to operate effectively.

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In our next article, we will be breaking down how NLP is applied in insurance and cybersecurity

Nowadays we all want machines to talk, and the only way a computer can talk is through Natural Language Processing (NLP). 

A clear example of this is Alexa, an Amazon product. A query is passed to it by voice, and it can respond by the same means, i.e. voice. It can also be used to ask anything, search for anything, play songs or even book a cab.

However, Alexa is not just one example, and these talking machines that are popularly known as chatbot can even manage complicated interactions and optimized business related processes using only NLP. In the past, chatbots were used only for customer interaction with limited conversational capabilities because they were usually rule-based, but after the emergence of Natural Language Processing and its integration with machine learning and Deep Learning, now the chatbot can handle many different areas, such as Human Resources, healthcare among others.

Now let’s take a brief look at other PLN use cases. Here are a few that xenonstack shares in his article:

• NLP in healthcare: in this case we will be able to make a prediction of different diseases by using pattern recognition methods and the patient’s speech and electronic health record. An example of this is Amazon Comprehend Medical.
• Sentiment Analysis using NLP: Sentiment analysis is very relevant, since it has the ability to provide a great amount of knowledge about the customer’s behavior and choices, which can be considered as an important decision factor.
• Cognitive Analytics and NLP: Using NLP, conversational frameworks have the ability to take commands through voice or text. By using cognitive analytics, it is possible to automate different technical processes, such as the generation of a technical ticket related to a technical problem and also its handling in an automated or semi-automated way.

The collaboration of these techniques results in an automated process of handling technical problems within a company. It can also provide the solution of some technical issues to the customer in an automated way.

• Spam detection: Google and Yahoo are known to use NLP to classify and filter emails suspected of being spam. This process is known as Spam Detection and Filtering. This results in an automated process that can classify email as spam and stop it from entering the Inbox.
• NLP in Recruitment: NLP is also used in both search and screening phases of job recruitment, even, chatbot can also be used to handle the job related query at the initial level. This also includes identifying the skills required for a specific job and handling entry level tests and exams.
• Conversational Framework: NLP and related devices are gaining a lot of popularity these days. Alexa, is one of them, also Apple’s Siri and Google’s Ok Google, which are examples of the same type of technology use cases.