NLP in Finance Market by Offering (Software, Services), Application (Customer Service and Support, Risk Management and Fraud Detection, Sentiment Analysis), Technology (Machine Learning, Deep Learning), Vertical and Region - Global Forecast to 2028
[376 Pages Report] The NLP in finance market is projected to grow from USD 5.5 billion in 2023 to USD 18.8 billion by 2028 at a compound annual growth rate (CAGR) of 27.6%. 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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Market 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.
Key Market Players
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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Report Metrics |
Details |
Market size available for years |
2019–2028 |
Base year considered |
2022 |
Forecast period |
2023–2028 |
Forecast units |
USD (Billion) |
Segments covered |
Offering, Technology, Application, Vertical, and Region |
Geographies covered |
North America, Asia Pacific, Europe, the Middle East & Africa, and Latin America |
Companies covered |
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), expert.ai (Italy), LivePerson (US), Veritone (US), Automated Insights (US), Bitext (US), Conversica (US), Accern (US), Kasisto (US), Kensho (US), ABBYY (US), Mosaic (US), Uniphore (US), Observe.AI (US), Lilt (US), Cognigy (Germany), Addepto (Poland), Skit.ai (US), MindTitan (Estonia), Supertext.ai (India), Narrativa (US), and Cresta (US). |
This research report categorizes the NLP in finance market based on offering, technology, application, vertical, and region.
By Offering:
- Software
- Rule-based NLP Software
- Regular Expression (Regex)
- Finite State Machines (FSMs)
- Named Entity Recognition (NER)
- Part-of-speech (POS) Tagging
- Statistical NLP Software
- Naive Bayes
- Logistic Regression
- Support Vector Machines (SVMs)
- Recurrent Neural Networks (RNNs)
- Hybrid NLP software
- Latent Dirichlet Allocation (LDA)
- Hidden Markov Models (HMMs)
- Conditional Random Fields (CRFs)
- Services
- Professional Services
- Training and Consulting
- System Integration and Implementation
- Support and Maintenance
- Managed Services
By Technology:
-
Machine Learning
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
-
Deep Learning
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Transformer Models (BERT, GPT-3, etc.)
-
Natural Language Generation
- Automated Report Writing
- Customer Communication
- Financial Document Generation
-
Text Classification
- Sentiment Classification
- Intent Classification
-
Topic Modeling
- Topic Identification
- Topic Clustering
- Topic Visualization
-
Emotion Detection
- Emotion Recognition
- Emotion Classification
- Other Technologies (Named Entity Recognition, Event Extraction)
By Application:
-
Sentiment Analysis
- Brand Reputation Management
- Market Sentiment Analysis
- Customer Feedback Analysis
- Product Review Analysis
- Social Media Monitoring
-
Risk Management and Fraud Detection
- Credit Risk Assessment
- Fraud Detection and Prevention
- Anti-money laundering (AML)
- Compliance Monitoring
- Cybersecurity and Threat Detection
-
Compliance Monitoring
- Regulatory Compliance Monitoring
- KYC/AML Compliance Monitoring
- Legal and Policy Compliance Monitoring
- Audit Trail Monitoring
- Trade Surveillance
-
Investment Analysis
- Asset Allocation and Portfolio Optimization
- Equity Research and Analysis
- Quantitative Analysis and Modeling
- Investment Recommendations and Planning
- Risk Management and Prediction
- Investment Opportunity Identification
-
Financial News and Market Analysis
- Financial News and Analysis
- Stock Market Prediction
- Macroeconomic Analysis
-
Customer Service and Support
- Chatbots and Virtual Assistants
- Personalized Support and Service
- Complaint Resolution
- Query Resolution and Escalation Management
- Self-service Options
-
Document and Contract Analysis
- Contract Management
- Legal Document Analysis
- Due Diligence Analysis
- Data Extraction and Normalization
-
Speech Recognition and Transcription
- Voice-enabled Search and Navigation
- Speech-to-Text Conversion
- Call Transcription and Analysis
- Voice Biometrics and Authentication
- Speech-enabled Virtual Assistants
-
Language Translation
- Financial Document Translation
- Investment Research Translation
- Multilingual Customer Service and Support
- Cross-border Business Communication
- Localization and Internationalization
- Other Applications (CRM Optimization, Underwriting Assistance)
By Vertical:
-
Banking
- Retail Banking
- Corporate Banking
- Investment Banking
- Wealth Management
-
Insurance
- Life Insurance
- Property and Casualty Insurance
- Health Insurance
-
Financial Services
- Credit rating
- Payment Processing and Remittance
- Accounting and Auditing
- Personal Finance Management
- Robo-advisory
- Cryptocurrencies and Blockchain
- Stock Movement Prediction
- Other Enterprise Verticals
- Retail and E-commerce
- Manufacturing
- Healthcare and Life Sciences
- Energy and Utilities
- Transportation and Logistics
By Region:
-
North America
- US
- Canada
-
Europe
- UK
- Germany
- France
- Italy
- Spain
- Switzerland
- Rest of Europe
-
Asia Pacific
- China
- India
- Japan
- South Korea
- Singapore
- Australia and New Zealand
- Rest of Asia Pacific
-
Middle East and Africa
- Saudi Arabia
- UAE
- South Africa
- Israel
- Rest of the Middle East & Africa
-
Latin America
- Brazil
- Mexico
- Argentina
- Rest of Latin America
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.
