NLP use cases in Finance
Nov 23, 2022
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.