The term “Natural Language Processing”, or NLP for short, is being covered with increasing frequency in the news cycle, yet ALL these “new” AI-focused terms are not necessarily familiar ones. Each week we delve into new topics in our AI EDUCATION series, and this week – with the help of Copilot & ChatGPT for research – we cover NLP in a quick read format, just the way we like to present content for our readers. CHECK IT OUT!
Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on the interaction between computers and humans through natural language. The ultimate objective of NLP is to read, decipher, understand, and make sense of human language in a valuable way.
Below you’ll find information on how NLP is impacting the financial services sector.
Pros of Natural Language Processing
- Efficiency and Automation: NLP can automate the process of extracting information from financial documents, saving countless hours of manual work.
- Sentiment Analysis: NLP can analyze social media and news articles to gauge public sentiment about financial markets or specific stocks (this could also be a negative, btw, if social media has been spammed with opinions from bots and paid trolls, which we all know happens).
- Customer Service: NLP can power chatbots and virtual assistants, providing instant responses to customer queries, thereby improving customer experience.
Cons of Natural Language Processing
- Context Understanding: NLP may struggle with understanding the context, especially in the financial world where a single word can have multiple meanings.
- Sarcasm and Irony: These subtle forms of communication can be challenging for NLP to understand, leading to misinterpretation.
- Language Diversity: The vast array of languages, dialects, and colloquialisms can pose a significant challenge for NLP.
Current Use Cases
- Robo-Advisors: Many financial institutions use NLP in robo-advisors to provide personalized financial advice based on the client’s financial history and goals.
- Risk Management: NLP is used to analyze legal and regulatory documents, helping to identify potential risks and ensure compliance.
- Market Analysis: NLP is used to analyze news articles, social media posts, and financial reports to predict market trends.
Future Use Cases
- Real-Time Translation: As NLP improves, we can expect real-time translation of financial documents, making global finance more accessible.
- Advanced Sentiment Analysis: Future NLP models will be better at understanding context, sarcasm, and irony, leading to more accurate sentiment analysis.
- Personalized Banking: With advancements in NLP, we can expect highly personalized banking experiences, with services tailored to individual’s specific needs and preferences.
Finally, when I queried CoPilot to provide me with 5 examples of companies using NLP, it managed to get to 3. For the record, I am sure there are thousands of firms who utilize NLP, so not sure why it only manged to find these3 companies, but at any rate, Builtin.com was cited as its reference source, fyi. Interestingly, 2 of the 3 were in medicine and the 3rd is a name many of us known, Grammaly, which is used to help write more effectively.
- Iodine Software: Based in Austin, Texas, Iodine Software is an enterprise AI company that focuses on automating complex clinical tasks, generating insights, and empowering intelligent care in healthcare. Their groundbreaking clinical machine-learning engine, Cognitive ML, interprets raw clinical data to provide real-time, clinically-informed predictive insights for healthcare professionals and administrators.
- Pfizer: A global pharmaceutical giant, Pfizer combines artificial intelligence, machine learning, and NLP to enhance drug discovery, development, and patient outcomes. Their purpose revolves around sourcing the best science, improving access to medicines, using digital technologies to enhance drug discovery and development, and leading conversations to advocate for pro-innovation and pro-patient policies.
- Grammarly: Known for its real-time writing assistance, Grammarly helps 30 million individuals and 30,000 teams write more clearly and effectively. Their AI-driven suggestions enhance communication quality, efficiency, and consistency in everyday writing, work, and school contexts.
In conclusion, while NLP has its challenges, its potential benefits in the financial (and other) sector(s) are immense. As technology advances, we can expect to see even more innovative applications of NLP in finance. Stay tuned!
ChatGPT, Copilot and DWN Staff
