What is NLP?
Natural language processing (NLP) is a subcategory of artificial intelligence (AI) that builds bridges between human language and computer processing. NLP analysis can extract and process information from a wide range of online and print sources, including newspapers, emails, social media posts, financial reports, press releases, shipping information, shopping histories, GPS, satellite data, and databases. Additionally, NLP facilitates the machinated processing of spoken language. Although NLP tools must be further developed to minimize shortfalls, the potential of NLP promises enhanced risk assessments for businesses, organizations, and other stakeholders.
How to operate NLP systems: Supervised Learning Algorithms
NLP is based on supervised or unsupervised learning algorithms. During supervised algorithmic processing, data scientists feed the system a training data set which consists of data, or input, that is labeled according to the data scientist’s desired output. For instance, certain words or phrases, such as “happy,” “sunshine,” or “success,” will be labeled with a positive sentiment, whereas terms like “frustration” or “pain” are labeled with a negative sentiment. Language, however, is not always this straightforward. The human ability to understand language also relies heavily on appropriately interpreting connotation and context-based meaning. This includes ambiguous, sarcastic, ironic, or other rhetorical messages. NLP systems may label a text like “Good job, Mr. President” as positive, even when it is intended to be sarcastic. This is where machine-based learning must be further refined.
How to operate NLP systems: Unsupervised Learning Algorithms
By contrast, unsupervised systems can function entirely without or only partially with pre-labeled training data sets, as they learn to independently interpret language through a large amount of available data that includes language in all its facets and meanings. For both supervised and unsupervised NLP, the machine requires a large amount of data input in order to learn intelligently and generate comprehensive, reliable results. Fortunately, contemporary society creates more data than it ever has before. The number of tweets published per second, for instance, illustrates how much information humans produce every day. In 2013, social media platform Twitter counted an average of 5,700 tweets per second and 500 million tweets per day worldwide. Using such a vast amount of accessible information, NLP technology can analyze data in various languages in a short amount of time. This saves businesses time and cost.
The Benefits of NLP for Risk Management
Utilizing NLP can be beneficial for a variety of reasons. The management of risks associated with investment strategies, due diligence procedures, or a company’s reputation can be enhanced with NLP. NLP tools can help asset managers evaluate and optimize investment strategies based on news articles, social media comments, business-internal documents, and other material. This information can help managers profile mergers and acquisitions, other companies, a client, a business partner, or another investment. An NLP analysis of financial data, company governance documentation, internal documents, legal texts, and contracts can also help law firms and the legal departments of companies minimize risks by assessing discrepancies and noncompliance in due diligence processes. Finally, NLP can also be utilized to monitor public sentiment about a company in order to map out potential reputational risks for a firm. This method can indicate how consumers, clients, and investors may feel about a company. Mexican-based beer brand Corona, for example, may have benefitted from an early analysis of the reputational risk that the company faces since the global spread of the coronavirus COVID-19 in early 2020. While the beer brand has no relation to the virus, a survey in the US showed that sales dropped because consumers associated the brand with the virus.
NLP Market Size
Due to the utility of NLP for evaluating due diligence, asset, and reputational risks, the NLP market worldwide is expected to grow significantly in the future. The NLP market is roughly $12.4 billion large in 2020 and is expected to further expand to $43.3 billion by 2025. Thus, the number of companies utilizing NLP is expected to rise. Companies like Google, UBS wealth management, or S&P Global are already deploying NLP tools to analyze natural language to their benefit.
With the development of improved AI systems and better hardware, the capacity of NLP tools to aptly interpret context-based or rhetorical meaning in multiple languages will improve. What we can expect from future developments in NLP is the capability to coherently summarize texts, automatically write new texts, assess the sentiment of a person speaking, and correctly interpret sarcasm, irony, and rhetorical questions. These can help improve compliance and mitigate business risks.