NLP in Finance Market

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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

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Report Code: TC 8620 Apr, 2023, by marketsandmarkets.com

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[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.

NLP in Finance Market

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NLP in Finance Market

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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

NLP in Finance Market

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 Market Size, and Share

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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Scope of the Report

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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TABLE OF CONTENTS

1 INTRODUCTION (Page No. - 46)

1.1 STUDY OBJECTIVES

1.2 MARKET DEFINITION

1.2.1 INCLUSIONS AND EXCLUSIONS

1.3 MARKET SCOPE

1.3.1 MARKET SEGMENTATION

1.3.2 REGIONS COVERED

1.3.3 YEARS CONSIDERED

1.4 CURRENCY CONSIDERED

TABLE 1 US DOLLAR EXCHANGE RATE, 2019–2022

1.5 STAKEHOLDERS

2 RESEARCH METHODOLOGY (Page No. - 51)

2.1 RESEARCH DATA

FIGURE 1 NLP IN FINANCE MARKET: RESEARCH DESIGN

2.1.1 SECONDARY DATA

2.1.2 PRIMARY DATA

2.1.2.1 Primary interviews

2.1.2.2 Breakup of primary profiles

2.1.2.3 Key industry insights

2.2 DATA TRIANGULATION

FIGURE 2 DATA TRIANGULATION

2.3 MARKET SIZE ESTIMATION

FIGURE 3 MARKET: TOP-DOWN AND BOTTOM-UP APPROACHES

2.3.1 TOP-DOWN APPROACH

2.3.2 BOTTOM-UP APPROACH

FIGURE 4 MARKET SIZE ESTIMATION METHODOLOGY - APPROACH 1 (SUPPLY-SIDE): REVENUE FROM SOLUTIONS/SERVICES OF NLP IN FINANCE MARKET

FIGURE 5 MARKET SIZE ESTIMATION METHODOLOGY - APPROACH 2, BOTTOM-UP (SUPPLY-SIDE): COLLECTIVE REVENUE FROM ALL SOLUTIONS/SERVICES OF MARKET

FIGURE 6 MARKET SIZE ESTIMATION METHODOLOGY - APPROACH 3, BOTTOM-UP (SUPPLY-SIDE): COLLECTIVE REVENUE FROM ALL SOLUTIONS/SERVICES OF MARKET

FIGURE 7 MARKET SIZE ESTIMATION METHODOLOGY - APPROACH 4, BOTTOM-UP (DEMAND-SIDE): SHARE OF NLP IN FINANCE THROUGH OVERALL SPENDING

2.4 MARKET FORECAST

TABLE 2 FACTOR ANALYSIS

2.5 RESEARCH ASSUMPTIONS

2.6 STUDY LIMITATIONS

2.7 IMPLICATIONS OF RECESSION IMPACT ON NLP IN FINANCE

3 EXECUTIVE SUMMARY (Page No. - 64)

TABLE 3 NLP IN FINANCE MARKET SIZE AND GROWTH RATE, 2019–2022 (USD MILLION, Y-O-Y %)

TABLE 4 GLOBAL MARKET SIZE AND GROWTH RATE, 2023–2028 (USD MILLION, Y-O-Y %)

FIGURE 8 SOFTWARE SEGMENT TO HOLD LARGEST MARKET SIZE IN 2023

FIGURE 9 STATISTICAL NLP SOFTWARE TO ACCOUNT FOR MAJOR MARKET SHARE IN 2023

FIGURE 10 PROFESSIONAL SERVICES TO DOMINATE MARKET IN 2023

FIGURE 11 SYSTEM INTEGRATION AND IMPLEMENTATION SERVICES TO DOMINATE MARKET IN 2023

FIGURE 12 RISK MANAGEMENT AND FRAUD DETECTION TO BE LEADING APPLICATION IN 2023

FIGURE 13 MACHINE LEARNING TO BE MOST DEPLOYED TECHNOLOGY IN 2023

FIGURE 14 INSURANCE VERTICAL SET TO WITNESS FASTEST GROWTH RATE

FIGURE 15 NORTH AMERICA TO HOLD LARGEST MARKET SHARE

4 PREMIUM INSIGHTS (Page No. - 70)

4.1 ATTRACTIVE OPPORTUNITIES IN NLP IN FINANCE MARKET

FIGURE 16 INCREASING POPULARITY OF CHATBOTS ACROSS FINANCE AND IMPROVING PERFORMANCE OF NLP MODELS TO DRIVE MARKET GROWTH

4.2 MARKET: TOP THREE APPLICATIONS

FIGURE 17 CUSTOMER SERVICE AND SUPPORT APPLICATION SEGMENT TO ACCOUNT FOR HIGHEST GROWTH RATE

4.3 NORTH AMERICA: MARKET, BY OFFERING AND VERTICAL

FIGURE 18 SOFTWARE AND BANKING TO BE LARGEST SHAREHOLDERS IN NORTH AMERICA IN 2023

4.4 MARKET, BY REGION

FIGURE 19 NORTH AMERICA TO HOLD LARGEST MARKET SHARE IN 2023

5 MARKET OVERVIEW AND INDUSTRY TRENDS (Page No. - 73)

5.1 INTRODUCTION

5.2 MARKET DYNAMICS

FIGURE 20 NLP IN FINANCE MARKET: DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES

