Use of Natural Language Processing in Extracting Data in Banks

 A data management executive at the Bank of America in Charlotte, NC, Bryan M. Wood worked as a credit risk analytics manager for Wells Fargo in Charlotte, NC. As a data management executive, Bryan Mitchell Wood of Charlotte, NC, built a technology platform to help extract and classify data from documents. The platform leverages optical character recognition, image recognition, and natural language processing (NLP).

Natural language processing (NLP) is a subset of artificial intelligence (AI) that focuses on programming computers to understand, interpret and generate human language. Banks process vast volumes of data daily. Using conventional methods of extracting data involves a lot of manual labor and is time-consuming. However, using NLP, banks can automate extracting relevant information from unstructured data, saving time and effort. By analyzing text data, NLP algorithms can scan documents, detect common words and group them.

NLP can also identify the sentiments of a piece of data, whether positive, neutral, or negative. Sentiment analysis is vital for banks to gauge public opinion on their products and services. These insights can improve customer service, product design, and risk management. Another advantage of using NLP in banking is that it enables banks to monitor and analyze customer feedback in real time. This helps banks to identify and resolve customer complaints quickly, which leads to better customer satisfaction.