Natural Language Processing in Finance Market Size

Natural Language Processing in Finance Market Size

Natural Language Processing (NLP) in Finance Market size was valued at USD 5.5 billion in 2023 and is anticipated to grow at a CAGR of over 25% between 2024 and 2032. The growing advancements in Artificial Intelligence (AI) and Machine Learning (ML) for market is changing the way financial firms and institutions operate, helping to enhance the customer experiences, improve the decision-making process, and streamline operations. AI-driven NLP systems provide support to the firms for survey of customer data and offer personalized financial advice with recommendations, helping the clients make informed decisions about investments, savings, and spending.

For instance, in June 2023, Amazon Web Services (AWS) notified that Banco Bilbao Vizcaya Argentaria, S.A. (BBVA), is set to explore the advanced technologies such as Amazon Bedrock. Amazon Bedrock is providing ingress to foundation models from Amazon and leading AI startups through an Application Programing Interface (API), which BBVA aims to hold and create innovative financial solutions.
 

The growing volume of unstructured data in the finance industry creates opportunities for organizations in the NLP industry as unstructured data includes e-mails, social media posts, news articles, financial reports, customer reviews, and other text-heavy formats that do not fit neatly into traditional databases. NLP is playing a critical role in harnessing this data to extract valuable insights and enhance various aspects of financial operations. Various banks and institutions are shifting toward NLP to understand & respond to customer inquiries, providing personalized financial advice, transaction details, and alerts.
 

Integrating NLP solutions with legacy systems in the finance market presents several complexities. Financial institutions rely on legacy systems, making integration a challenging process. Legacy systems often operate in silos, making it difficult to integrate data seamlessly. NLP solutions require access to vast amounts of data, and the challenge lies in ensuring compatibility and smooth data flow between disparate systems. Legacy systems are based on outdated hardware & software infrastructure that lacks the capabilities to support advanced NLP algorithms and processing power.