5.2.1 DRIVERS

5.2.1.1 Increasing demand for automated and efficient financial services worldwide

5.2.1.2 Rising need for accurate and real-time analysis of complex financial data

5.2.1.3 Emergence of AI and ML models

5.2.2 RESTRAINTS

5.2.2.1 Lack of standardization in NLP-based financial applications and services

5.2.2.2 Difficulty in managing large volumes of unstructured data

5.2.2.3 Complexity in developing and training sophisticated NLP models

5.2.3 OPPORTUNITIES

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

5.2.3.2 Integration of NLP with blockchain and big data to enhance accuracy and efficiency of financial operations

5.2.3.3 Growing adoption of NLP-powered chatbots and virtual assistants

5.2.4 CHALLENGES

5.2.4.1 High implementation costs associated with NLP

5.2.4.2 Limited availability of skilled professionals

5.2.4.3 Data privacy concerns associated with use of NLP

5.3 ETHICS AND IMPLICATIONS OF NLP IN FINANCE

5.3.1 BIAS AND FAIRNESS

5.3.2 PRIVACY AND SECURITY

5.3.3 INTELLECTUAL PROPERTY

5.3.4 ACCOUNTABILITY AND RESPONSIBILITY

5.3.5 SOCIETAL AND ECONOMIC IMPACT

5.4 BRIEF HISTORY OF NLP IN FINANCE

FIGURE 21 BRIEF HISTORY OF NLP IN FINANCE

5.5 ECOSYSTEM ANALYSIS

FIGURE 22 KEY PLAYERS IN NLP IN FINANCE MARKET ECOSYSTEM

5.5.1 NLP IN FINANCE TECHNOLOGY PROVIDERS

5.5.2 NLP IN FINANCE CLOUD PLATFORM PROVIDERS

5.5.3 NLP IN FINANCE API AND AS-A-SERVICE PROVIDERS

5.5.4 NLP IN FINANCE HARDWARE PROVIDERS

5.5.5 NLP IN FINANCE END USERS

5.5.6 NLP IN FINANCE REGULATORS

5.6 NLP IN FINANCE TOOLS AND FRAMEWORK

5.6.1 TENSORFLOW

5.6.2 PYTORCH

5.6.3 KERAS

5.6.4 NLTK

5.6.5 APACHE OPENNLP

5.6.6 SPACY

5.6.7 GENSIM

5.6.8 ALLENNLP

5.6.9 FLAIR

5.6.10 STANFORD CORENLP

5.7 CASE STUDY ANALYSIS

5.7.1 CASE STUDY 1: NATWEST IMPROVED SPEED AND ACCURACY OF COMPLAINT-HANDLING PROCESS THROUGH IBM

5.7.2 CASE STUDY 2: AYASDI’S NLP PLATFORM HELPED J.P. MORGAN CHASE RAMP UP RISK ASSESSMENT TECHNIQUES

5.7.3 CASE STUDY 3: CAPITAL ONE ELIMINATED INEFFICIENCIES IN CUSTOMER QUERY RESOLUTION THROUGH NLP

5.7.4 CASE STUDY 4: BLACKROCK IDENTIFIED NEW INVESTMENT AVENUES BY ANALYZING LARGE VOLUMES OF UNSTRUCTURED DATA

5.7.5 CASE STUDY 5: YSEOP ASSISTED TD AMERITRADE IN DISCOVERING NEW CUSTOMER INSIGHTS

5.7.6 CASE STUDY 6: ALLIANZ WITNESSED SUBSTANTIAL IMPROVEMENT IN INSURANCE CLAIMS PROCESSING THROUGH NLP

5.7.7 CASE STUDY 7: UBS TRAINED DATASETS THROUGH NLP TO AUGMENT RISK MANAGEMENT PROCESSES

5.7.8 CASE STUDY 8: CITI ADDED PERSONALIZED TOUCH TO CUSTOMER RECOMMENDATIONS VIA NLP-BASED QUERY ANALYSIS

5.7.9 CASE STUDY 9: BARCLAYS SCALED ITS TRADING AND INVESTMENT ANALYSIS PROCESSES VIA AYASDI’S NLP TOOL

5.7.10 CASE STUDY 10: GOLDMAN SACHS AUGMENTED ITS FINANCIAL R&D PROWESS

5.7.11 CASE STUDY 11: NLP EMPOWERED KABBAGE WITH SMARTER DECISION-MAKING FOR LOAN DISBURSAL

5.7.12 CASE STUDY 12: CHAINALYSIS DEPLOYED NLP FOR FRAUD PREVENTION IN CRYPTO TRADING

5.8 SUPPLY CHAIN ANALYSIS

FIGURE 23 NLP IN FINANCE MARKET: SUPPLY CHAIN ANALYSIS

TABLE 5 MARKET: SUPPLY CHAIN ANALYSIS

5.9 REGULATORY LANDSCAPE

5.9.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS

TABLE 6 NORTH AMERICA: LIST OF REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS

TABLE 7 EUROPE: LIST OF REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS

TABLE 8 ASIA PACIFIC: LIST OF REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS

TABLE 9 MIDDLE EAST & AFRICA: LIST OF REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS

TABLE 10 LATIN AMERICA: LIST OF REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS

5.9.2 NORTH AMERICA

5.9.2.1 Fair Credit Reporting Act (FCRA)

5.9.2.2 Consumer Financial Protection Act (CFPA)

5.9.2.3 Gramm-Leach-Bliley Act (GLBA)

5.9.2.4 Sarbanes-Oxley Act (SOX)

5.9.2.5 Dodd-Frank Wall Street Reform and Consumer Protection Act

5.9.3 EUROPE

5.9.3.1 Markets in Financial Instruments Directive II (MiFID II)

5.9.3.2 General Data Protection Regulation (GDPR)

5.9.3.3 Payment Services Directive 2 (PSD2)

5.9.3.4 Markets in Financial Instruments Regulation (MiFIR)

5.9.3.5 Anti-Money Laundering (AML) Directive

5.9.4 ASIA PACIFIC

5.9.4.1 Personal Information Protection Act (PIPA) – Japan

5.9.4.2 Personal Data Protection Act (PDPA) – Singapore

5.9.4.3 Information Technology Act (ITA) – India

5.9.4.4 Personal Information Protection Law (PIPL) – China

5.9.4.5 Privacy Act – Australia

5.9.5 LATIN AMERICA

5.9.5.1 General Data Protection Law (LGPD) – Brazil

5.9.5.2 Data Protection Law (Ley de Proteccion de Datos Personales) – Mexico

5.9.5.3 Financial Institutions Law (Ley de Instituciones de Credito) – Mexico

5.9.5.4 Anti-Money Laundering (AML) Law – Colombia

5.9.5.5 Financial Sector Law (Ley del Sector Financiero) – Colombia

5.9.6 MIDDLE EAST AND AFRICA

5.9.6.1 Dubai Financial Services Authority (DFSA) Regulations

5.9.6.2 Financial Sector Regulation (FSR) – South Africa

5.9.6.3 Anti-Money Laundering and Countering Financing of Terrorism (AML/CFT) Regulations – Saudi Arabia

5.9.6.4 Data Protection and Privacy Regulations – Egypt

5.9.6.5 Financial Services Authority (FSA) Regulations – Morocco

5.10 PATENT ANALYSIS

5.10.1 METHODOLOGY

5.10.2 PATENTS FILED, BY DOCUMENT TYPE, 2019–2022

TABLE 11 PATENTS FILED, 2019–2022

5.10.3 INNOVATION AND PATENT APPLICATIONS

FIGURE 24 TOTAL NUMBER OF PATENTS GRANTED, 2013–2022

5.10.4 TOP APPLICANTS

FIGURE 25 TOP 10 COMPANIES WITH HIGHEST NUMBER OF PATENT APPLICATIONS IN LAST 10 YEARS, 2013–2022

TABLE 12 TOP 20 PATENT OWNERS IN NLP IN FINANCE MARKET, 2013–2022

TABLE 13 LIST OF PATENTS IN MARKET, 2021–2023

FIGURE 26 REGIONAL ANALYSIS OF PATENTS GRANTED FOR MARKET, 2013-2022

5.11 KEY CONFERENCES AND EVENTS, 2023–2024

TABLE 14 MARKET: DETAILED LIST OF CONFERENCES AND EVENTS

5.12 PRICING ANALYSIS

FIGURE 27 INDICATIVE SELLING PRICES OF KEY PLAYERS FOR TOP 3 APPLICATIONS

TABLE 15 AVERAGE SELLING PRICING ANALYSIS OF KEY PLAYERS FOR TOP 3 APPLICATIONS (USD)

