5 Natural Language Processing (NLP) applications used in finance
June 29, 2025 11 min read 122 views
Natural Language Processing (NLP) is reshaping how financial institutions analyze information, automate routine work, and make data-driven decisions. As a branch of artificial intelligence, NLP is rapidly transforming the financial sector by unlocking the value hidden in vast amounts of unstructured text data. It enables machines to understand, interpret, and generate human language, giving financial organizations powerful tools to automate routine tasks, enhance decision-making, and gain deeper market insights. With growing access to data and advancements in machine learning, NLP applications are becoming essential across finance, from improving customer experiences to driving investment strategies.
In this article, we’ll explain how NLP works and explore some of its most common applications in finance, including credit risk assessment, sentiment analysis, auditing, portfolio optimization, and stock market prediction.
What is natural language processing (NLP)?
Natural language processing (NLP) is a branch of artificial intelligence (AI) that enables computers to understand, interpret, and work with human language. It combines knowledge from fields such as linguistics, neuroscience, mathematics, and computer science to help machines process and analyze text and speech.
With the advent of machine learning (ML) algorithms and increased computational abilities, NLP has become much more scalable and reliable.
Recent advances in machine learning, transformer-based models, and large language models (LLMs) have significantly expanded what NLP systems can do. Modern NLP applications can understand context, analyze large volumes of text, generate human-like responses, and support tasks ranging from document analysis and translation to conversational AI.
In addition, the viability of NLP models has broadened to many languages, apart from English, enabling more accurate machine translation algorithms across different platforms. NLP tools, techniques, and APIs (Application Programming Interfaces) are now used across different industries, including finance.
How does NLP work in finance?
In finance, NLP works by analyzing large volumes of unstructured text data, such as financial documents, reports, websites, forums, and market news, to identify patterns and extract meaningful insights. By automating text processing, NLP helps financial professionals reduce manual work, improve financial analysis, and make faster, more informed decisions.
Nowadays, data is driving finance – data in documents, texts, websites, forums, and so on. Finance professionals spend a considerable amount of time reading analyst reports, financial press, and other sources of information. Automatic textual data processing can significantly decrease the amount of manual routine work and accelerate trades.
NLP techniques and algorithms help translate raw textual data into meaningful insights across several areas in finance. Traders, portfolio managers, analysts, banks, and other financial organizations strive to improve their financial analysis, and NLP and ML have become the technologies of choice. NLP is used across the financial industry, from retail banking to hedge fund investing. NLP techniques such as sentiment analysis, question-answering (chatbots), document classification, and topic clustering are used to work with unstructured financial data.
NLP and ML techniques can be used to design financial infrastructure that can make informed decisions on a real-time basis. NLP can help create systems that enrich financial processes by tracking changes in a company’s situation and market environment. For example, NLP can improve the operation of a bank by enabling:
- Better personalized experiences for customers
- Better capabilities for detecting fraud and money laundering activities
- Improved operational efficiency
- Better compliance with policy norms
- New analytical insights
- Innovative product offerings
Applications of NLP in finance
NLP is used across the financial industry to analyze large volumes of unstructured data and support faster, more accurate decision-making. Financial institutions apply NLP techniques in areas such as credit risk assessment, market sentiment analysis, auditing, portfolio management, and investment research.
Below are some of the most common applications of NLP in finance and how organizations use them to improve financial processes.
Risk assessments
Banks can quantify the chances of a successful loan payment based on a credit risk assessment. Usually, the payment capacity is calculated based on previous spending patterns and past loan payment history data. But this information is not available in several cases, especially in the case of poorer people. According to an estimate, almost half of the world population does not use financial services due to poverty.
NLP is there to solve this problem. NLP techniques use multiple data points to assess credit risk. For instance, NLP can measure attitude and an entrepreneurial mindset in business loans. Similarly, it can also point out incoherent data and take it up for more scrutiny. Even more, the subtle aspects like lenders and borrower’s emotions during a loan process can be incorporated with the help of NLP.
Usually, companies capture a lot of information from personal loan documents and feed it into credit risk models for further analysis. Although the collected information helps assess credit risk, mistakes in data extraction can lead to wrong assessments. Named entity recognition (NER), an NLP technique, is useful in such situations. NER helps to derive the relevant entities extracted from the loan agreement, including the date, location, and details of parties involved.
Financial sentiment
Successful trading in the stock market depends upon information about select stocks. Based on this knowledge, traders can decide whether to buy, hold, or sell a stock. Besides analyzing quarterly financial statements, it’s essential to know what analysts are saying about those companies, and this information can be found on social media.
Social media analysis involves monitoring such information within social media posts and selecting potential opportunities for trading. For example, news of a CEO resignation usually conveys a negative sentiment and can affect the stock price negatively. But if the CEO was not performing well, the stock market takes resignation news positively and it may potentially increase the stock price.