Frequently Asked Questions (FAQ):
What is NLP in Finance?
NLP is a machine learning technology that gives computers the ability to interpret, manipulate, and comprehend human language. Financial organizations today have large volumes of voice and text data from various communication channels like emails, text messages, social media newsfeeds, video, audio, and more. They use NLP software to automatically process this data, analyze the intent or sentiment in the message, and respond in real time to human communication.
What is the total CAGR expected for the NLP in Finance market during 2023-2028?
The market is expected to record a CAGR of 27.6% from 2023-2028.
Which are the key drivers supporting the growth of the NLP in finance market?
Some factors driving the growth of the NLP in finance market include the rise in content creation and creative applications, evolution in AI and Deep Learning, innovation of cloud storage enabling easy access to data, and acceleration in the deployment of Large Language Models (LLMs).
Which are the key technology trends prevailing in the NLP in Finance market?
The three key technologies gaining a foothold in the NLP in Finance market are machine learning, deep Learning, and natural language generation. Apart from these three, other prominent technologies include text classification, topic modeling, emotion detection, named entity recognition, and event extraction.
Who are the key vendors in the NLP in Finance market?
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), Expert.ai (Italy), LivePerson (US), Veritone (US), Automated Insights (US), Bitext (US), Conversica (US), Accern (US), Kasisto (US), Kensho (US), ABBYY (US), Mosaic (US), Uniphore (US), Observe.AI (US), Lilt (US), Cognigy (Germany), Addepto (Poland), Skit.ai (US), MindTitan (Estonia), Supertext.ai (India), Narrativa (US), and Cresta (US).
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The research study for the NLP in finance market involved extensive secondary sources, directories, journals, and paid databases. Primary sources were mainly industry experts from the core and related industries, preferred NLP in finance providers, third-party service providers, consulting service providers, end-users, and other commercial enterprises. In-depth interviews were conducted with primary respondents, including key industry participants and subject matter experts, to obtain and verify critical qualitative & quantitative information and assess the market’s prospects.
Secondary Research
In the secondary research process, various sources were referred for identifying and collecting information for this study. Secondary sources included annual reports, press releases, and investor presentations of companies; white papers, journals, and certified publications; and articles from recognized authors, directories, and databases. The data was also collected from other secondary sources, such as journals, government websites, blogs, and vendor websites. Additionally, the spending of various countries on NLP in finance was extracted from the respective sources. Secondary research was mainly used to obtain the key information related to the industry’s value chain and supply chain to identify the key players based on solutions, services, market classification, and segmentation. This was done in accordance with the offerings of the major players, industry trends related to solutions, services, technologies, applications, verticals, and regions, and the key developments from both market- and technology-oriented perspectives.
Primary Research
In the primary research process, various sources from both supply and demand sides were interviewed to obtain qualitative & quantitative information on the market. The primary sources from the supply side included various industry experts, including chief experience officers (CXOs), vice presidents (VPs), directors from business development, marketing, and NLP in finance expertise; related key executives from NLP in finance solution vendors, SIs, professional service providers, and industry associations; and the key opinion leaders.