5.13 PORTER’S FIVE FORCES ANALYSIS

TABLE 16 IMPACT OF EACH FORCE ON MARKET

FIGURE 28 NLP IN FINANCE MARKET: PORTER’S FIVE FORCES ANALYSIS

5.13.1 THREAT OF NEW ENTRANTS

5.13.2 THREAT OF SUBSTITUTES

5.13.3 BARGAINING POWER OF SUPPLIERS

5.13.4 BARGAINING POWER OF BUYERS

5.13.5 INTENSITY OF COMPETITIVE RIVALRY

5.14 KEY STAKEHOLDERS AND BUYING CRITERIA

5.14.1 KEY STAKEHOLDERS IN BUYING PROCESS

FIGURE 29 INFLUENCE OF STAKEHOLDERS ON BUYING PROCESS FOR TOP THREE APPLICATIONS

TABLE 17 INFLUENCE OF STAKEHOLDERS ON BUYING PROCESS FOR TOP THREE APPLICATIONS

5.14.2 BUYING CRITERIA

FIGURE 30 KEY BUYING CRITERIA FOR TOP THREE APPLICATIONS

TABLE 18 KEY BUYING CRITERIA FOR TOP THREE APPLICATIONS

5.15 TRENDS/DISRUPTIONS IMPACTING BUYERS/CLIENTS OF NLP IN FINANCE MARKET

FIGURE 31 MARKET: TRENDS/DISRUPTIONS IMPACTING BUYERS/CLIENTS

5.16 BEST PRACTICES IN MARKET

5.16.1 DOMAIN-SPECIFIC DATA SELECTION AND DATA CLEANING

5.16.2 FEATURE ENGINEERING

5.16.3 MODEL SELECTION

5.16.4 EVALUATION METRICS

5.16.5 CROSS-VALIDATION

5.16.6 REGULARIZATION

5.16.7 HYPERPARAMETER TUNING

5.16.8 TRANSFER LEARNING

5.16.9 INTERPRETABILITY

5.16.10 REGULATORY COMPLIANCE

5.16.11 BACKTESTING AND DEPLOYMENT

5.17 TECHNOLOGY ROADMAP OF NLP IN FINANCE

5.17.1 NLP IN FINANCE ROADMAP TILL 2030

TABLE 19 NLP IN FINANCE ROADMAP TILL 2030

5.17.1.1 Pre-2020

5.17.1.2 2020-2022

5.17.1.3 Short-term (2023-2025)

5.17.1.4 Mid-term (2026-2028)

5.17.1.5 Long-term (2029-2030)

5.18 CURRENT AND EMERGING BUSINESS MODELS

5.18.1 SAAS MODEL

5.18.2 CONSULTING SERVICES MODEL

5.18.3 PARTNER PROGRAMS (REVENUE SHARING MODEL)

5.18.4 PAY-PER-USE MODEL

5.19 NLP IN FINANCE’S IMPACT ON ADJACENT NICHE TECHNOLOGIES

5.19.1 HIGH-FREQUENCY TRADING AND ELECTRONIC TRADING PLATFORMS

5.19.2 FINANCIAL CYBERSECURITY

5.19.3 REGULATORY TECHNOLOGY (REGTECH)

6 NLP IN FINANCE MARKET, BY OFFERING (Page No. - 128)

6.1 INTRODUCTION

6.1.1 OFFERING: MARKET DRIVERS

FIGURE 32 SERVICES SEGMENT TO REGISTER HIGHER CAGR DURING FORECAST PERIOD

TABLE 20 MARKET, BY OFFERING, 2019–2022 (USD MILLION)

TABLE 21 MARKET, BY OFFERING, 2023–2028 (USD MILLION)

6.2 SOFTWARE

TABLE 22 SOFTWARE: MARKET, BY REGION, 2019–2022 (USD MILLION)

TABLE 23 SOFTWARE: MARKET, BY REGION, 2023–2028 (USD MILLION)

6.2.1 NLP IN FINANCE SOFTWARE, BY SOFTWARE TYPE

FIGURE 33 STATISTICAL NLP SOFTWARE TO HOLD LARGEST MARKET SHARE IN 2023

TABLE 24 SOFTWARE: MARKET, BY SOFTWARE TYPE, 2019–2022 (USD MILLION)

TABLE 25 SOFTWARE: MARKET, BY SOFTWARE TYPE, 2023–2028 (USD MILLION)

6.2.1.1 Rule-based NLP Software

6.2.1.1.1 Rule-based NLP software to help financial institutions automate compliance and risk management processes

TABLE 26 RULE-BASED NLP SOFTWARE: MARKET, BY REGION, 2019–2022 (USD MILLION)

TABLE 27 RULE-BASED NLP SOFTWARE: MARKET, BY REGION, 2023–2028 (USD MILLION)

6.2.1.1.1.1 Regular Expression (Regex)

6.2.1.1.1.2 Finite State Machines (FSMs)

6.2.1.1.1.3 Named Entity Recognition (NER)

6.2.1.1.1.4 Part-of-Speech (POS) Tagging

6.2.1.2 Statistical NLP Software

6.2.1.2.1 Statistical NLP software to analyze large volumes of unstructured data

TABLE 28 STATISTICAL NLP SOFTWARE: NLP IN FINANCE MARKET, BY REGION, 2019–2022 (USD MILLION)

TABLE 29 STATISTICAL NLP SOFTWARE: MARKET, BY REGION, 2023–2028 (USD MILLION)

6.2.1.2.1.1 Naive Bayes

6.2.1.2.1.2 Logistic Regression

6.2.1.2.1.3 Support Vector Machines (SVMs)

6.2.1.2.1.4 Recurrent Neural Networks (RNNs)

6.2.1.3 Hybrid NLP Software

6.2.1.3.1 Hybrid NLP to combine strengths of rule-based and statistical approaches

TABLE 30 HYBRID NLP SOFTWARE: MARKET, BY REGION, 2019–2022 (USD MILLION)

TABLE 31 HYBRID NLP SOFTWARE: MARKET, BY REGION, 2023–2028 (USD MILLION)

6.2.1.3.1.1 Latent Dirichlet Allocation (LDA)

6.2.1.3.1.2 Hidden Markov Models (HMMs)

6.2.1.3.1.3 Conditional Random Fields (CRFs)

6.3 SERVICES

FIGURE 34 MANAGED SERVICES SEGMENT TO REGISTER HIGHER CAGR IN MARKET FOR SERVICES DURING FORECAST PERIOD

TABLE 32 NLP IN FINANCE MARKET, BY SERVICE, 2019–2022 (USD MILLION)

TABLE 33 MARKET, BY SERVICE, 2023–2028 (USD MILLION)

TABLE 34 SERVICES: MARKET, BY REGION, 2019–2022 (USD MILLION)

TABLE 35 SERVICES: MARKET, BY REGION, 2023–2028 (USD MILLION)

6.3.1 PROFESSIONAL SERVICES

6.3.1.1 Professional services to offer specialized expertise in NLP in finance

FIGURE 35 TRAINING AND CONSULTING SERVICES SUB-SEGMENT TO REGISTER HIGHEST CAGR DURING FORECAST PERIOD

TABLE 36 SERVICES: NLP IN FINANCE MARKET, BY PROFESSIONAL SERVICE, 2019–2022 (USD MILLION)

TABLE 37 SERVICES: MARKET, BY PROFESSIONAL SERVICE, 2023–2028 (USD MILLION)

TABLE 38 PROFESSIONAL SERVICES: MARKET, BY REGION, 2019–2022 (USD MILLION)

TABLE 39 PROFESSIONAL SERVICES: MARKET, BY REGION, 2023–2028 (USD MILLION)

6.3.1.1.1 Training and consulting services

TABLE 40 TRAINING AND CONSULTING SERVICES: MARKET, BY REGION, 2019–2022 (USD MILLION)

TABLE 41 TRAINING AND CONSULTING SERVICES: MARKET, BY REGION, 2023–2028 (USD MILLION)

6.3.1.1.2 System integration and implementation services

TABLE 42 SYSTEM INTEGRATION AND IMPLEMENTATION SERVICES: MARKET, BY REGION, 2019–2022 (USD MILLION)

TABLE 43 SYSTEM INTEGRATION AND IMPLEMENTATION SERVICES: MARKET, BY REGION, 2023–2028 (USD MILLION)

6.3.1.1.3 Support and maintenance services

TABLE 44 SUPPORT AND MAINTENANCE SERVICES: MARKET, BY REGION, 2019–2022 (USD MILLION)

TABLE 45 SUPPORT AND MAINTENANCE SERVICES: MARKET, BY REGION, 2023–2028 (USD MILLION)

6.3.2 MANAGED SERVICES

6.3.2.1 Managed services to provide end-to-end management to help businesses focus on core competencies

TABLE 46 MANAGED SERVICES: MARKET, BY REGION, 2019–2022 (USD MILLION)

TABLE 47 MANAGED SERVICES: MARKET, BY REGION, 2023–2028 (USD MILLION)