Dataminr and Bloomberg are some of the companies that provide such information for trading support. For example, Dataminr provides stock-specific alerts and news about companies through its terminals that can potentially affect the market.
Financial sentiment analysis is different from routine sentiment analysis. It’s different in both the domain and its purpose. In regular sentiment analysis, the objective is to determine whether the information is inherently positive or negative. However, in financial sentiment analysis based on NLP, the purpose is to understand how the market may react to the news and whether the stock price may fall or rise.
The development of domain-specific language models has shown how adapting NLP models to specialized fields can improve their performance. For example, BioBERT, a pre-trained biomedical language representation model for biomedical text mining, demonstrated the value of training models on industry-specific data. Similar approaches led to the creation of FinBERT, a language model developed specifically for the financial services sector.
FinBERT operates on a dataset containing financial news from Reuters. To assign sentiment, the Financial PhraseBank dataset was utilized, consisting of about 4,000 sentences labeled by people with business and finance backgrounds.
In usual sentiment analysis, a positive statement implies a positive emotion. However, in the Financial PhraseBank, negative sentiment indicates that a company’s stock price may fall because of the published news. FinBERT has been successful in financial sentiment classification, significantly improving results compared to other available tools. The FinBERT library is open on GitHub with the relevant data. This language model for economic sentiment classification can be used for different purposes.
Accounting and auditing
Deloitte, Ernst & Young, and PwC are focused on providing meaningful actionable audits of a company’s annual performance. For instance, Deloitte has evolved its Audit Command Language into a more efficient NLP application. It has applied NLP techniques to contract document reviews and long-term procurement agreements, especially with government data.
Companies now realize NLP’s importance in gaining a significant advantage in the audit process, especially after dealing with endless daily transactions and invoice-like papers for decades. NLP enables financial professionals to directly identify, focus, and visualize anomalies in day-to-day transactions. With the right technology, less time and effort are spent to find irregularities in the transactions and their 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.
Portfolio selection and optimization
The main goal of every investor is to maximize its capital in the long-term without knowledge of the underlying distribution generated by stock prices. Investment strategies in financial stock markets can be predicted with data science, machine learning and nonparametric statistics. The collected data from the past can be used to predict the beginning of the trade period and a portfolio. Thanks to this data, investors can distribute their current capital among the available assets.
NLP can be utilized for semi-log-optimal portfolio optimization. Semi-log-optimal portfolio selection is a computational alternative to the log-optimal portfolio selection. With its help, the maximum possible growth rate is achieved when the environmental factors are uncertain. Data envelopment analysis can be utilized for portfolio selection by filtering out desirable and undesirable stocks.
Stock behavior predictions
Predicting time series for financial analysis is a complicated task because of the fluctuating and irregular data as well as the long-term and seasonal variations that can cause large errors in the analysis. However, deep learning combined with NLP outmatches previous methodologies working with financial time series to a great extent. These two technologies combined effectively deal with large amounts of information.
Deep learning by itself is not a brand-new notion. In the last 5 years, a great number of deep learning algorithms have started to perform better than humans at a number of tasks, such as speech recognition and medical image analysis. Within the financial domain, recurrent neural networks (RNN) are a very effective method of predicting time series, like stock prices. RNNs have inherent capabilities to determine complex nonlinear relationships present in financial time series data and approximate any nonlinear function with a high degree of accuracy. These methods are viable alternatives to existing conventional techniques of stock indices prediction because of the high-level of precision they offer. NLP and deep learning techniques are useful to predict the volatility of stock prices and trends and also are a valuable tool for making stock trading decisions.
FAQ
NLP in financial practice
NLP techniques are used to transform unstructured text information into meaningful analytics. As NLP algorithms become more reliable and scalable, financial organizations can process information from sources such as financial reports, market news, customer communications, and regulatory documents faster and more accurately.
The financial industry is using NLP to reduce manual routine work, accelerate analysis, assess risks, understand financial sentiment, and support portfolio management while automating auditing and accounting processes. These advances are achieved through NLP capabilities such as sentiment analysis, question-answering (chatbots), topic clustering, and document classification.
NLP and ML have become important technologies for financial analysts, traders, and portfolio managers. Insurance organizations use NLP to process claims and analyze customer information, while retail banks are applying it to streamline customer support, improve financial products, and strengthen fraud and money laundering detection.
Looking ahead, the biggest shift in NLP adoption will come from combining traditional NLP techniques with generative AI and large language models. This combination allows financial institutions to move beyond simply extracting information from documents and toward AI systems that can summarize research, answer complex questions, assist analysts, and support faster decision-making across financial operations.
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