One of the trendiest and most discussed topics in data science now is Natural language processing. Many companies and individuals are investing lots of time and money in studying and implementing this field in their daily lives. Similarly, everyone is trying to understand NLP and its application as a potential professional field. In some way, most companies are fighting hard to incorporate its use in their operations.
Considering the fast growth of Natural Language Processing within a short period, it is important to understand its application, influence and strength in business world.
What is NLP in The Business World?
Natural Language Processing involves several techniques essential for enterprises and companies that deal with large volumes of unstructured data like online chats, emails, survey replies, social network dialogues and other data types. Therefore, companies that have introduced Natural Language Processing in their workplace can analyze large amounts of data and get a meaningful message within the noise. As a result, these companies can get useful insights that can help them automate certain processes and boost their business decisions.
Different organizations are investing heavily on Natural Language Processing since it can help them enhance their financial game in this digital era. By definition, NLP is an AI field that helps computers understand human language by using Machine Learning methods. Natural Language Processing is mostly associated with activities that aim at improving human-machine interactions, like virtual agents or customer chatbots.
We can all agree that data is a driving force in today’s finance sector. Therefore, you can find important information and data in written form like, texts, documents, forums, websites and other areas. Also, finance professionals spend most of their time reviewing financial print publications, analytic reports and other data sources. With the help of ML (machine learning) and NLP techniques, it will be easy to build an efficient financial infrastructure and improve decision-making abilities in real-time.
Application of NLP in finance
NLP algorithms and approaches are important in translating raw data into relevant information in different financial domains. Banks, traders, analysts, financial institutions and portfolio managers are looking for better ways to do their financial research and Natural Language Processing has proven to be among the top choice technology.
Recently, Natural Language Processing has become the savior and more dependable in the financial sector by providing financial decision-makers with the necessary knowledge to understand the market. Here are a few examples of how Natural Language Processing is changing the financial sector.
Stock Behaviour Prediction
One of the difficult processes in financial analysis is predicting time series considering the irregular and fluctuating data. Also, seasonal and long-term data fluctuations can cause great mistakes in the analysis. Thanks to deep learning integrated with NLP, it is now easy to work with financial time series. Another great advantage is that the two technologies can handle a huge volume of unstructured data quickly.
One of the best examples of approaches that have proven to be useful in predicting time series like stock prices in the financial spectrum is Recurrent Neural Network (RNN). Due to the high precision that NLP offers alongside Deep Learning, they are used for making stock trading decisions and predicting stock price patterns and volatility.
Risk assessment
Banks used credit risk assessment to estimate the chances of successful loan payback. Therefore, they will use parameters like loan payment history and spending habits data to determine payment capacity. The challenge comes when trying to access this information: these data are missing in most situations.
NLP can help with the situation. NLP approaches can use different data points during loan risk assessment. For instance, Natural Language Processing can be used to study entrepreneurial mentality and attitude in company financing. Also, this technology can flag incomplete data or data that does not make sense and call for further investigation.
Analysing Financial Documents
It is possible for users to integrate their financial solution documents into current workflows without interfering with the existing processes by using artificial intelligence technology. Therefore, financial professionals can implement use cases of Natural Language Processing to automate the reading and interpretation processes. This, in turn, will help deal with large volumes of financial documents at the same time. Existing documentation resources can be used to train Natural Language Processing models.
In a nutshell
Natural Language Processing has grown and improved in consistency, scalability, reliability and accuracy over the years now. In return, it gives financial decision-makers a better understanding of the market. Also, NLP reduces tedious duties, analyzing risk, speeding up negotiations, building portfolios and comprehending financial emotion in the banking sector.