7 NLP IN FINANCE MARKET, BY APPLICATION (Page No. - 148)

7.1 INTRODUCTION

7.1.1 APPLICATION: MARKET DRIVERS

FIGURE 36 NATURAL LANGUAGE GENERATION SEGMENT TO ACCOUNT FOR LARGEST MARKET SHARE IN 2023

TABLE 48 MARKET, BY APPLICATION, 2019–2022 (USD MILLION)

TABLE 49 MARKET, BY APPLICATION, 2023–2028 (USD MILLION)

7.2 SENTIMENT ANALYSIS

7.2.1 SENTIMENT ANALYSIS TO IDENTIFY AND MITIGATE POTENTIAL FINANCIAL RISKS

TABLE 50 SENTIMENT ANALYSIS: MARKET, BY REGION, 2019–2022 (USD MILLION)

TABLE 51 SENTIMENT ANALYSIS: MARKET, BY REGION, 2023–2028 (USD MILLION)

7.2.1.1 Brand reputation management

7.2.1.2 Market sentiment analysis

7.2.1.3 Customer feedback analysis

7.2.1.4 Product review analysis

7.2.1.5 Social media monitoring

7.3 RISK MANAGEMENT AND FRAUD DETECTION

7.3.1 NLP TO IMPROVE SPEED AND ACCURACY OF RISK IDENTIFICATION AND FRAUD DETECTION

TABLE 52 RISK MANAGEMENT AND FRAUD DETECTION: MARKET, BY REGION, 2019–2022 (USD MILLION)

TABLE 53 RISK MANAGEMENT AND FRAUD DETECTION: MARKET, BY REGION, 2023–2028 (USD MILLION)

7.3.1.1 Credit risk assessment

7.3.1.2 Fraud Detection and Prevention

7.3.1.3 Anti-money laundering (AML)

7.3.1.4 Compliance monitoring

7.3.1.5 Cybersecurity threat detection

7.4 COMPLIANCE MONITORING

7.4.1 NLP TO ANALYZE FINANCIAL TRANSACTIONS AND IDENTIFY POTENTIAL NON-COMPLIANCE ISSUES

TABLE 54 COMPLIANCE MONITORING: MARKET, BY REGION, 2019–2022 (USD MILLION)

TABLE 55 COMPLIANCE MONITORING: MARKET, BY REGION, 2023–2028 (USD MILLION)

7.4.1.1 Regulatory compliance monitoring

7.4.1.2 KYC/AML compliance monitoring

7.4.1.3 Legal and policy compliance monitoring

7.4.1.4 Audit trail monitoring

7.4.1.5 Trade surveillance

7.5 INVESTMENT ANALYSIS

7.5.1 FINANCIAL INSTITUTIONS INVESTING IN NLP TECHNOLOGY TO HAVE COMPETITIVE EDGE

TABLE 56 INVESTMENT ANALYSIS: MARKET, BY REGION, 2019–2022 (USD MILLION)

TABLE 57 INVESTMENT ANALYSIS: MARKET, BY REGION, 2023–2028 (USD MILLION)

7.5.1.1 Asset allocation and portfolio optimization

7.5.1.2 Equity research and analysis

7.5.1.3 Quantitative analysis and modeling

7.5.1.4 Investment recommendations and planning

7.5.1.5 Risk management and prediction

7.5.1.6 Investment opportunity identification

7.6 FINANCIAL NEWS AND MARKET ANALYSIS

7.6.1 NLP ALGORITHMS TO PREDICT HOW MARKETS REACT AND HELP INVESTORS MAKE INFORMED INVESTMENT DECISIONS

TABLE 58 FINANCIAL NEWS AND MARKET ANALYSIS: MARKET, BY REGION, 2019–2022 (USD MILLION)

TABLE 59 FINANCIAL NEWS AND MARKET ANALYSIS: MARKET, BY REGION, 2023–2028 (USD MILLION)

7.6.1.1 Financial news analysis

7.6.1.2 Stock market prediction

7.6.1.3 Macroeconomic analysis

7.7 CUSTOMER SERVICE AND SUPPORT

7.7.1 ADOPTION OF INTELLIGENT CHATBOTS AND CUSTOMER SUPPORT SYSTEMS TO DRIVE GROWTH

TABLE 60 CUSTOMER SERVICE AND SUPPORT: MARKET, BY REGION, 2019–2022 (USD MILLION)

TABLE 61 CUSTOMER SERVICE AND SUPPORT: MARKET, BY REGION, 2023–2028 (USD MILLION)

7.7.1.1 Chatbots and virtual assistants

7.7.1.2 Personalized support and service

7.7.1.3 Compliant resolution

7.7.1.4 Query resolution and escalation management

7.7.1.5 Self-service options

7.7.1.6 Multilingual customer service and support

7.8 DOCUMENT AND CONTRACT ANALYSIS

7.8.1 DOCUMENT AND CONTRACT ANALYSIS TO STREAMLINE DATA PROCESSING WORKFLOWS

TABLE 62 DOCUMENT AND CONTRACT ANALYSIS: MARKET, BY REGION, 2019–2022 (USD MILLION)

TABLE 63 DOCUMENT AND CONTRACT ANALYSIS: MARKET, BY REGION, 2023–2028 (USD MILLION)

7.8.1.1 Contract management

7.8.1.2 Legal document analysis

7.8.1.3 Due diligence analysis

7.8.1.4 Data extraction and normalization

7.9 SPEECH RECOGNITION AND TRANSCRIPTION

7.9.1 POWERFUL TOOL TO CAPTURE AND ANALYZE VOICE DATA AND ENSURE COMPLIANCE

TABLE 64 SPEECH RECOGNITION AND TRANSCRIPTION: MARKET, BY REGION, 2019–2022 (USD MILLION)

TABLE 65 SPEECH RECOGNITION AND TRANSCRIPTION: MARKET, BY REGION, 2023–2028 (USD MILLION)

7.9.1.1 Voice-enabled search and navigation

7.9.1.2 Speech-to-text conversion

7.9.1.3 Call transcription and analysis

7.9.1.4 Voice biometrics and authentication

7.9.1.5 Speech-enabled virtual assistants

7.10 LANGUAGE TRANSLATION

7.10.1 AUTOMATING REPORT WRITING AND PERSONALIZED FINANCIAL ADVICE TO DRIVE UPTAKE OF LANGUAGE TRANSLATION TOOLS

TABLE 66 LANGUAGE TRANSLATION: NLP IN FINANCE MARKET, BY REGION, 2019–2022 (USD MILLION)

TABLE 67 LANGUAGE TRANSLATION: MARKET, BY REGION, 2023–2028 (USD MILLION)

7.10.1.1 Financial document translation

7.10.1.2 Investment research translation

7.10.1.3 Cross-border business communication

7.10.1.4 Localization and internationalization

7.11 OTHER APPLICATIONS

TABLE 68 OTHER APPLICATIONS: MARKET, BY REGION, 2019–2022 (USD MILLION)

TABLE 69 OTHER APPLICATIONS: MARKET, BY REGION, 2023–2028 (USD MILLION)

8 NLP IN FINANCE MARKET, BY TECHNOLOGY (Page No. - 178)

8.1 INTRODUCTION

8.1.1 TECHNOLOGY: MARKET DRIVERS

FIGURE 37 DEEP LEARNING SEGMENT TO GROW AT HIGHER CAGR

TABLE 70 MARKET, BY TECHNOLOGY, 2019–2022 (USD MILLION)

TABLE 71 MARKET, BY TECHNOLOGY, 2023–2028 (USD MILLION)

