NLP in Finance Market

Natural Language Processing (NLP) in Finance Market - Size, Growth, Report & Analysis

[376 Pages Report] The global NLP in Finance Market size was surpassed $5.5 billion in 2023 and is anticipated to rise over $18.8 billion by the end of 2028, projecting a CAGR of 27.6% during the forecast period(2023-2028). The base year for estimation is 2022 and the market size available for the years 2023 to 2028.

The NLP in finance market is estimated to witness significant growth during the forecast period, attributed to the increasing demand for automated and efficient financial services. The rising need for accurate and real-time analysis of complex financial data and the emergence of AI and ML models that enable enhanced NLP capabilities in finance are also major growth drivers.

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NLP in Finance Market Growth Dynamics

Driver: Increasing demand for automated and efficient financial services across the globe

The adoption of NLP in the finance industry has been driven by the increasing demand for automated and efficient financial services worldwide. The use of NLP technology has become increasingly popular among financial institutions as they strive to provide personalized financial solutions that are cost-effective, efficient, and easily accessible to customers.

One of the key areas of delivering enhanced financial services is to improve customer service. Financial institutions are using NLP-powered chatbots to provide instant assistance to their customers, which has led to significant cost savings and improved customer satisfaction levels. These chatbots can answer frequently asked questions, provide information on account balances, and assist with money transfers. For example, Bank of America’s chatbot, Erica, has assisted over 15 million customers with their banking needs, resulting in a 19% reduction in customer service costs.

Restraints: Difficulty in managing large volumes of unstructured data

One of the primary reasons for the difficulty in managing large volumes of unstructured data is the lack of standardization. Unstructured data comes in different formats and types, such as text, images, and videos, making extracting meaningful insights challenging. Financial institutions often rely on manual processing, which can be time-consuming, expensive, and prone to errors.

Another factor contributing to the same is the lack of sophisticated tools to handle the complexities of unstructured data. Traditional data analysis tools were designed to handle structured data and are often ill-equipped to handle unstructured data. As a result, financial institutions are turning to advanced technologies such as natural language processing (NLP) to help them manage and analyze their data effectively.

Opportunity: Development of customized NLP solutions for specific financial services and use cases

The finance industry is witnessing rapid growth in the adoption of Natural Language Processing (NLP) techniques. NLP is used to analyze unstructured data, such as news articles, social media posts, and earnings call transcripts, to extract valuable insights and drive informed decision-making. However, the lack of standardization in NLP-based financial applications and services, difficulty in managing large volumes of unstructured data, and the complexity in developing and training sophisticated NLP models are major restraints that hinder the market growth.

Despite these challenges, the market opportunity for NLP in the finance industry remains significant. The development of customized NLP solutions & services for specific financial use cases is a major market opportunity. For instance, banks can use NLP to extract valuable insights from customer feedback to improve their products and services. Similarly, investment firms can use NLP to analyze market sentiments and news articles to make informed investment decisions.

Challenge: High implementation costs associated with NLP

The high cost of implementation can be a significant barrier to entry for smaller financial institutions, which may not have the resources or expertise to effectively implement NLP solutions. Hence, this factor can lead to a widening gap between larger and smaller financial institutions, with the former being better equipped to leverage the benefits of NLP in their operations. For example, a financial institution implementing an NLP-powered chatbot may need to invest in additional hardware and software to support the application, as well as hire specialized developers and data scientists to build and maintain the underlying NLP model. The costs of training employees on how to use the chatbot and monitor its performance may also add to the total cost of ownership.

NLP in Finance Market Ecosystem

Software segment to account for larger market size during forecast period

The market is expected to continue growing at a rapid pace due to the increasing demand for NLP tools in the finance industry. The adoption of machine learning algorithms for NLP has significantly improved the accuracy and efficiency of NLP solutions in the finance industry. Machine learning-based NLP tools are capable of processing large volumes of data and providing more accurate and personalized insights. The use of chatbots and virtual assistants powered by NLP is gaining popularity among financial institutions. These tools provide customers personalized financial advice and support, improving customer engagement and satisfaction.

Deep Learning to register highest CAGR during forecast period

The deep learning segment is projected to witness a higher growth rate during the forecast period. Deep Learning has played a critical role in advancing NLP developments in the finance sector. One of the main advantages of deep Learning is its ability to learn from large and complex datasets, which is particularly important in finance, where a vast amount of data is available. This has led to the development of more accurate and sophisticated NLP models for various applications. For example, deep learning algorithms have been shown to outperform traditional machine learning algorithms in sentiment analysis, resulting in more accurate predictions of market trends and behaviors.

North America to have largest market size during forecast period

North America is expected to have the largest NLP in finance market share. The region has a lot of technological research centers, human capital, and strong infrastructure. Moreover, the rise in technical support and the developed R&D sector in the region fuels the growth of the market. NLP has been widely adopted in the finance industry in North America for various applications, including sentiment analysis, fraud detection, risk management, and customer service. NLP technology has proven useful for analyzing large volumes of unstructured data, such as news articles, social media posts, and customer feedback, to extract valuable insights.

NLP in Finance Comapnies

The NLP in finance solutions and service providers have implemented various types of organic and inorganic growth strategies, such as product launches, product upgradations, partnerships, agreements, business expansions, and mergers and acquisitions to strengthen their offerings. Some major players in the NLP in finance market include Microsoft (US), IBM (US), Google (US), AWS (US), Oracle (US), SAS Institute (US), Qualtrics (US), Baidu (China), Inbenta (US), Basis Technology (US), Nuance Communications (US) and Expert.ai (Italy).

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Scope of the Report

NLP in Finance Market Highlights

This research report categorizes the NLP in Finance Market to forecast revenues and analyze trends in each of the following submarkets:

Recent Developments:

• In December 2022, AWS announced that Stability AI, a community-driven, open-source artificial intelligence (AI) company, has selected AWS as its preferred cloud provider to build and scale its AI models for image, language, audio, video, and 3D content generation.
• In March 2022, Microsoft announced its acquisition of Nuance Communications, a leader in conversational AI and ambient intelligence across industries, including healthcare, financial services, retail, and telecommunications. Driven by a shared vision to build outcomes-based AI, Microsoft, and Nuance will enable organizations across industries to accelerate their business goals.
• In February 2022, Google Cloud, KeyBank, and Deloitte announced an expanded, multi-year strategic partnership to accelerate KeyBank’s commitment to a cloud-first approach to banking.
• In November 2021, IBM launched its latest version of Watson Discovery, a cloud-based platform that uses natural language processing to extract insights from unstructured data in documents.

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