Natural Language Processing (NLP) is an off-shoot of Artificial Intelligence (AI) that assists AI to understand human language (Natural language). Human or natural language means the way humans interact with each other. Humans have been nurtured in such an environment where they understand the meaning under the meaning of a statement. Two separate sentences when combined have a different meaning altogether. Human languages in general have a lot of exceptions in grammar and vocabulary. Machine language or Programming Language comparatively is fairly straightforward. The nuances of natural language slip right past AI.
This is where NLP comes to the rescue. NLP uses several tools to understand the basic techniques of word definition, phrases, sentences, and texts, as well as syntactic (knowledge of word meanings and vocabulary) and semantic processing (understanding the combination of phrases). It also develops applications such as machine translation (MT), question-answering (QA), data retrieval, discussion, document production, and recommendation programs.
NLP models can also be trained to dissect unstructured content and underlying issues or trends that may impact financial markets. Content enrichment and sentiment analysis can be used to make informed investment decisions, and streamline risk management and compliance, especially due to COVID-19 (Learn about how NLP is being used during COVID-19).
An overload of information is a common problem in the financial services industry. Traders and investment managers have x-number of sources to wade through, such as research reports, company filings, and transcripts of quarterly earnings calls. The unstructured content is already immense and is only accelerating at an unprecedented rate, which makes the entire process a long one.
As a result, unstructured content is kept in the dark as a source of discernment. The dataset will provide much better insight for the right trading practices at the right time but the towering wall of information makes it impossible to spot the nuances that could initiate a decision-making process. NLP can assist in uncovering important insights to improve trade practice from this mammoth task to create structure in this overwhelming volume of information. NLP provides speed, accuracy, efficiency, and consistency by using various tools like speech recognition, content enrichment, and Sentiment analysis.
Speech recognition is an essential tool for analyzing the companies’ quarterly or semi-annual earnings calls. Corporate conferences are usually introduced with a presentation of the summary of the previous quarter and the outline for the next quarter followed by a Q&A session. NLP helps in understanding the flow and tone of the conference which makes it easier for traders to understand the entirety of the company’s performance.
NLP is also used to create structure from unstructured text. This process is called ‘Named Entity Recognition’ (NER) and is applied in the detection and labeling of entities, i.e, real-world concepts, like people or companies. NER effectively creates the structure of the data provided by tagging it with machine-readable metadata combined with an ontology (the domain that shows their properties and the relations between them). NLP can also be redirected and used for supporting the banks’ compliance processes. Tagging unstructured data provides the opportunity to make use of the infinite digital documents that would usually go undetected. This allows compliance officers to create a much better plan for compliance processes.
NER is a tool of NLP that has multiple uses. It helps in linking entities and building a graph of relationships. For instance, an entity-modeling system can pick out key pointers of specific topics within the unstructured text and create new connections. It also assists with keeping track of relationships between entities, with the potential to detect money laundering or fraud.
Sentiment analysis simply means understanding the emotion behind a statement. It is a tool of NLP that helps AI (Artificial Intelligence) understand the human emotion behind a document or statement. When we humans communicate with each other, we use ‘natural language’ and can easily understand the intent or emotion behind a statement but for a machine, it is quite messy and difficult to understand. Sentiment analysis can determine the subjective meaning from text relatively efficiently. It is an ideal tool for taking a look at unstructured content about a specific company to inspect for inconsistencies and anomalies.
The main issue when it comes to dealing with compliance is the sheer volume of data that traders, compliance officers, and companies have to deal with. Especially when it comes to the unlabeled dataset. It is not humanly possible to use each and every piece of information that comes into the system. This is why NLP is extremely useful. Not only does it create structure in the chaos, but it also has tools under its belt to determine the emotion and tone behind certain types of datasets to give a holistic view of the data which helps the respective parties to make informed decisions.
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