8.2 MACHINE LEARNING

8.2.1 MACHINE LEARNING TO BE EXTENSIVELY DEPLOYED TO PREDICT FINANCIAL MARKET INSIGHTS

TABLE 72 MACHINE LEARNING: MARKET, BY REGION, 2019–2022 (USD MILLION)

TABLE 73 MACHINE LEARNING: MARKET, BY REGION, 2023–2028 (USD MILLION)

8.2.1.1 Supervised learning

8.2.1.2 Unsupervised learning

8.2.1.3 Reinforcement learning

8.3 DEEP LEARNING

8.3.1 DEEP LEARNING TO PLAY CRITICAL ROLE IN ADVANCING NLP DEVELOPMENTS

TABLE 74 DEEP LEARNING: NLP IN FINANCE MARKET, BY REGION, 2019–2022 (USD MILLION)

TABLE 75 DEEP LEARNING: MARKET, BY REGION, 2023–2028 (USD MILLION)

8.3.1.1 Convolutional neural networks (CNN)

8.3.1.2 Recurrent neural networks (RNN)

8.3.1.3 Transformer models (BERT, GPT-3, etc.)

8.4 NATURAL LANGUAGE GENERATION

8.4.1 FINANCIAL INSTITUTIONS TO INCREASINGLY ADOPT NLG TO IMPROVE EFFICIENCY AND REDUCE COSTS

TABLE 76 NATURAL LANGUAGE GENERATION: MARKET, BY REGION, 2019–2022 (USD MILLION)

TABLE 77 NATURAL LANGUAGE GENERATION: MARKET, BY REGION, 2023–2028 (USD MILLION)

8.4.1.1 Automated report writing

8.4.1.2 Customer communication

8.4.1.3 Financial document generation

8.5 TEXT CLASSIFICATION

8.5.1 TEXT CLASSIFICATION TO ANALYZE MARKET SENTIMENTS IN FINANCE

TABLE 78 TEXT CLASSIFICATION: MARKET, BY REGION, 2019–2022 (USD MILLION)

TABLE 79 TEXT CLASSIFICATION: MARKET, BY REGION, 2023–2028 (USD MILLION)

8.5.1.1 Sentiment classification

8.5.1.2 Intent classification

8.6 TOPIC MODELING

8.6.1 TOPIC MODELING TO EXTRACT INSIGHTS FROM FINANCIAL NEWS ARTICLES

TABLE 80 TOPIC MODELING: NLP IN FINANCE MARKET, BY REGION, 2019–2022 (USD MILLION)

TABLE 81 TOPIC MODELING: MARKET, BY REGION, 2023–2028 (USD MILLION)

8.6.1.1 Topic identification

8.6.1.2 Topic clustering

8.6.1.3 Topic visualization

8.7 EMOTION DETECTION

8.7.1 EMOTION DETECTION TO IMPROVE SENTIMENT ANALYSIS IN FINANCIAL DISCOURSE

TABLE 82 EMOTION DETECTION: MARKET, BY REGION, 2019–2022 (USD MILLION)

TABLE 83 EMOTION DETECTION: MARKET, BY REGION, 2023–2028 (USD MILLION)

8.7.1.1 Emotion recognition

8.7.1.2 Emotion classification

8.8 OTHER TECHNOLOGIES

8.8.1 NER AND EVENT EXTRACTION TO FACE SPIKE IN HANDLING UNSTRUCTURED FINANCIAL DATA

TABLE 84 OTHER TECHNOLOGIES: MARKET, BY REGION, 2019–2022 (USD MILLION)

TABLE 85 OTHER TECHNOLOGIES: MARKET, BY REGION, 2023–2028 (USD MILLION)

9 NLP IN FINANCE MARKET, BY VERTICAL (Page No. - 194)

9.1 INTRODUCTION

9.1.1 VERTICAL: MARKET DRIVERS

FIGURE 38 INSURANCE SEGMENT TO GROW AT HIGHEST CAGR

TABLE 86 MARKET, BY VERTICAL, 2019–2022 (USD MILLION)

TABLE 87 MARKET, BY VERTICAL, 2023–2028 (USD MILLION)

9.2 BANKING

9.2.1 NLP TO IMPROVE EFFICIENCY, ACCURACY, AND CUSTOMER EXPERIENCE

9.2.2 NLP IN FINANCE: BANKING USE CASES

TABLE 88 BANKING: NLP IN FINANCE MARKET, BY REGION, 2019–2022 (USD MILLION)

TABLE 89 BANKING: MARKET, BY REGION, 2023–2028 (USD MILLION)

9.2.2.1 Retail banking

9.2.2.2 Corporate banking

9.2.2.3 Investment banking

9.2.2.4 Wealth management

9.3 INSURANCE

9.3.1 INSURANCE COMPANIES TO ANALYZE LARGE AMOUNTS OF DATA USING NLP

9.3.2 NLP IN FINANCE: INSURANCE USE CASES

TABLE 90 INSURANCE: MARKET, BY REGION, 2019–2022 (USD MILLION)

TABLE 91 INSURANCE: MARKET, BY REGION, 2023–2028 (USD MILLION)

9.3.2.1 Life insurance

9.3.2.2 Property and casualty insurance

9.3.2.3 Health insurance

9.4 FINANCIAL SERVICES

9.4.1 USE OF NLP TO GROW IN FINTECH

9.4.2 NLP IN FINANCE: FINANCIAL SERVICES USE CASES

TABLE 92 FINANCIAL SERVICES: NLP IN FINANCE MARKET, BY REGION, 2019–2022 (USD MILLION)

TABLE 93 FINANCIAL SERVICES: MARKET, BY REGION, 2023–2028 (USD MILLION)

9.4.2.1 Credit rating

9.4.2.2 Payment processing and remitting

9.4.2.3 Accounting and auditing

9.4.2.4 Personal finance management

9.4.2.5 Robo-advisory

9.4.2.6 Cryptocurrencies and blockchain

9.4.2.7 Stock movement prediction

9.4.2.8 Others

9.5 OTHER ENTERPRISE VERTICALS

9.5.1 NLP IN FINANCE TO MAKE INROADS ACROSS FINANCIAL OPERATIONS

9.5.1.1 Healthcare and life sciences

9.5.1.2 Manufacturing

9.5.1.3 Retail and eCommerce

9.5.1.4 Energy & utilities

9.5.1.5 Transportation and logistics

9.5.1.6 Others

10 NLP IN FINANCE MARKET, BY REGION (Page No. - 210)

10.1 INTRODUCTION

FIGURE 39 ASIA PACIFIC MARKET TO REGISTER HIGHEST CAGR DURING FORECAST PERIOD

FIGURE 40 INDIA TO REGISTER HIGHEST CAGR IN NLP IN FINANCE

TABLE 94 MARKET, BY REGION, 2019–2022 (USD MILLION)

TABLE 95 MARKET, BY REGION, 2023–2028 (USD MILLION)

10.2 NORTH AMERICA

10.2.1 NORTH AMERICA: MARKET DRIVERS

10.2.2 NORTH AMERICA: RECESSION IMPACT

FIGURE 41 NORTH AMERICA: SNAPSHOT OF MARKET

TABLE 96 NORTH AMERICA: NLP IN FINANCE MARKET, BY OFFERING, 2019–2022 (USD MILLION)

TABLE 97 NORTH AMERICA: MARKET, BY OFFERING, 2023–2028 (USD MILLION)

TABLE 98 NORTH AMERICA: MARKET, BY SOFTWARE, 2019–2022 (USD MILLION)

TABLE 99 NORTH AMERICA: MARKET, BY SOFTWARE, 2023–2028 (USD MILLION)

TABLE 100 NORTH AMERICA: MARKET, BY SERVICE, 2019–2022 (USD MILLION)

