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Find Your Dream JobNLP use cases have grown significantly in recent years. From NLP use cases in finance to NLP in healthcare use cases, we explore the various uses for business.
Nearly all business today must deal with a vast amount of text. This could be anything from customer service feedback and social media comments to email communication, contracts, and other documents. This unstructured text data can be a gold mine of opportunities to improve, enhance, and grow the business.
In this article we will discuss what NLP is, specific NLP capabilities and use cases, and how it benefits businesses across sectors.
NLP, or natural language processing, is a way for businesses to spend less time and money to gain the critical insights they need to improve their business, with more satisfied customers and employees being just one well-known benefit. Industry leaders are already benefitting bigtime from it, so every business today must take an NLP-savvy approach to stay competitive and primed for growth.
In simple terms, NLP allows machines or computers to understand the human language. It comprises algorithms which recognise human texts, and then edit, summarise and classify them, according to the business’s unique requirements. To machines, human language is more or less unstructured text data. And, understanding it to benefit business means that the NLP algorithm must not only recognise the meaning of words, but also perceive the individual ideas and concepts behind those words, and then link them up to create meaning.
Chances are that you may already be familiar with some of NLP’s capabilities or use cases, such as chatbots, translation or autocorrection. However, beyond these general use cases, we’ll be looking at the powerful impact of NLP in specific sectors, such as NLP use cases in banking or NLP in healthcare use cases.
Before we get to that, however, it’s worth exploring some of NLP’s use case cases in general:
Language translation has been one of the top NLP use cases for a very long time. In fact, the very first machine translation driven by NLP was unveiled by Georgetown and IBM in the 1950s. It was capable of translating 60 Russian sentences automatically to English.
Today, translation applications using NLP have grown so sophisticated that with the help of machine learning (ML), they can understand as well as produce a very accurate translation of nearly any global language – and not just in text but also voice.
NLP has been used to identify misspelled words by cross-matching them against a set of relevant words in the dictionary, which is used as a training set. The misspelled word is then inputted into a ML algorithm to calculate the word’s deviation percentage from the correct one, which has already been fed into the training set. It then either removes, adds, or replaces specific letters from the word, matching it with a word candidate which best fits the intended meaning of the sentence.
Autocomplete, also known as sentence completion, uses NLP at the core, combining it with specific learning algorithms like LSA (latent semantic analysis), RNN (recurrent neural networks) or supervised learning to predict the feasibility, likelihood, or chances of inserting a following word/sentence to complete the meaning of the overall sentence.
With chatbots being the most common application, conversational AI enables auto-generated conversations between people and computers. In addition to chatbots, virtual assistants like Alexa or Siri are also a common example.
Relying on NLP and intent recognition to understand the questions or queries users put forward, conversational AI applications like chatbots refer to their training data and then generate the appropriate or desired response.
Voice recognition is a kind of software which converts human speech into a digital form so that it can be translated as text onto a document or search engine field, for example. It uses NLP and deep learning algorithms, to name a few, to find the right word, in order to translate it correctly onto a screen.
The smartphones we use today use voice recognition to provide better accessibility to those who prefer to speak to their phones, rather than enter commands via direct text.
Language models are generally AI models using NLP and DL to output speech and human-like text. The most common example of this are the GPT transformers developed by OpenAI today, which use large datasets from the internet and web sources to train themselves and, in turn, automate tasks requiring understanding natural language understanding (NLU), for instance.
This had gotten advanced to the point that one of OpenAI’s language models, GPT-3, can produce lines of codes after you just input a few basic instructions.
Text mining also uses NLP to discover relevant information and/or insights from unstructured text by transforming that text into meaningful and usable data. This can be used to highlight patterns, for example, across a wide data set, helping businesses discover everything from how to improve customer service or employee satisfaction levels, to what their competitors’ strategies are, to how they can cut down risk.
Now, we shall discuss the more interesting stuff: sector-specific NLP capabilities.
NLP in healthcare use cases have been wide and varied over the years, including but not limited to:
Clinical documentation – A report in 2017 estimated that primary care physicians spend at least 6 hours on data entry tasks related to electronic health records (EHR) during their usual 12-hour shift. But when NLP is used in conjunction with OCR (optical character recognition), it can help physicians extract the required healthcare data from medical forms, physicians’ notes, or EHRs at a much faster rate – which they can then feed into data entry software like RPA bots, for example.
This significantly cuts down the time they spend on data entry each day, thus, increasing the quality of that data quite notably, as the likelihood of human errors during the process is reduced to zero.
Dictation – In order to document clinical processes and results, physicians must dictate those processes to a voice recorder or more commonly, a medical stenographer, which is then transcribed to texts and, subsequently, fed into the EMR and EHR systems. However, this can prove to be very cumbersome and time-consuming. NLP may be used, in this case, to analyse those voice recordings and convert them quickly to text, which can then be fed into both EMRs and patients’ records, therefore, vastly speeding up the process.
