Natural Language Processing in Finance Market Size - By Component (Software, Services), By Distribution Channel (Online, Offline), By Technology, By Application, By Industry Vertical, Forecast 2024 – 2032

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 Legacy systems are based on outdated hardware & software infrastructure that lacks the capabilities to support advanced NLP algorithms and processing power.
 

Natural Language Processing in Finance Market Trends

The finance industry is significantly adopting cloud-based services for NLP applications to leverage the advantages it provides such as scalability, flexibility, and AI-powered solutions to drive insights, innovation, and competitive advantage in the financial sector. Cloud platforms provide scalability, allowing financial institutions to configure vast amounts of unstructured data from sources including news articles, social media, and financial reports. This scalability is crucial for NLP tasks such as sentiment analysis, trend detection, and risk assessment, which require processing large datasets in real-time. Cloud services incorporate AI & ML capabilities, which are essential for enhancing the accuracy and efficiency of NLP models in finance.
 

The technologies are automating the tasks such as regulatory compliance monitoring, customer sentiment analysis, and personalized financial advice, which is improving operational efficiency & customer satisfaction and building the trust factor. For instance, in February 2022, Google Cloud, KeyBank, and Deloitte extended the multi-year strategic collaboration aiming towards promoting the advance KeyBank's adoption of a cloud strategy in banking. The purpose is to revolutionize its approach to developing, implementing, and delivering digital services to clients, partners, and employees, with a strong emphasis on security throughout the transformation process.
 

There is a notable surge in demand within the finance industry for automation and efficiency, especially in leveraging NLP. This technology is increasingly sought after to streamline processes such as sentiment analysis, trend detection, and risk assessment, thereby enhancing operational efficiency and decision-making capabilities across financial institutions. NLP algorithms swiftly analyze and extract valuable insights from diverse sources including news articles, social media feeds, earnings reports, and regulatory filings.
 

This automation accelerates the speed at which financial data is processed and analyzed, thereby enabling quicker decision-making. For instance, in April 2024, Oracle Financial Services launched Oracle Financial Services Compliance Agent, a new AI-powered cloud service designed for banks. This service enables banks to conduct cost-effective hypothetical scenario testing, adjust thresholds and controls, analyze transactions, detect suspicious activities, and enhance compliance efforts more efficiently.
 

Natural Language Processing in Finance Market Analysis

Based on component, the market is segmented into software and services. The services segment represents the fastest growing segment, with a CAGR of over 20% between 2024 and 2032.
 

• Service providers are using advanced analytics capabilities adhering with NLP solutions to provide deeper insights into financial data and help the firms make the right decisions. The trend encourages expanding the use of ML and AI algorithms to enhance the accuracy and relevance of NLP-driven insights, ensuring more informed decision-making by financial institutions.
   
• There is a growing trend toward offering NLP solutions that include robust regulatory compliance features. Service providers are developing algorithms and frameworks that can interpret & adhere to complex regulatory requirements, such as GDPR and financial reporting standards. This ensures that NLP applications not only analyze data effectively but also meet stringent compliance mandates, reducing regulatory risks for financial firms.
   

Based on the industry vertical, the NLP in the finance market is segmented into banking, insurance, financial services, and others. The banking segment dominated the market in 2023 and is expected to reach over USD 20 billion by 2032.
 

• There is a trend toward developing NLP solutions that can understand & interpret the context of customer inquiries and interactions. This helps banks in providing personalized responses based on individual customer preferences, transaction history, and financial goals. For example, NLP-powered chatbots provides more meaningful conversations for customers and offer them tailored product assistance under the complex financial queries.
   
• Banks are integrating NLP capabilities across multiple channels, including websites, mobile apps, and social media platforms. This omni-channel approach ensures consistent and seamless customer experiences, allowing customers to interact with their bank using natural language queries and commands across various digital touchpoints. This trend not only enhances convenience for customers but also improves the overall satisfaction and loyalty.
   

The NLP in the finance market is experiencing significant growth in Asia Pacific and is estimated to reach USD 10 billion by 2032. The growing usage of AI-powered resources and tools in financial institutions across the Asia Pacific region is expanding the NLP in finance sectors. The resources such as chatbots make full use of NLP to interact with customers in their native languages, and provide them the personalized assistance, answering all financial related issues, and clear doubts regarding account balances, transaction histories, and even offering financial advice.
 

