Applications of Natural Language Processing (NLP) in Finance

NLP, or natural language processing is a branch of artificial intelligence (AI), with the development of machine learning (ML) algorithms and improved computational power. NLP and ML have emerged as the go-to technology for traders, portfolio managers, analysts, banks, and other financial organizations as they work to improve their financial analyses.

Fremont, CA: The use of natural language comes from various disciplines, including linguistics, neurobiology, mathematics, and computer science. This interdisciplinary topic is known as natural language processing or NLP.  NLP is a branch of artificial intelligence (AI), with the development of machine learning (ML) algorithms and improved computational power. The viability of NLP models has expanded to many other languages besides English, making it possible for platforms to implement machine translation algorithms that are nearly error-free.

NLP technologies, methodologies, and APIs (Application Programming Interfaces) are now widely used across various industries, including finance, to transform raw textual data into insightful information that may be used in many financial domains. NLP and ML have emerged as the go-to technology for traders, portfolio managers, analysts, banks, and other financial organizations as they work to improve their financial analyses. Working with unstructured financial data requires using NLP techniques such as sentiment analysis, chatbot question-answering, document classification, and topic clustering.

NLP applications in finance

1. Risk evaluations

Based on a credit risk assessment, banks can estimate the likelihood that a loan payment will be successful. The payment capability is often determined using information on previous spending habits and loan repayment history. However, this information is only sometimes available, particularly for underprivileged people. A rough estimate states that poverty prevents over half of the world's population from using financial services. NLP can help with this issue. NLP approaches evaluate credit risk using a variety of data points.

2. The state of the economy

Information regarding certain stocks is essential for successful stock market trading. Based on this knowledge, Traders can purchase, hold, or sell a stock. In addition to scrutinizing quarterly financial reports, it's critical to understand what analysts say about those businesses; you may find this information on social media. Monitoring such information inside social media posts and choosing prospective trade opportunities are also part of social media analysis. For instance, the stock price may decline when news of a CEO's resignation is reported since it frequently carries a negative message.

3. Auditing process and accounting

After dealing with endless daily transactions and invoice-like papers for decades, businesses now understand how important NLP is to get a major advantage in the audit process. Financial experts can use NLP to quickly find, concentrate on, and visualize anomalies in regular transactions. Finding abnormalities in the transactions and their causes takes less time and effort when the proper technology is used. Identification of substantial potential dangers can be aided using NLP. The detection of important potential dangers and probable fraud, including money laundering, can be aided by NLP.

4. Portfolio optimization and selection

Without being aware of the underlying distribution caused by stock prices, the main objective of any investor is to maximize their capital over the long term. Data science, machine learning, and nonparametric statistics can be used to anticipate investment strategies in the financial stock markets. The start of the trade period and a portfolio can be predicted using the historical data gathered. Investors might use this data to allocate their present capital among the available assets.

5. Stock market behavior forecasts

Deep learning and NLP can significantly outperform earlier approaches for analyzing financial time series. When coupled, these two technologies can handle a lot of information. Because of the fluctuating and irregular data and the long-term and seasonal variations that can lead to significant inaccuracies in the analysis, forecasting time series for financial analysis is challenging.

Many deep learning algorithms have begun to outperform humans in the previous five years at various tasks, including speech recognition and medical image analysis. Recurrent neural networks (RNN) are a particularly efficient approach to forecasting time series in the financial sector.