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How NLP is making waves in the world of finance?

History: How did natural language processing come about?

In 1956, John McCarthy proposed the first computer science conference at Dartmouth College. The goal was to bring together researchers from various disciplines who were interested in computers in order to foster collaboration and interdisciplinary exchange. It was during this conference that McCarthy coined the term “artificial intelligence” (AI). In his paper titled “Some Problems in Artificial Intelligence”, he outlined the goals of AI as:

  • To create programs that can reason, learn, and solve problems.
  • To understand human intelligence and replicate it using computers.
  • This laid the groundwork for the development of natural language processing (NLP), which aims to replicate human communication using computers.

Introduction: What is natural language processing?

Natural language processing is a field of computer science and artificial intelligence involving the development of software that can understand human language. The goal of natural language processing is to create technology that can enable computers to automatically interpret, understand, and generate human language. This involves tasks such as parsing sentences into their component parts, identifying the meaning of words, and understanding the relationships between words.

Representative image: NLP Will Impact The Financial Sector in 2022

10 Exciting Ways NLP Will Change Finance Forever

On that note, Techmarketreports have a look at “How NLP is making waves in the world of finance”

1. Compliance
Natural Language Process is a tool that can be used to assist banks and financial institutions in their compliance processes. NLP allows compliance officers to quickly determine whether regulations have been observed by searching through thousands of digital documents.

2. Underwriting intelligence
NLP is a way to connect structured and unstructured data. Then, NLP can smartly transform the data and learn from each interaction. This provides contextual intelligence.

3. Tracking relationships
Finance industry analysts are responsible for analyzing earnings calls in order to determine the main theme or compare calls against each other to identify trends. NLP is a great tool for this purpose. It can be used for linking entities and building a graph of relationships. This helps to detect money laundering and/or fraud.

4. Risk management
Natural Language Processing has been a blessing in the creation of more precise, flexible models to protect businesses. These models are able to identify trends, save manpower, and provide better information for future planning.

5. Better lending decisions
NLP plays a key role in helping organizations make better lending decisions. NLP analyses, bank statements, pay stubs, tax documents, mortgage forms, invoices, etc. to determine credit lending eligibility.

6. Answers to complicated financial questions
NLP is known for its ability answer complicated financial questions. Cloud computing is used to enable this. This NLP application has been widely used, so much that global institutions such as J.P. Morgan and Bank of America, Morgan Stanley and S&P Global depend on it.

7. Accounting and Auditing
Companies around the world have come to realize how important NLP is in gaining an advantage in auditing. Finance professionals can now identify, focus and visualize anomalies in their day-to-day transactions with NLP.

8. Predicting stock behavior
Given the irregularity and volatility of stock prices, it is obvious that large errors can be made in the analysis. Combining NLP with deep learning is a better way to analyze financial time series than any other methods.

9. Sentiment analysis
Financial industry is often responsible for reviewing unstructured information about a company in order to identify anomalies and inconsistencies. NLP can be a lifesaver as it can detect potential indicators of equity performance using thousands of company transcripts and research reports.

10. Identifying financial market trends
NLP has the potential to analyse keywords in filings, transcripts and news to find changes and trends within financial markets. This is the best thing about NLP.

Applications: What are some practical applications of natural language processing?

There are many practical applications of natural language processing. One example is sentiment analysis, which is used to determine the attitude of a text, such as whether it is positive or negative. This can be used for things like customer feedback or social media monitoring. Another application is machine translation, which can be used to translate text from one language to another. It can also be used to improve search results by translating queries into multiple languages.

Technology: What are the underlying technologies behind natural language processing?

In natural language processing (NLP), computers are taught to read, understand, and analyze human language. NLP is a subfield of artificial intelligence and linguistics that deals with the interactions between computers and human languages. There are a few different underlying technologies behind NLP.

One technology used in NLP is machine learning. Machine learning algorithms are used to automatically learn how to represent and interpret text by analyzing large data sets. These algorithms can be used to learn the features of text, such as the words that occur most often in a given language or the syntactic structures of sentences.

Another technology used in NLP is artificial intelligence (AI). AI algorithms can be used to understand the meaning of a text by understanding the relationships between words and concepts.

Future: What is the future of natural language processing?

The future of natural language processing is shrouded in potential, but fraught with uncertainty. Despite the challenges, there are many reasons to be optimistic about the future of NLP. First, there has been a surge in recent years in the number of papers published on NLP topics, as well as the amount of data being used to train models. This indicates that researchers are making progress and that more people are interested in this field. Additionally, recent advances in deep learning have led to dramatic improvements in many NLP tasks such as machine translation and speech recognition. Finally, NLP is already being applied in a number of interesting ways, from helping doctors diagnose diseases to powering chatbots. All of these factors together suggest that the future of natural language processing is bright.