TABLE 101 NORTH AMERICA: MARKET, BY SERVICE, 2023–2028 (USD MILLION)

TABLE 102 NORTH AMERICA: MARKET, BY PROFESSIONAL SERVICE, 2019–2022 (USD MILLION)

TABLE 103 NORTH AMERICA: MARKET, BY PROFESSIONAL SERVICE, 2023–2028 (USD MILLION)

TABLE 104 NORTH AMERICA: MARKET, BY TECHNOLOGY, 2019–2022 (USD MILLION)

TABLE 105 NORTH AMERICA: MARKET, BY TECHNOLOGY, 2023–2028 (USD MILLION)

TABLE 106 NORTH AMERICA: MARKET, BY APPLICATION, 2019–2022 (USD MILLION)

TABLE 107 NORTH AMERICA: MARKET, BY APPLICATION, 2023–2028 (USD MILLION)

TABLE 108 NORTH AMERICA: MARKET, BY VERTICAL, 2019–2022 (USD MILLION)

TABLE 109 NORTH AMERICA: MARKET, BY VERTICAL, 2023–2028 (USD MILLION)

TABLE 110 NORTH AMERICA: MARKET, BY COUNTRY, 2019–2022 (USD MILLION)

TABLE 111 NORTH AMERICA: MARKET, BY COUNTRY, 2023–2028 (USD MILLION)

10.2.3 US

10.2.3.1 US to implement NLP for real-time data analysis

TABLE 112 US: NLP IN FINANCE MARKET, BY OFFERING, 2019–2022 (USD MILLION)

TABLE 113 US: MARKET, BY OFFERING, 2023–2028 (USD MILLION)

10.2.4 CANADA

10.2.4.1 Canadian banks to use NLP-powered chatbots to interact with customers

TABLE 114 CANADA: MARKET, BY OFFERING, 2019–2022 (USD MILLION)

TABLE 115 CANADA: MARKET, BY OFFERING, 2023–2028 (USD MILLION)

10.3 EUROPE

10.3.1 EUROPE: MARKET DRIVERS

10.3.2 EUROPE: RECESSION IMPACT

TABLE 116 EUROPE: NLP IN FINANCE MARKET, BY OFFERING, 2019–2022 (USD MILLION)

TABLE 117 EUROPE: MARKET, BY OFFERING, 2023–2028 (USD MILLION)

TABLE 118 EUROPE: MARKET, BY SOFTWARE, 2019–2022 (USD MILLION)

TABLE 119 EUROPE: MARKET, BY SOFTWARE, 2023–2028 (USD MILLION)

TABLE 120 EUROPE: MARKET, BY SERVICE, 2019–2022 (USD MILLION)

TABLE 121 EUROPE: MARKET, BY SERVICE, 2023–2028 (USD MILLION)

TABLE 122 EUROPE: MARKET, BY PROFESSIONAL SERVICE, 2019–2022 (USD MILLION)

TABLE 123 EUROPE: MARKET, BY PROFESSIONAL SERVICE, 2023–2028 (USD MILLION)

TABLE 124 EUROPE: MARKET, BY TECHNOLOGY, 2019–2022 (USD MILLION)

TABLE 125 EUROPE: MARKET, BY TECHNOLOGY, 2023–2028 (USD MILLION)

TABLE 126 EUROPE: MARKET, BY APPLICATION, 2019–2022 (USD MILLION)

TABLE 127 EUROPE: MARKET, BY APPLICATION, 2023–2028 (USD MILLION)

TABLE 128 EUROPE: MARKET, BY VERTICAL, 2019–2022 (USD MILLION)

TABLE 129 EUROPE: MARKET, BY VERTICAL, 2023–2028 (USD MILLION)

TABLE 130 EUROPE: MARKET, BY COUNTRY, 2019–2022 (USD MILLION)

TABLE 131 EUROPE: MARKET, BY COUNTRY, 2023–2028 (USD MILLION)

10.3.3 UK

10.3.3.1 UK companies to leverage NLP to improve operations and gain competitive edge

TABLE 132 UK: NLP IN FINANCE MARKET, BY OFFERING, 2019–2022 (USD MILLION)

TABLE 133 UK: MARKET, BY OFFERING, 2023–2028 (USD MILLION)

10.3.4 GERMANY

10.3.4.1 Adoption of NLP to be driven by regulatory compliance, cost reduction, and better customer experience

TABLE 134 GERMANY: MARKET, BY OFFERING, 2019–2022 (USD MILLION)

TABLE 135 GERMANY: MARKET, BY OFFERING, 2023–2028 (USD MILLION)

10.3.5 FRANCE

10.3.5.1 France to witness emergence of AI-based chatbots using NLP

TABLE 136 FRANCE: MARKET, BY OFFERING, 2019–2022 (USD MILLION)

TABLE 137 FRANCE: MARKET, BY OFFERING, 2023–2028 (USD MILLION)

10.3.6 ITALY

10.3.6.1 NLP to help financial institutions analyze large volumes of data efficiently and accurately

TABLE 138 ITALY: NLP IN FINANCE MARKET, BY OFFERING, 2019–2022 (USD MILLION)

TABLE 139 ITALY: MARKET, BY OFFERING, 2023–2028 (USD MILLION)

10.3.7 SPAIN

10.3.7.1 NLP to significantly improve customer service and reduce operating costs in banking

TABLE 140 SPAIN: MARKET, BY OFFERING, 2019–2022 (USD MILLION)

TABLE 141 SPAIN: MARKET, BY OFFERING, 2023–2028 (USD MILLION)

10.3.8 SWITZERLAND

10.3.8.1 Swiss banks and financial institutions to invest in NLP to gain competitive advantage

TABLE 142 SWITZERLAND: MARKET, BY OFFERING, 2019–2022 (USD MILLION)

TABLE 143 SWITZERLAND: MARKET, BY OFFERING, 2023–2028 (USD MILLION)

10.3.9 REST OF EUROPE

TABLE 144 REST OF EUROPE: MARKET, BY OFFERING, 2019–2022 (USD MILLION)

TABLE 145 REST OF EUROPE: MARKET, BY OFFERING, 2023–2028 (USD MILLION)

10.4 ASIA PACIFIC

10.4.1 ASIA PACIFIC: MARKET DRIVERS

10.4.2 ASIA PACIFIC: RECESSION IMPACT

FIGURE 42 ASIA PACIFIC: SNAPSHOT OF MARKET

TABLE 146 ASIA PACIFIC: NLP IN FINANCE MARKET, BY OFFERING, 2019–2022 (USD MILLION)

TABLE 147 ASIA PACIFIC: MARKET, BY OFFERING, 2023–2028 (USD MILLION)

TABLE 148 ASIA PACIFIC: MARKET, BY SOFTWARE, 2019–2022 (USD MILLION)

TABLE 149 ASIA PACIFIC: MARKET, BY SOFTWARE, 2023–2028 (USD MILLION)

TABLE 150 ASIA PACIFIC: MARKET, BY SERVICE, 2019–2022 (USD MILLION)

TABLE 151 ASIA PACIFIC: MARKET, BY SERVICE, 2023–2028 (USD MILLION)

TABLE 152 ASIA PACIFIC: MARKET, BY PROFESSIONAL SERVICE, 2019–2022 (USD MILLION)

TABLE 153 ASIA PACIFIC: MARKET, BY PROFESSIONAL SERVICE, 2023–2028 (USD MILLION)

TABLE 154 ASIA PACIFIC: MARKET, BY TECHNOLOGY, 2019–2022 (USD MILLION)

TABLE 155 ASIA PACIFIC: MARKET, BY TECHNOLOGY, 2023–2028 (USD MILLION)