Clinical trial matching – NLP has also been used to interpret clinical trial descriptions, where it can check unstructured/raw data pathology reports and doctors’ notes – to recognise when individuals would qualify for any given clinical trial. The algorithm which has been used to come up this kind of NLP model uses research papers and medical records as training data, enabling it to process everything from recognising medical terms and synonyms to interpreting the general purpose or context of a trial, to generating a list of trial eligibility criteria, to even evaluating each participant’s application accordingly.
CAC – Computer Assisted Coding tools are custom-built pieces of software which screen medical documentations and produce medical codes pertaining to specific phrases and terminologies within that document. NLP-based CAC tools can analyse and interpret raw or unstructured healthcare data in order to extract the desired features (such as medical facts) pertaining to the assigned codes.
Virtual therapists – Also referred to as therapist chatbots, these are a specific application of conversational AI in the healthcare sector. Here, NLP is used to train the algorithm on a variety of mental health diseases, for example, along with evidence-based guidelines, which help it to deliver cognitive behavioural therapy to patients suffering from anxiety, depression, and PTSD.
Additionally, virtual therapists are being used to converse with autistic patients so that they can build the confidence to improve not just their everyday social skills but also their job interview skills, for example.
Computational phenotyping – This refers to the process of analysing a patient’s biochemical or physical characteristics (called phenotyping) which relies merely on genetic data acquired through genotyping or DNA sequencing. In computational phenotyping, both structured data (diagnosis, medication prescriptions and EHR) and unstructured data (physicians vocal records containing everything from the patient’s medical history to lab results, discharge reports, allergies, immunisations, etc.) enable novel phenotype discovery, pharmacogenomics, patient diagnosis categorisation, clinical trial screening, and drug-to-drug interaction.
In this specific NLP healthcare use case, keyword search within rule-based systems search for specific keywords through unstructured data to achieve the above via filtering the noise, checking for abbreviations/synonyms, and matching the main keyword with an underlying event as defined by the rules.
Clinical diagnosis – In this case, NLP is used for building medical models which recognise disease criteria according to standard medical word and clinical terminology usage. A cognitive NLP solution has already been implemented to analyse patients’ EHR documents, suggesting treatments with 90% accuracy.
Process improvement – NLP may be used to study all kinds of data in production processes in order to identify areas which require improvements, increasing efficiency and productivity.
Maintenance and repair – NLP can analyse data through sensors and equipment in order to predict the likelihood of maintenance and/or repairs, reducing downtime while significantly improving efficiency.
Quality control – NLP is being used to scan vast amounts of data to zero-in on trends and patterns that may indicate current issues or those down the line in product quality. This information can be used to improve the overall quality control on processes.
Customer service – NLP is also being used by some manufacturers to analyse customer service data to better understand sentiments, thus, identifying specific areas where the customer experience can be improved upon. This may be anything from streamlining the overall order process to offering better customer support quality.
Streamline day-to-day processes – Manufacturers may also use NLP to analyse a variety of shipment-related information in order to better streamline their processes; for instance, chatbots, callbots, or voice assistants receiving updates on goods description or the delivery ETA. Resultantly, manufacturers can quickly understand where changes/improvements can be made along the chain and transition to those changes and/or improvements more easily.
In terms of process streamlining, NLP also offers a very viable alternative to cost optimisation, where trained algorithms can ‘mine’ open web sources to discover the best prices across a variety of raw materials and services.
Warehouse/facility automation – Since NLP in AI (artificial intelligence) is capable of collecting data in real-time, manufacturers can monitor warehouse operations around the clock and plan logistics accordingly. Using demand forecasting AI solutions, they can improve stocking, thus, ordering goods well ahead of time. NLP’s ability to interpret the human language can be very helpful in manufacturing as it can help to automate a lot of tasks in manufacturing warehouses or facilities, which have been traditionally done by people.
For instance, it may be used to interpret customer orders, employee communications, or machine/equipment data. By automating such tasks, manufacturers can redirect their employees toward other tasks where the margin for error is much more forgiving.
Improve communication – NLP may also be used to improve communication between employees and machines; for example, chatbots can quickly help answer customer concerns or queries, rather than a staff member intervening every time a question comes up, which could range in the hundreds to thousands on any given day. This can significantly improve customer service delivery and customer experience satisfaction, while also reducing the response time required to resolve issues.
Financial auditing – NLP can enable financial auditing by screening an organisation’s financial documents, classifying the content of financial statements, and, identifying documents which share similarities and differences. This will enable the quick detection of any deviations and anomalies in statements which might have been missed otherwise.
Financial reporting – When combined with machine learning (ML) algorithms, NLP can identify significant data and insights in unstructured invoices, payment documentations, or financial statements, for example, and then extract and feed it into an automation solution like an RPA bot used for reporting – allowing businesses to quickly generate highly detailed and accurate financial reports.
