Natural Language Processing (NLP) in Finance

Natural Language Processing (NLP) in Finance

Global Natural Language Processing (NLP) in Finance Market to Reach US$28.7 Billion by 2030

The global market for Natural Language Processing (NLP) in Finance estimated at US$5.9 Billion in the year 2023, is expected to reach US$28.7 Billion by 2030, growing at a CAGR of 25.4% over the analysis period 2023-2030. NLP in Finance Software, one of the segments analyzed in the report, is expected to record a 20.3% CAGR and reach US$13.8 Billion by the end of the analysis period. Growth in the NLP in Finance Services segment is estimated at 32.2% CAGR over the analysis period.

The U.S. Market is Estimated at US$1.6 Billion While China is Forecast to Grow at 24.3% CAGR

The Natural Language Processing (NLP) in Finance market in the U.S. is estimated at US$1.6 Billion in the year 2023. China, the world`s second largest economy, is forecast to reach a projected market size of US$4.3 Billion by the year 2030 trailing a CAGR of 24.3% over the analysis period 2023-2030. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at a CAGR of 22.1% and 21.8% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 17.5% CAGR.

Global Natural Language Processing (NLP) in Finance Market - Key Trends and Drivers Summarized

Natural Language Processing (NLP) is revolutionizing the financial sector by enhancing how financial institutions manage and analyze the vast amounts of unstructured data they accumulate. This data, which is growing annually by 55-65%, includes everything from customer communications and regulatory filings to financial news articles and social media content. Through NLP, which is a subset of artificial intelligence, financial companies are able to automate the processing of this data and extract actionable insights more efficiently. NLP applications in finance include automating document classification, entity recognition, and extraction of financial relationships, which collectively improve decision-making and operational efficiency. By transforming unstructured data into structured, actionable information, NLP enables financial institutions to enhance accuracy, save time, and reduce the need for extensive manual intervention in data handling.

The growth of NLP in finance is driven by several critical factors. The exponential increase in unstructured data necessitates robust tools for data extraction and analysis, making NLP invaluable for financial institutions aiming to leverage this data for strategic planning and decision-making. Enhancing customer experience is another major driver, with NLP enabling more personalized customer interactions through technologies such as chatbots and virtual assistants. These tools automate responses and provide tailored financial advice, significantly improving customer engagement. Additionally, NLP technologies increase operational efficiency by automating routine tasks such as data entry and report generation, which not only accelerates these processes but also minimizes the errors associated with manual tasks. Compliance and risk management are also facilitated by NLP, as it helps monitor and analyze communications to ensure adherence to regulatory standards and identify risky behaviors or transactions.

Advancements in AI and machine learning have significantly improved the capabilities of NLP, making these tools more accurate and efficient in processing natural language. This technological evolution is crucial as it enhances the utility of NLP in financial applications, allowing for better decision-making through deeper insights into market trends and customer behavior. Moreover, the integration of NLP with existing IT infrastructures, including legacy systems, is relatively seamless, promoting its adoption within the financial sector. The competitive pressure among financial institutions to adopt NLP is also a key growth driver, as firms seek to remain relevant and competitive in a rapidly evolving digital landscape. As real-time analytics become increasingly important in finance, NLP supports the timely decision-making required in dynamic markets. These factors collectively contribute to the widespread adoption and ongoing development of NLP in finance, setting new standards for efficiency and customer service in the industry.

Select Competitors (Total 23 Featured) -

• Amazon Web Services, Inc.
• Basis Technology Corporation
• Expert.ai
• IBM Corporation
• John Snow Labs Inc.
• Oracle Corporation
• Qualtrics
• SAS Institute, Inc.
• Softengi

Please note: Reports are sold as single-site single-user licenses. Electronic versions require 24-48 hours as each copy is customized to the client with digital controls and custom watermarks. The Publisher uses digital controls protecting against copying and printing is restricted to one full copy to be used at the same location.

The latest version of Adobe Acrobat Reader is required to view the report. Upon ordering an electronic version, the Publisher will provide a link to download the purchased report.

Prior to fulfillment of an order, the client will be required to sign a document detailing the purchase terms for a publication from this publisher.