TABLE 156 ASIA PACIFIC: MARKET, BY APPLICATION, 2019–2022 (USD MILLION)

TABLE 157 ASIA PACIFIC: MARKET, BY APPLICATION, 2023–2028 (USD MILLION)

TABLE 158 ASIA PACIFIC: MARKET, BY VERTICAL, 2019–2022 (USD MILLION)

TABLE 159 ASIA PACIFIC: MARKET, BY VERTICAL, 2023–2028 (USD MILLION)

TABLE 160 ASIA PACIFIC: MARKET, BY COUNTRY, 2019–2022 (USD MILLION)

TABLE 161 ASIA PACIFIC: MARKET, BY COUNTRY, 2023–2028 (USD MILLION)

10.4.3 CHINA

10.4.3.1 NLP solutions to develop as demand for digital transformation increases

TABLE 162 CHINA: MARKET, BY OFFERING, 2019–2022 (USD MILLION)

TABLE 163 CHINA: MARKET, BY OFFERING, 2023–2028 (USD MILLION)

10.4.4 INDIA

10.4.4.1 Adoption of NLP in banking to be influenced by startups and Digital India movement

TABLE 164 INDIA: MARKET, BY OFFERING, 2019–2022 (USD MILLION)

TABLE 165 INDIA: MARKET, BY OFFERING, 2023–2028 (USD MILLION)

10.4.5 JAPAN

10.4.5.1 NLP potential to be unlocked in Japan's finance markets

TABLE 166 JAPAN: NLP IN FINANCE MARKET, BY OFFERING, 2019–2022 (USD MILLION)

TABLE 167 JAPAN: MARKET, BY OFFERING, 2023–2028 (USD MILLION)

10.4.6 SOUTH KOREA

10.4.6.1 NLP to change financial sector by improving consumer experience

TABLE 168 SOUTH KOREA: MARKET, BY OFFERING, 2019–2022 (USD MILLION)

TABLE 169 SOUTH KOREA: MARKET, BY OFFERING, 2023–2028 (USD MILLION)

10.4.7 SINGAPORE

10.4.7.1 Singapore to improve its financial services and stay competitive using NLP

TABLE 170 SINGAPORE: MARKET, BY OFFERING, 2019–2022 (USD MILLION)

TABLE 171 SINGAPORE: MARKET, BY OFFERING, 2023–2028 (USD MILLION)

10.4.8 ANZ

10.4.8.1 NLP solutions to gain more prominence due to technology development

TABLE 172 ANZ: MARKET, BY OFFERING, 2019–2022 (USD MILLION)

TABLE 173 ANZ: MARKET, BY OFFERING, 2023–2028 (USD MILLION)

10.4.9 REST OF ASIA PACIFIC

TABLE 174 REST OF ASIA PACIFIC: MARKET, BY OFFERING, 2019–2022 (USD MILLION)

TABLE 175 REST OF ASIA PACIFIC: MARKET, BY OFFERING, 2023–2028 (USD MILLION)

10.5 MIDDLE EAST & AFRICA

10.5.1 MIDDLE EAST & AFRICA: MARKET DRIVERS

10.5.2 MIDDLE EAST & AFRICA: RECESSION IMPACT

TABLE 176 MIDDLE EAST & AFRICA: NLP IN FINANCE MARKET, BY OFFERING, 2019–2022 (USD MILLION)

TABLE 177 MIDDLE EAST & AFRICA: MARKET, BY OFFERING, 2023–2028 (USD MILLION)

TABLE 178 MIDDLE EAST & AFRICA: MARKET, BY SOFTWARE, 2019–2022 (USD MILLION)

TABLE 179 MIDDLE EAST & AFRICA: MARKET, BY SOFTWARE, 2023–2028 (USD MILLION)

TABLE 180 MIDDLE EAST & AFRICA: MARKET, BY SERVICE, 2019–2022 (USD MILLION)

TABLE 181 MIDDLE EAST & AFRICA: MARKET, BY SERVICE, 2023–2028 (USD MILLION)

TABLE 182 MIDDLE EAST & AFRICA: MARKET, BY PROFESSIONAL SERVICE, 2019–2022 (USD MILLION)

TABLE 183 MIDDLE EAST & AFRICA: MARKET, BY PROFESSIONAL SERVICE, 2023–2028 (USD MILLION)

TABLE 184 MIDDLE EAST & AFRICA: MARKET, BY TECHNOLOGY, 2019–2022 (USD MILLION)

TABLE 185 MIDDLE EAST & AFRICA: MARKET, BY TECHNOLOGY, 2023–2028 (USD MILLION)

TABLE 186 MIDDLE EAST & AFRICA: MARKET, BY APPLICATION, 2019–2022 (USD MILLION)

TABLE 187 MIDDLE EAST & AFRICA: MARKET, BY APPLICATION, 2023–2028 (USD MILLION)

TABLE 188 MIDDLE EAST & AFRICA: MARKET, BY VERTICAL, 2019–2022 (USD MILLION)

TABLE 189 MIDDLE EAST & AFRICA: MARKET, BY VERTICAL, 2023–2028 (USD MILLION)

TABLE 190 MIDDLE EAST & AFRICA: MARKET, BY COUNTRY, 2019–2022 (USD MILLION)

TABLE 191 MIDDLE EAST & AFRICA: MARKET, BY COUNTRY, 2023–2028 (USD MILLION)

10.5.3 SAUDI ARABIA

10.5.3.1 Saudi Arabia to embrace NLP to drive economic growth

TABLE 192 SAUDI ARABIA: NLP IN FINANCE MARKET, BY OFFERING, 2019–2022 (USD MILLION)

TABLE 193 SAUDI ARABIA: MARKET, BY OFFERING, 2023–2028 (USD MILLION)

10.5.4 UAE

10.5.4.1 Several UAE startups to leverage NLP to drive innovation

TABLE 194 UAE: MARKET, BY OFFERING, 2019–2022 (USD MILLION)

TABLE 195 UAE: MARKET, BY OFFERING, 2023–2028 (USD MILLION)

10.5.5 SOUTH AFRICA

10.5.5.1 South Africa to witness several developments in NLP

TABLE 196 SOUTH AFRICA: MARKET, BY OFFERING, 2019–2022 (USD MILLION)

TABLE 197 SOUTH AFRICA: MARKET, BY OFFERING, 2023–2028 (USD MILLION)

10.5.6 ISRAEL

10.5.6.1 Adoption of NLP to position country as leader in technological advancements

TABLE 198 ISRAEL: MARKET, BY OFFERING, 2019–2022 (USD MILLION)

TABLE 199 ISRAEL: MARKET, BY OFFERING, 2023–2028 (USD MILLION)

10.5.7 REST OF MIDDLE EAST & AFRICA

TABLE 200 REST OF MIDDLE EAST & AFRICA: MARKET, BY OFFERING, 2019–2022 (USD MILLION)

TABLE 201 REST OF MIDDLE EAST & AFRICA: MARKET, BY OFFERING, 2023–2028 (USD MILLION)

10.6 LATIN AMERICA

10.6.1 LATIN AMERICA: MARKET DRIVERS

10.6.2 LATIN AMERICA: RECESSION IMPACT

TABLE 202 LATIN AMERICA: NLP IN FINANCE MARKET, BY OFFERING, 2019–2022 (USD MILLION)

TABLE 203 LATIN AMERICA: MARKET, BY OFFERING, 2023–2028 (USD MILLION)

TABLE 204 LATIN AMERICA: MARKET, BY SOFTWARE, 2019–2022 (USD MILLION)

TABLE 205 LATIN AMERICA: MARKET, BY SOFTWARE, 2023–2028 (USD MILLION)