Fraud detection – When used in conjunction with ML and predictive analysis, NLP can help to detect fraud and misinterpreted information which may have originated from unstructured financial documents. For example, this study shows that NLP linguistic models detected deceptive emails which were identified through an increased frequency of emotion words and action verbs with negative connotations/emotions, and a reduced frequency of 1st person pronouns as well as exclusive words and phrases.
The researchers relied on an SVM (Support Vector Machine) classifier algorithm to analyse linguistic features of annual reports, which included voice data, occurrences of active vs. passive voice, and content readability, allowing it to build associations between the above features and financial statements that were fraudulent.
Sentiment analysis – Sentiment analysis are always being used to aid in better financial decision making. The stock market is highly volatile, often reacting violently to world events; this is where NLP can be used to sift through financial social media posts, tweets, articles, and stock market opinions on StockTwits, for example, to acquire the relevant insights and information. This will provide financial analysts with the information they need on market moods and make better decisions around investments, as a result.
Credit scoring – Credit scoring is one of the most common NLP use cases in banking today, a statistical analysis performed by banks as well as lenders and financial institutions to understand the creditworthiness of a person or business.
NLP can be used to improve credit scoring by extracting the relevant data from unstructured documents; e.g. such as income, expenses, investments, loans, etc. which can then be fed into the organisation’s credit scoring software to establish an individual’s credit score.
Additionally, modern credit scoring software can utilise NLP to quickly extract the required information from the personal profiles of an individual, like mobile applications or social media accounts, and then use ML algorithms to weigh the above information in order to assess creditworthiness.
Many banks are using conversational banking tools to help with credit scoring, where conversational AI tools combined with NLP are being used to analyse the answers customers provide to specific questions, allowing the tools to assess their risk attitudes.
Stock prices forecast – When used together with KNN classification algorithms, NLP can help to assess real-time financial news on the web as it becomes known, paving the way for ‘news-based trading’ – helping analysts to isolate financial news directly affecting stock prices and market conditions. In order to extract this real-time web data, financial analysts can utilise web scraping APIs and web crawling/scraping tools, all powered by NLP at the core.
Money transfer via voice – Virtual assistants in banks are nothing new although their capabilities have improved a lot in the last few years. The Royal Bank of Canada, for example, allows clients to conduct money transfers through voice. In this NLP use case for banking, the NLP-based facility is activated via voice, where it first recognises a client’s voice and speech, and then generates feedback in a human voice. It doesn’t end there, however, as clients must also call out the name of the recipient and the sum, then authorise the process through a bank application, and finalise it with their unique Touch ID. This is a mandatory practice to keep security at the highest level possible.
Intelligent document search – Performing good financial decision-making that’s also well-grounded requires digging deeper into information that should not only be relevant but also serves the underlying purpose for making those decisions.
NLP algorithms can be trained to find the relevant information banks and financial institutions need by conducting analysis on free or unstructured text data. The algorithms can be trained to detect specific patterns in massive volumes of raw/unstructured data, helping banks/financial institutions uncover data that could potentially improve the services they deliver, reduce risk, and become more competitive.
NLP’s capabilities and use cases in 2023 are wide and varied. We’ve discussed only some of the most common NLP use cases – including NLP use cases in Manufacturing, NLP use cases in Finance, NLP in Healthcare use cases, and NLP use cases in Banking.
This report merely highlights some of NLP’s capabilities in 2023 and beyond as there are many, many use cases you’ll likely find across anything from Customer Service and Insurance to HR, Law & Accountancy, to Education, Marketing, and Public Sector.
While we can’t cover all possible NLP use cases under one article, we have certainly shed enough light, hopefully, for businesses across multiple sectors to sit up and take notice. NLP capabilities are only going to improve in the coming years and you need natural language processing services that can help you become more productive, competitive, and risk-free than you thought possible. Not only that, but the right NLP partner can help you identify issues within your organisational departments that you may not even know existed.
To discuss the NLP capabilities that the latest technology offers and benefit from a wide range of natural language processing services that Fast Data Science offers, get in touch now: +44 20 3488 5740.
Our data scientists belong to a team of highly capable individuals who have helped businesses across multiple sectors make the most of NLP, AI, ML, and more, to improve business processes and efficiencies, as well as the greater customer and employee experience. The ability to extract the right insights from unstructured data can give you a powerful competitive edge in today’s uncertain global economy. ß How’s this for a CTA? You think we overdid it a little?
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NLP capabilities; NLP in healthcare use cases; NLP use cases in banking; NLP use cases in finance; NLP use cases in manufacturing; NLP use cases
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In natural language processing, we have the concept of word vector embeddings and sentence embeddings. This is a vector, typically hundreds of numbers, which represents the meaning of a word or sentence.
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