How Natural Language Processing Can Revolutionize The Financial Industry
Since volumes of textual data increase, natural language processing becomes an effective tool for financial analysis. Here are four ways it helps.
The language is the substance absorbing information from the epochs, reflecting social trends and giving a profound insight into things happening to us, humans, today. It’s the most natural communication medium for us. It’s an environment where technology can learn to think like a human, make the best decisions, and predict the most likely future. What about the future of finance? Modern finance is being swayed not only by numbers but also by words. The automatic analysis of the text by computers, also known as natural language processing, is now transforming financial services and makes them more personalized, efficient and accessible.
Where human and machine languages converge
In the world of humans, the communication process is complex and implies the use of words, tones, facial expressions, and gestures. The world of machines communicates in a different way, using only clearly defined quantities and labels. For those aiming to integrate these two realms into a decision-making process, there has been the challenge of incompatibility. However, with the fast evolution of AI, the gap has closed. It has been the birth of natural language processing (NLP), the field of artificial intelligence focused on the ability of computers to understand text/speech and analyze unstructured natural language data.
NLP combines two other technologies: natural language understanding (NLU) and natural language generation (NLG). Natural language understanding refers to the process of mining the meaning behind the speech or text by computers. This involves processing unstructured data of the human language input and transforming it into structured data which computers can understand.
Natural language generation is the process of converting structured data into a computer-generated text or speech which humans can understand. For example, if you ask Alexa “What day of the week is today?,” it will use NLU to figure out what you are saying. And it will use NLG to respond to you with the phrase “Today is Tuesday.”
Integrating NLP into financial decision making
Natural language processing can transform unstructured data from text or human speech into meaningful insights across various areas of modern finance. With this basic understanding of NLP, let’s dive deeper into how financial companies can leverage this technology to improve customer experience and decision-making in financial services.
Customer service and insights
Current financial systems have a lot of gaps in customer service. However, NLP technology opens up new opportunities for customer service enhancement. Innovation-thinking banks and fintech startups use natural language processing to augment intelligent chatbots into the customer experience. Cognitive chatbots process user queries from the chat and constantly learn from the transcripts of previous conversations. Banks can use NLP-powered chatbots to provide 24/7 customer assistance and personalized insights based on real-time financial data analysis.
Sentiment analysis
Banks can use NLP to enhance sentiment analysis. It means that NLP can be applied to consumer texts, such as surveys, social media posts, contact forms, emails, or call transcripts, to gauge the consumer attitude (positive, negative, and natural) toward specific financial products or the bank in general. For example, a Boston-based AI company Lexalytics developed a tool called Semantria, which employees in financial organizations and banks can use to detect sentiment trends and customer moods by processing surveys and social media texts collected in Microsoft Excel Sheets. Semantria uses natural language processing to identify and categorize sentiments by positive, neutral, and negative tones.
Legal document review
Natural language processing can reduce the time and human effort needed to process huge volumes of customer data. Fintechs and banks can use NLP to optimize and speed up the Know Your Customer (KYC) process that requires in-depth customer data analysis and document review.
J.P. Morgan Chase’s AI bot COIN (short for contract intelligence) is an excellent example of NLP in document search. The bank uses NLP to automate the process of reviewing large amounts of legal documents. COIN recognizes and extracts key insights from the contracts which J.P. Morgan Chase’s team has tagged as important.
Investment analysis
For years, financial analytics firms have been hiring dozens of analysts to process tons of company reports, conference calls, and media news to receive valuable insights about securities. With the advent of NLP, things have changed. Now, financial companies are using NLP-powered tools and software able to ‘read’ the documents and ‘listen’ to the conference calls to summarize and categorize key information for human analysts.
State Street created the NLP-based solution to help investment professionals read and interpret lengthy company reports more efficiently. It saves a lot of time by automating the process of reviewing, tagging, and classifying documents, as well as summarizing their content. The software developed by State Street allows portfolio managers to extract findings relevant to their investment strategies much faster. It identifies the words and phrases and leverages keywords to categorize all documents by regions, asset classes, and topics.
Key takeaways
Natural language processing is one of the most progressive computer science and AI areas. It opens tremendous opportunities for different industries. Specifically, financial analytics firms and banks turn to this technology to parse mountains of textual data hundreds of times faster and more efficiently than humans would. While a casual observer might think that financial data is more numerical than textual, in reality, data that can enhance the decision-making process comes in text.
NLP-based software can help banks bring customer service to the next level by providing higher personalization and real-time support to customers. In addition, NLP tools for sentiment analysis can give financial companies valuable insight into customer moods and behaviors. It allows financial companies to craft better strategies and adjust financial services for real customer needs. With the help of NLP, legal teams can save a lot of time by automating the process of document search and content categorization. Investment portfolio managers can apply NLP to process company-related data faster, perform sentiment analysis to figure out how the company is being talked about in the media, and extract findings relevant to their investment strategies.
