What is Natural Language Processing?

With new data generated each second, it is no doubt that we live in a data-driven world. The IDC predicts that the amount of digital data will continue to grow exponentially at 55-65% each year (IDC). Natural language processing can help businesses sift through large amounts of data and extract greater insights. But, what is natural language processing (NLP)? 

Definition of Natural Language Processing (NLP)

Natural language processing (NLP) is a branch of artificial intelligence that enables computers to understand text and audio data in the same way that humans can—with the human language. By combining computational linguistics and computer science, computers can understand data and interpret the text's intent, relevance, and sentiment. 

The Big Data Dilemma

The growth of data—especially textual data—is both an advantage and an obstacle in business. Data is the key for any company to understand its clients, predict client behavior, and turn insights into results. However, what differentiates a successful company is the ability to obtain clean data, analyze it and transform it into actionable results.

While data that comes in a structured format is easier to understand and analyze, large amounts of unstructured data are nearly impossible to use without natural language processing. Through text classification and machine learning algorithms, NLP can help break down unstructured data at scale. 

Unstructured Data and Natural Language Processing

Unstructured data comes in various forms such as customer transactions, online communities, surveys, reviews, chats, emails, and more. Data from these sources is constantly evolving with every tweet, blog post, review, etc. Therefore, businesses across industries face challenges in monitoring these sources and extracting the most relevant data needed to uncover insights that lead to an ROI.

The use cases of NLP vary from translating text into different languages, responding to commands, and researching, extracting, and summarizing large volumes of text from the past and even in real-time. If you've ever used a chatbot, speech-to-text, virtual assistant, or interacted with Siri, Alexa, or Google, you have used NLP in some form. However, businesses are increasingly finding value in using natural language processing to achieve business objectives, increase workforce efficiency and advance business operations.  

One industry in which natural language processing is especially helpful is financial services—across asset, quantitative, and wealth management firms, insurance companies, and banks. Financial services teams must sort through a large quantity of data from various sources such as corporate transactions, statements, news, social media posts, reviews, etc. and evaluate the data to ensure that companies and/or individuals are in line with certain practices and are reliable. 

Wealth managers, investment bankers, and traders can use NLP tasks for data mining on corporate news, mentions, statements, and more to create investment signals that can help predict stock market movement. On the other hand, insurers and bankers can use NLP to mine data to assist in underwriting and identifying credit risk. NLP's various use cases within finance result in better investment management practices, accurate trading investment signals and market movement predictions, risk management, and client retention. 

NLP Use Cases within Finance

• Sentiment analysis uses artificial intelligence and natural language processing to identify, extract, and analyze textual data to understand the overall attitude and emotion of the text. Once the data is evaluated, a sentiment score will be generated to determine whether the data is positive, negative, or neutral. Sentiment analysis is most commonly used by hedge funds in analyzing financial news to predict stock market trends and movements. For example, traders and investment bankers can use NLP to research, extract data on, and analyze ESG compliance and mergers and acquisitions news. With sentiment analysis, financial teams can evaluate consumers' sentiment around specific companies. 
  
  
• Named Entity Recognition (NER) uses artificial intelligence and NLP algorithms to identify specific companies or individuals (named entities) mentioned in unstructured text data. Once the named entities are identified, they are classified into pre-defined categories such as company names, person names, locations, products, services, etc. NER plays an important role with hedge funds and investment management in financial news analysis as it enables analysts to quickly research, find, and extract specific information. Wealth and portfolio managers and investment bankers can quickly find the exact mentions of entities with NER to determine which financial actions to take.
  
  
• Text summarization uses AI and NLP to take large quantities of data and extract the most relevant details of the text. The most important points within the document are summarized while keeping the meaning of the data. With large amounts of data constantly generated, financial analysts do not have the time to read through every work and manually extract the important points.
  
  Financial analysts can use machine translation and text summarization to receive summaries of important documents and not have to worry about human error. NLP enables text summarization applications to add insightful context and conclusions to summaries as well, further assisting financial teams on the next steps. 
  
  
• Speech recognition allows software to use artificial intelligence to recognize spoken language and convert it into text data. Banks have already implemented speech recognition through customer service calls, which has resulted in a massive decrease in operational costs. A subset of speech recognition and a new advancement that banks are beginning to adopt is voice recognition. Voice recognition uses artificial intelligence to match a customer to his/her voice. HSBC recently announced that it will be the first bank to implement voice recognition for telephone banking customers.

Natural Language Processing with Accern

With the growth in data, financial firms that can extract information to create actionable insights quickly and at scale will have the most leverage in a competitive landscape. Accern offers a no-code AI platform for financial enterprises to quickly build and deploy adaptive NLP models—no NLP practitioners or data science experts required. Accern focuses on empowering and easing workflows for technical and non-technical users alike, enabling data scientists, engineers and financial analysts to use Accern's platform for sentiment analysis, NER, and text summarization. 

Accern's value proposition is its access to large amounts of clean data, making insights reliable, timely, and accurate. From public, global and local news sources to premium sources like Dow Jones, FactSet and Morningstar, social media platforms, reviews, blogs, search engines and more, Accern allows you to apply clean, historic and real-time data sets toward building your custom NLP use case for financial insights.

You can also upload data from your CRM system, excel spreadsheet, Word doc, PowerPoint, and PDF to build NLP use cases directly into the Accern platform. If you want to read more about why financial firms are integrating their CRM systems with AI, here are four ways that integrating artificial intelligence with CRM systems brings value across the front, middle, and back-office operations in financial firms.

About Accern

Accern enhances artificial intelligence (AI) workflows for financial services enterprises with a no-code AI platform. Researchers, business analysts, data science teams, and developers use Accern to build and deploy AI use cases powered by adaptive natural language processing (NLP) and forecasting features. The results are that companies cut costs, generate better risk and investment insights, and experience a 24x productivity gain with our smart insights. Allianz, IBM, and Jefferies are utilizing Accern to accelerate innovation. To schedule your quick demo of the Accern platform, fill out the form below.