China's large and growing digital economy, with significant e-commerce and online banking penetration, provides a fertile ground for NLP applications. The complexity and nuances of the Chinese language require advanced NLP solutions, driving innovation and development in this field.
 

In April 2024, ExtractAlpha, a provider of alternative data and analytics solutions, unveiled its latest innovation, the Japan New Signal which is designed specifically for the Japanese stock market. The Japan News Signal combines machine learning techniques, including a sentiment model constructed from Japanese BERT, a machine learning tool that uses embedded text vectors to predict long-term results.
 

The South Korean government is actively promoting fintech and AI through various programs and subsidies. There is a high demand for digital and personalized financial services among tech-savvy consumers. Financial institutions are competing to provide superior customer service and operational efficiency through advanced technologies like NLP.
 

During the fin-tech festival SFF2023 conducted in Singapore, important discussions highlighted the intersection of policy, finance, and technology. As many financial firms explore AI applications, the Monetary Authority of Singapore (MAS) emerge for its proactive implementation efforts. MAS encouraged the AI's role in supervising financial institutions, emphasizing the development in data analytics, including AI & ML, which is increasing its ability to interpret large sets of data and identify risk signals effectively.
 

Financial firms in North America are leveraging NLP to get deeper knowledge of the customer preferences, behaviors, and create a portfolio of sentiment analysis. By analyzing unstructured data from customer interactions, including emails, call transcripts, and social media posts, banks help to personalize their services and offers more impactfully. This trend aims to improve customer satisfaction, loyalty, and retention by delivering tailored financial solutions and proactive support.
 

Natural Language Processing in Finance Market Share

Google LLC & Microsoft Corporation held over 15% share of the NLP in finance industry in 2023. Google LLC is known for its great capabilities in AI and ML domain. Google Cloud's AI and ML services, leverages NLP platform in understanding users’ sentiment analysis, which enables the financial firms to extract actionable insights from unstructured data sources such as customer communications, market news, and regulatory filings.
 

Microsoft Corporation plays a vital role in the NLP as it offers Microsoft Azure, a suite of helpful services that include NLP capabilities such as text analytics, language understanding, and sentiment analysis. The Microsoft Bot structure facilitates the development and deployment of AI-powered chatbots & virtual assistants. In the finance sector, these chatbots take the help of NLP to understand & quickly respond to customer inquiries, provide account information, offer personalized financial advice, and assist with transactional activities.
 

Natural Language Processing in Finance Companies Share

Major players operating in the NLP in finance industry are:

• Google LLC
• Microsoft Corporation
• IBM Corporation
• Amazon Web Services, Inc.
• SAS Institute Inc.
• Uniphore Technologies Inc.
• Veritone, Inc.
   

Natural Language Processing in Finance Industry News

• In February 2023, Oracle introduced Oracle Banking Cloud Services, a fresh suite of modular, adaptable cloud-native services. This launch includes six new services designed to offer banks scalable solutions for corporate demand deposit account processing, enterprise-wide limits & collateral management, real-time ISO20022 global payment processing, API management, retail onboarding & originations, and enhanced self-service digital experiences. Utilizing a microservices architecture, these offerings enable banks to renovate and modernize their business capabilities swiftly and securely.
   
• In November 2021, IBM announced upcoming enhancements to IBM Watson Discovery, aimed at NLP capabilities. The updates aim to improve customer care and streamline business operations by extracting insights and synthesizing information from intricate documents.
   

The natural language processing in finance market research report includes in-depth coverage of the industry with estimates & forecast in terms of revenue (USD million) from 2021 to 2032, for the following segments:

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Market, By Component

• 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
  • Logistics 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

Market, 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
• Others

Market, 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
• Others

Market, By Industry 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 & auditing
  • Personal finance management
  • Robo-advisory
  • Cryptocurrencies and blockchain
  • Stock movement prediction
• Others

The above information is provided for the following regions and countries:

• North America
  • U.S.
  • Canada
• Europe
  • Germany
  • UK
  • France
  • Italy
  • Spain
  • Rest of Europe
• Asia Pacific
  • China
  • Japan
  • India
  • South Korea
  • ANZ
  • Rest of Asia Pacific 
• Latin America
  • Brazil
  • Mexico
  • Rest of Latin America
• MEA
  • UAE
  • Saudi Arabia
  • South Africa
  • Rest of MEA