NLP use cases in Finance

Nov 23, 2022

Introduction

Businesses are betting big on Natural Language Processing (NLP) to pick up their financial game in today’s digital age.

NLP is a component of AI that uses machine learning algorithms to enable computer systems to comprehend and interpret human language.

Finance is one of the main sectors that heavily rely on NLP because it is driven by textual data such as texts, analyst reports, financial print media, websites, forums, and so on.

As a result, automating textual data processing can reduce the amount of manual routine work and speed up trades by transforming unstructured text material into useful analytics using algorithms.

In the financial services industry, NLP has a wide range of applications, including risk assessments, credibility analysis, customer chatbots, portfolio selection, named entity recognition, sentiment analysis, speech recognition, etc.

However, in this article, we will focus on the seven top NLP use cases that stand out.

1- Analyzing text and sentiments

The process of extracting qualitative, insightful structured data from unstructured text, known as text analytics, has grown in importance in the financial industry.

One of the most common text analytics goals is sentiment analysis. It is a technique for determining the underlying sentiment and extracting key financial entities from the text by understanding the context.

Financial sentiment analysis differs from regular sentiment analysis, in terms of both domain and purpose.

The goal of regular sentiment analysis is to determine whether the information is inherently positive or negative.

However, the goal of financial sentiment analysis based on NLP is to determine how the market will react to the news and whether the stock price will fall or rise.

2- Assessing Risks

Measuring the chances of an effective loan payment capacity is determined in banking systems based on prior spending patterns and previous loan payment history data, which is hardly accessible.

NLP tools use a variety of data sources to evaluate credit risk; they can detect incoherent information as well as lender or borrower emotions during the loan process.

NER aids in the extraction of relevant entities from the loan agreement, such as the date, location, and information about the parties involved.

3 -Structuring Data

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.

These records are typically stored in formats such as pdf, XML, HTML, web, and feeds, and sorting through them is time-consuming and tiresome.

NLP allows you to extract useful information from underused data. You could train NLP models to analyze unstructured data, content, and information to address issues or patterns that may impact financial businesses, providing in-depth additional insight to build strategic planning that may go unutilized and therefore influence decision making.

4- Selecting and optimizing Portfolios

Data science and machine learning can predict investment portfolios in financial stock markets.

Data from the past can be used to forecast the start of a trading period and a portfolio.

By filtering out desirable and undesirable stocks, NLP can be used for semi-log-optimal portfolio selection.

Using this data, investors can distribute their current capital among available assets and maximize its value over time.

5- Predicting Stock behavior

Estimating stock behavior for financial analysis is a challenging task due to fluctuating and random data as well as long-term and seasonal variations that can cause significant mistakes in the evaluation.

Machine learning combined with NLP, on the other hand, far outperforms previous methodologies for working with financial time series.

These two technologies, when combined, effectively deal with large amounts of data and make it easier to predict the volatility of stock prices and trends, as well as being a valuable tool for making stock trading decisions.

6 – Reporting customers’ engagement

Financial companies must provide high-quality services to their customers, which requires understanding customer data, personalized services, and client communication.

which is essential in the financial sector, and NLP tools provide banks with important information when they communicate with clients.

NLP algorithms forecast and identify customers ’ problems, enabling banks to develop policies and services to address them. Other financial firms could make decisions based on NLP, which would accelerate the overall reporting procedure even more.

7- Accounting and audits

NLP is critical to gaining a significant advantage during the audit process.

Financial professionals can use NLP to directly identify, focus on, and visualize anomalies in day-to-day transactions.

With NLP, it takes less time and effort to identify irregularities in transactions and their causes and helps identify significant potential risks and possible fraud, such as money laundering.

Conclusion

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.

Natural language processing techniques have recently become much more accurate and reliable, making financial choices more efficient and cost-effective.

Because NLP and machine learning have become the tools of choice for financial analysts and traders, you should consider labeling and structuring your documents using our data training platform, UBIAI.

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