In order to gain a competitive advantage, financial institutions are increasingly relying on powerful and adaptable technology. This technology is known as natural language processing or NLP. It is a branch of Artificial Intelligence and linguistics. NLP allows computers to comprehend and communicate with humans using natural language. The huge rise in processing power has unleashed the potential of Machine Learning (ML) techniques. It has greatly contributed to making NLP more scalable and trustworthy. It is especially valuable in fields where speed and precision are typically required for competitive advantage, like financial services, because it can help professionals make better decisions faster. Here are four examples of where NLP is already flourishing:
Reporting and auditing
Significant data elements in unstructured financial statements, purchase orders, invoices, or other payment documentation can be detected using NLP in conjunction with ML algorithms. Financial auditing is similar in that financial records are inspected and classified for anomalies or deviations.
Credit Checks
To establish a customer’s creditworthiness, it can be used in conjunction with credit score software. It gathers relevant information from documents. For example, loan applications, income/expense statements, investments, and so on, are then evaluated by credit scoring software to determine a credit score.
Natural Language Processing (NLP) for Fraud-Prevention
NLP, in conjunction with machine learning and predictive analytics, can be used to detect fraud and misread data. In order to reduce fraudulent activity, banking organizations, for example, use NLP for Know Your Customer (KYC) and Anti-Money Laundering (AML) applications. So they extract and analyze data from a customer’s personal details, spending patterns, and financial behavior. Similarly, in order to detect fraudulent claims, NLP can be used in conjunction with a robotic process automation solution to inspect and read submitted documents.
