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5 Use Cases Of NLP In Banking And Finance

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Security risks and other technological pitfalls are the most common threats to a financial company. And while a proactive approach to cybersecurity is a significant deterrent to costly attacks, other technologies, such as Natural Language Processing (NLP), can help industries dramatically improve their operations.

As one of the main branches of Artificial Intelligence, NLP is responsible for allowing machines to understand human language across all formats. It’s a machine-learning model that enables big data processing, mining, and predictive analysis, among many other functions widely used in accelerating business growth.

If you want to gather general information about the subject, you can click here to know more about NLP, including the techniques used, trends, and challenges.

Besides fueling voice home assistants and chatbots in end-consumer settings, NLP has multiple applications in the banking and finance sectors, such as:

  • Advanced enterprise search 

Financial businesses rely on internal and external data sources to process information. Unfortunately, these sources are found in different databases and come in various formats and languages.

A survey has indicated that employees spend as long as two hours daily in companies with inefficient data management systems to locate and recover data. This translates to USD$2 million in annual productivity losses.

When used alongside other machine learning technologies, NLP can be used for cognitive search, which finds the most relevant user search results across all formats, languages, and platforms. Fast information retrieval can be used for several banking and finance functions, including answering customer queries, reviewing and ensuring regulatory compliance, and staff onboarding and management.

  • Fraud detection

Fraud is another significant risk in the financial industry. According to the Federal Bureau of Investigation, fraudulent insurance collections cost companies up to USD$40 billion annually. A December 2022 study from the Nilson Report estimated that card fraud losses would reach USD$165.1 billion over the next decade.

Through NLP’s text mining functionality, machines can spot common keywords and repetitive descriptions across numerous locations and multiple cards or claim applications within large volumes of electronic data and scanned documents. These likely indicators of organized fraud can be flagged for proper action.

  • Risk assessment and management 

Banks and lending institutions rely on multiple data channels to assess borrower risk. Done manually, this may lead to mistakes and questionable evaluation outcomes.

With NLP, however, lenders can use Named Entity Recognition (NER), which identifies key textual information and prevents ambiguity by appropriately categorizing them. Additionally, NLP can review the business plan to evaluate the borrower’s consistency and attitude based on the words and the writing tone used in the document.

  • Portfolio management and optimization 

Loan applications and credit card use aside, NLP can be used in fund and investment management, as it can process big data more efficiently.

Financial data analytics

That said, NLP’s text analytics capabilities enable businesses to scour different sources, including financial news and market and investment trends, to determine the volatility of certain investments.

Similarly, financial firms can use content enrichment and sentiment analysis to arrive at better investment decisions and business strategies. Content enrichment refers to using NLP and other technological solutions to make content relevant to your business operations, especially from unstructured datasets.

Meanwhile, sentiment analysis analyzes public attitudes and behavior toward certain market contexts. In some instances, negative sentiment may cause investors to pull out from certain entities, which could affect their stock market price. FinBERT is the premiere NLP model used in financial text analysis.

Predictive uses

Besides current sentiment, NLP can be used for historical data to predict the future performances of investment funds, especially with specific risk factors involved. Doing so enables financial wealth managers to identify high-risk investments and optimize growth potentials even in uncertain situations.

  • Customer services, analysis, and retention 

Chatbots and virtual assistants were two of the earliest applications of financial services automation. These machine-driven service tools use NLP and predictive analytics to accept and process voice and text commands to respond to customer queries and needs. This NLP-driven service delivery has enhanced customer experience and satisfaction.

Natural language processing is likewise paramount in gathering customer analysis. By tapping NLP’s sentiment analysis and intelligent document search, financial firms can determine the most sought-after services, identify customers’ primary challenges, and discover how clients feel about the company. The results can be used for personalized offers, measuring customer response, and improving products and services.

Concluding thoughts 

Training computers to process text and speech inputs goes a long way in boosting business intelligence. Because of increasing demand, NLP has become one of the fastest-rising AI subsectors, with experts projecting a cumulative industry growth of 39% from 2022 to 2030, or USD$361.6 billion in industry value.

In the finance and banking sectors, NLP is used to streamline repetitive tasks, reduce errors, analyze sentiments, and predict future performance using historical data. With such applications, firms can save time and costs, increase productivity and efficiency, and ensure service quality delivery.

Global Banking & Finance Review


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