How Natural Language Processing NLP is Revolutionizing Financial Services

Sentiment analysis is one of the most commonly used objectives of text analytics. It is a technique for determining the underlying sentiment and extract key financial entities by understanding the context from a piece of text. Text analytics is the process of extracting qualitative, insightful structured data from unstructured text, which has grown in importance in the financial industry. In the finance industry, NLP is being used to significantly expedite transactions, evaluate risks, understand the financial sentiment, and create portfolios while automating audits and accounting. Financial companies must provide high-quality services to their customers, which requires understanding customer data, personalized services, and client communication. Analysis and research reports, corporate filings, and quarterly revenue documents are just a few of the financial resources that traders, investment firms, and financial experts must navigate.

The use of NLP techniques such as sentiment analysis and intent recognition also allows chatbots to understand the user’s emotions and intent, providing more relevant and useful responses. NLP employs a variety of techniques, machine learning, deep learning algorithms, and statistical models, to perform tasks such as text categorization, sentiment analysis, machine translation, speech recognition, and question answering. Banks can expect AI vendors to offer NLP solutions for extracting data from both structured and unstructured documents with a reasonable level of accuracy. SAS claims users can integrate the SAS platform in the form of a cloud solution and that it includes data and model management so that data scientists at banks can develop additional AI models.

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BioBERT, a pre-trained biomedical language representation model for biomedical text mining, has been quite useful for healthcare and now researchers are working on adapting BERT into the financial domain. It consists of about 4,000 sentences labeled by different people of business or finance backgrounds. The natural language processing has emerged as a powerful tool in the development of financial chatbots, which can enhance customer engagement, improve customer experience, and streamline business processes. The ability to understand natural language and to generate human-like responses has made chatbots an effective solution for addressing customer queries and concerns.

Some may outsource the analysis, relying on a third-party to deliver data that they use in conjunction with their own. Meanwhile, firms with the expertise and resources are bringing NLP in-house, customizing the models to their questions and needs. Analysts are currently using NLP to glean a better understanding of what’s happening to the economy in real-time. For example, Turrell noted that NLP helps central banks forecast aspects of the macroeconomy. He’s been leveraging NLP to analyze job advertisements, looking for indicators of structural changes in the job market.

Advanced Materials

Machine learning (ML) and AI in financial services have often been trained on quantitative data, such as historical stock prices. However, natural language processing (NLP), including the large language models used with ChatGPT, teaches computers to read and derive meaning from language. This means it can allow financial documents — such as the annual 10-k financial performance reports required by the Securities and Exchange Commission — to be used to predict stock movements. These reports are often dense and difficult for humans to comb through to gain sentiment analysis. By using NLP, investors can quickly analyse the tone of a report and use the data for investment decisions.

• Read more about how NLP is applied for industry specific tasks in this post about The Role of Natural Language Processing in Healthcare.
• Using OCR and sentiment analysis, firms can scan through customer comments, reviews, social media interactions, phone log transcripts, and more and pull out contextual and behavioral data.
• A company with the right model that could predict these changes could potentially save or make a lot of money.
• An NLP technique called semantic search brings the traditional search to the next level by determining its intent and context instead of just relying on the keywords.
• The process is manual, highly inefficient, and dependent on human expertise” he adds.

With the right technology, less time and effort is spent to find out irregularities in the transactions and its causes. NLP can aid with the identification of significant potential risks and possible fraud, like money laundering. This helps to increase value-generating activities in order to disseminate them across the organization. The groundwork on how to make computers understand and use natural language derives from various fields including linguistics, neuroscience, mathematics and computer science, and results in an interdisciplinary area called NLP.

Methods and Technologies

In our previous report, we covered 6 use-cases for AI in business intelligence. As of now, numerous companies claim to assist business leaders in the finance domain, specifically, in aspects of their roles using AI. They often have to navigate, with limited resources, a stormy market made of customers, competitors, and regulators, and the interactions between all these actors make finding answers to business questions a complex process. To do this, RBS looked to NLP to extract the most relevant customer issues and interaction events, which it found included applying for a loan and making a payment. We were unable to find evidence of C-level executives with AI experience on the company’s team, although they claim that COO and Co-Founder David Govrin has expertise in machine learning and analytics algorithms.

The authors suggest that pre-trained language models do not need many labeled examples. News analytics, alongside customer feedback, are where sentiment analysis systems excel. Using an https://www.globalcloudteam.com/ advanced sentiment analysis system could have potentially led to a company understanding that the aforementioned tweet from Elon Musk would cause an increase in share prices of Tesla.

An illustration of high-value use cases in underwriting and claims management

For example, based on loan default risk models or other business data analytics, loan applications are prioritized and sent for manual reviews and approvals by loan officers. A revolution occurred in the late 1980s, caused by the introduction of machine learning. Machine learning as a technology allowed language processing systems to evolve from following rules to using corpus linguistics, or text collected in its natural context and annotated by humans or computers. To put it simply, instead of following some pre-written set of rules, machine learning models automatically created new rules to follow by analyzing some example text. Models like these were also able to express how certain a model was of its results.

Read more about how NLP is applied for industry specific tasks in this post about The Role of Natural Language Processing in Healthcare. Another good example of augmenting the analyst’s work is automated text summarization tools. They can scan Natural Language Processing Examples in Action through hundreds of pages and deliver summaries containing only the relevant information. The NLP-based tool can scrape texts and find relevant information efficiently and swiftly, significantly boosting the efficiency of the analyst.

AP & FINANCE

Content intelligence is an emerging technology that boosts content strategies by delivering data-driven analytics on content and its influence on consumers. It entails both direct and indirect financial gains from content marketing initiatives, such as decreased squandering and increased ROI. The most crucial data is present in textual form in records, texts, websites, forums, and other places. Finance professionals spend a lot of time reading analyst reports, financial print media, and other sources of information. Which is essential in the financial sector, and NLP tools provide banks with important information when they communicate with clients.

Natural language processing offers an ideal platform for the management of trading activities by relaying updates based on company operations. One challenge facing banks is the lack of tools for reporting market conditions such as company news posted online or mentioned in business news. NLP is bridging this gap by supporting real-time dissemination of information about their services from customers and business partners.

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That’s what happened recently to Terra (LUNA), the fall of which has questioned the future of the crypto market. Today, we will focus on other sectors that have been discovering NLP for themselves in recent years – banking, finance, and insurance. If would like to delve deep into the concept of NLP first, check out our
definitive guide to this technology that explains its intricacies to the smallest detail.