Natural Language Processing – Key Benefits and Use Cases

March 22, 2022

Natural language processing (NLP) uses artificial intelligence and machine learning to extract meaning from human language while it is spoken. Depending on the natural language programming, the presentation of that meaning could be through pure text, a text-to-speech reading, or within a graphical representation or chart.

Understanding human language, including its intricacies, can be difficult even for people, let alone algorithms. The challenge is getting the algorithms to understand the words and their underlying meaning. Machine learning is beneficial when you consider the sheer number of variables that need to be accounted for in a natural learning process application to be effective.

Benefits of Natural Language Processing

Natural language processing (NLP) is a cutting-edge development for several reasons. Before NLP, businesses were using AI and machine learning for essential insights, but NLP provides the tools to enhance data and analyze both linguistic and statistical data. NLP offers several benefits for companies across different industries.

  • Enable non-subject matter experts to find answers to their questions
  • Analyze data from both structured and unstructured sources
  • Identify the root causes of your business problems
  • Discover your most profitable customers and understand the reasons behind it
  • Identify and address fraudulent claims and behavior
  • Understand several languages, dialects, slang, and jargon
  • Identifying patterns in customer communication and reducing customer complaints
  • Analyze and evaluate your competitors’ product offerings

Use Cases of Natural Language Processing

Natural language processing is just starting to impact business operations across different industries. Here are some of the top use cases of NLP in various sectors.

Banking and Finance

Banking and financial institutions can use sentiment analysis to analyze market data and use that insight to reduce risks and make better decisions. NLP can help these institutions identify illegal activities like money laundering and other fraudulent behavior.


Insurance companies can use NLP to identify and reject fraudulent claims. Insurers can use machine learning and artificial intelligence to analyze customer communication to identify indicators of fraud and flag these claims for deeper analysis. Insurance companies can also use these features for competitor research.


Manufacturers can use NLP to analyze shipment-related information to streamline processes and increase automation. They can quickly identify the areas that need improvement and make changes to drive efficiencies. NLP can scrape the web for pricing information of different raw materials and labor to optimize costs.


Retailers can use NLP to analyze customer sentiment about their products and make more informed decisions across their processes, from product design and inventory management to sales and marketing initiatives. NLP analyzes all available customer data and transforms it into actionable insights that can improve the customer experience.


NLP can analyze patient communication from emails, chat applications, and patient helplines and help medical professionals prioritize patients based on their needs, improving patient diagnosis and treatment, and driving better outcomes.

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