Primary interviews were conducted to gather insights, such as market statistics, revenue data collected from solutions & services, market breakups, market size estimations, market forecasts, and data triangulation. Primary research also helped understand various trends related to technologies, applications, deployments, and regions. Stakeholders from the demand side, such as chief information officers (CIOs), chief technology officers (CTOs), chief strategy officers (CSOs), and end-users using NLP in finance solutions, were interviewed to understand the buyer’s perspective on suppliers, products, service providers, and their current usage of NLP.
The Breakup of Primary Research:
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COMPANY NAME |
DESIGNATION |
Skit.ai |
Senior Conversational User Experience Designer |
Expert.ai |
Senior Project Manager |
Uniphore |
Senior Solutions Consultant |
|
Senior NLP Researcher |
Market Size Estimation
In the bottom-up approach, For cross-validation, the adoption of NLP in finance solutions & services among industries, along with different use cases with respect to their regions, was identified and extrapolated. Weightage was given to use cases identified in different regions for the market size calculation.
Based on the market numbers, the regional split was determined by primary and secondary sources. The procedure included the analysis of the NLP in finance market’s regional penetration. Based on secondary research, the regional spending on information and communications technology (ICT), socio-economic analysis of each country, strategic vendor analysis of major NLP in finance providers, and organic and inorganic business development activities of regional and global players were estimated. With the data triangulation procedure and data validation through primaries, the exact values of the overall NLP in finance market size and segments’ size were determined and confirmed.
Global NLP in Finance Market Size: Bottom-Up Approach:
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Global NLP in Finance Market Size: Top-down Approach
Data Triangulation
Based on the market numbers, the regional split was determined by primary and secondary sources. The procedure included the analysis of the NLP in finance market’s regional penetration. Based on secondary research, the regional spending on information and communications technology (ICT), socio-economic analysis of each country, strategic vendor analysis of major NLP in finance providers, and organic and inorganic business development activities of regional and global players were estimated. With the data triangulation procedure and data validation through primaries, the exact values of the overall NLP in finance market size and segments’ size were determined and confirmed.
Market Definition
NLP uses machine learning to reveal the structure and meaning of the text. With natural language processing applications, organizations can analyze text and extract information about people, places, and events to better understand social media sentiment and customer conversations. NLP technology for the finance sector is built on advanced algorithms trained on vast amounts of financial data to improve the accuracy and performance of financial models–such as credit risk assessment, market sentiment analysis, and asset management.
Stakeholders
- NLP in finance software vendors
- NLP in finance service providers
- Managed service providers
- Support and maintenance service providers
- System integrators (SIs)/migration service providers
- Value-added resellers (VARs) and distributors
- Independent software vendors (ISVs)
- Third-party providers
- Technology providers
Report Objectives
- To define, describe, and predict the NLP in finance market by offering (software and services), application, technology, vertical, and region
- To provide detailed information related to major factors (drivers, restraints, opportunities, and industry-specific challenges) influencing the market growth
- To analyze the micro markets with respect to individual growth trends, prospects, and their contribution to the total market
- To analyze the opportunities in the market for stakeholders by identifying the high-growth segments of the NLP in finance market
- To analyze opportunities in the market and provide details of the competitive landscape for stakeholders and market leaders
- To forecast the market size of segments for five main regions: North America, Europe, Asia Pacific, the Middle East & Africa, and Latin America
- To profile the key players and comprehensively analyze their market ranking and core competencies
- To analyze competitive developments, such as partnerships, product launches, and mergers and acquisitions, in the NLP in finance market
- To analyze the impact of recession in the NLP in finance market across all the regions
Available Customizations
With the given market data, MarketsandMarkets offers customizations as per the company’s specific needs. The following customization options are available for the report:
Product Analysis
- Product quadrant, which gives a detailed comparison of the product portfolio of each company.
Geographic Analysis
- Further breakup of the North American NLP in finance market
- Further breakup of the European NLP in finance market
- Further breakup of the Asia Pacific NLP in finance market
- Further breakup of the Middle Eastern & African NLP in finance market
- Further breakup of the Latin America NLP in finance market
Company Information
- Detailed analysis and profiling of additional market players (up to five)
Growth opportunities and latent adjacency in NLP in Finance Market