TABLE 206 LATIN AMERICA: MARKET, BY SERVICE, 2019–2022 (USD MILLION)

TABLE 207 LATIN AMERICA: MARKET, BY SERVICE, 2023–2028 (USD MILLION)

TABLE 208 LATIN AMERICA: MARKET, BY PROFESSIONAL SERVICE, 2019–2022 (USD MILLION)

TABLE 209 LATIN AMERICA: MARKET, BY PROFESSIONAL SERVICE, 2023–2028 (USD MILLION)

TABLE 210 LATIN AMERICA: MARKET, BY TECHNOLOGY, 2019–2022 (USD MILLION)

TABLE 211 LATIN AMERICA: MARKET, BY TECHNOLOGY, 2023–2028 (USD MILLION)

TABLE 212 LATIN AMERICA: MARKET, BY APPLICATION, 2019–2022 (USD MILLION)

TABLE 213 LATIN AMERICA: MARKET, BY APPLICATION, 2023–2028 (USD MILLION)

TABLE 214 LATIN AMERICA: MARKET, BY VERTICAL, 2019–2022 (USD MILLION)

TABLE 215 LATIN AMERICA: MARKET, BY VERTICAL, 2023–2028 (USD MILLION)

TABLE 216 LATIN AMERICA: MARKET, BY COUNTRY, 2019–2022 (USD MILLION)

TABLE 217 LATIN AMERICA: MARKET, BY COUNTRY, 2023–2028 (USD MILLION)

10.6.2.1 Brazil

10.6.2.1.1 NLP to be used in customer service

TABLE 218 BRAZIL: MARKET, BY OFFERING, 2019–2022 (USD MILLION)

TABLE 219 BRAZIL: MARKET, BY OFFERING, 2023–2028 (USD MILLION)

10.6.2.2 Mexico

10.6.2.2.1 NLP to witness wide adoption in finance

TABLE 220 MEXICO: NLP IN FINANCE MARKET, BY OFFERING, 2019–2022 (USD MILLION)

TABLE 221 MEXICO: MARKET, BY OFFERING, 2023–2028 (USD MILLION)

10.6.2.3 Argentina

10.6.2.3.1 Advancements in NLP to change ways how financial institutions interact with customers

TABLE 222 ARGENTINA: MARKET, BY OFFERING, 2019–2022 (USD MILLION)

TABLE 223 ARGENTINA: MARKET, BY OFFERING, 2023–2028 (USD MILLION)

10.6.2.4 Rest of Latin America

TABLE 224 REST OF LATIN AMERICA: MARKET, BY OFFERING, 2019–2022 (USD MILLION)

TABLE 225 REST OF LATIN AMERICA: MARKET, BY OFFERING, 2023–2028 (USD MILLION)

11 COMPETITIVE LANDSCAPE (Page No. - 268)

11.1 OVERVIEW

11.2 KEY STRATEGIES ADOPTED BY MAJOR PLAYERS

TABLE 226 OVERVIEW OF STRATEGIES ADOPTED BY KEY NLP IN FINANCE VENDORS

11.3 REVENUE ANALYSIS

11.3.1 HISTORIC REVENUE ANALYSIS

FIGURE 43 HISTORIC REVENUE ANALYSIS OF TOP FIVE PLAYERS, 2020–2022 (USD MILLION)

11.4 MARKET SHARE ANALYSIS

FIGURE 44 MARKET SHARE ANALYSIS FOR KEY COMPANIES IN 2022

TABLE 227 MARKET: DEGREE OF COMPETITION

11.5 COMPANY EVALUATION QUADRANT

11.5.1 STARS

11.5.2 EMERGING LEADERS

11.5.3 PERVASIVE PLAYERS

11.5.4 PARTICIPANTS

FIGURE 45 NLP IN FINANCE MARKET: COMPANY EVALUATION QUADRANT, 2022

11.6 COMPETITIVE BENCHMARKING

TABLE 228 MARKET: PRODUCT FOOTPRINT ANALYSIS OF KEY PLAYERS, 2022

TABLE 229 MARKET: PRODUCT FOOTPRINT ANALYSIS OF OTHER KEY PLAYERS, 2022

11.7 STARTUP/SME EVALUATION QUADRANT

11.7.1 PROGRESSIVE COMPANIES

11.7.2 RESPONSIVE COMPANIES

11.7.3 DYNAMIC COMPANIES

11.7.4 STARTING BLOCKS

FIGURE 46 STARTUPS/SMES: COMPANY EVALUATION QUADRANT, 2022

11.8 STARTUP/SME COMPETITIVE BENCHMARKING

TABLE 230 MARKET: DETAILED LIST OF KEY STARTUPS/SMES

TABLE 231 NLP IN FINANCE MARKET: PRODUCT FOOTPRINT ANALYSIS OF STARTUPS/ SMES, 2022

11.9 NLP IN FINANCE PRODUCT LANDSCAPE

11.9.1 PROMINENT NAMED SENTIMENT ANALYSIS PRODUCTS

TABLE 232 COMPARATIVE ANALYSIS OF PROMINENT NAMED SENTIMENT ANALYSIS PRODUCTS

11.9.1.1 Lexalytics

11.9.1.2 Aylien

11.9.1.3 Google Cloud

11.9.1.4 IBM Watson

11.9.1.5 Amazon Comprehend

11.9.2 PROMINENT NAMED ENTITY RECOGNITION PRODUCTS

TABLE 233 COMPARATIVE ANALYSIS OF PROMINENT NAMED ENTITY RECOGNITION PRODUCTS

11.9.2.1 Rosette

11.9.2.2 Spacy

11.9.2.3 Basis Tech

11.9.2.4 Expert.AI

11.9.2.5 MeaningCloud

11.9.3 PROMINENT TOPIC MODELING PRODUCTS

TABLE 234 COMPARATIVE ANALYSIS OF PROMINENT TOPIC MODELING PRODUCTS

11.9.3.1 Gensim

11.9.3.2 Mallet

11.9.3.3 LDAvis

11.9.3.4 bigARTM

11.9.3.5 Stanford NLP

11.9.4 PROMINENT TEXT CLASSIFICATION PRODUCTS

TABLE 235 COMPARATIVE ANALYSIS OF PROMINENT TEXT CLASSIFICATION PRODUCTS

11.9.4.1 MonkeyLearn

11.9.4.2 Datumbox

11.9.4.3 OpenAI

11.9.4.4 Hugging Face

11.9.4.5 TensorFlow

11.9.5 PROMINENT DOCUMENT CLASSIFICATION PRODUCTS

TABLE 236 COMPARATIVE ANALYSIS OF PROMINENT DOCUMENT CLASSIFICATION PRODUCTS

11.9.5.1 Azure Cognitive Services Text Analytics

11.9.5.2 OpenText Magellan

11.9.5.3 RapidMiner

11.9.5.4 Prodigy By Explosion AI

11.9.5.5 KNIME Analytics Platform

11.10 VALUATION AND FINANCIAL METRICS OF KEY NLP IN FINANCE VENDORS

FIGURE 47 VALUATION AND FINANCIAL METRICS OF KEY NLP IN FINANCE VENDORS

11.11 COMPETITIVE SCENARIO AND TRENDS

11.11.1 PRODUCT LAUNCHES AND ENHANCEMENTS

TABLE 237 SERVICE/PRODUCT LAUNCHES, 2020–2023

11.11.2 DEALS

TABLE 238 DEALS, 2021–2023

12 COMPANY PROFILES (Page No. - 298)

12.1 INTRODUCTION

(Business Overview, Software/Services offered, Recent Developments, MnM view, Key strengths, Strategic choices, Weakness and competitive threats)*

12.2 KEY PLAYERS

12.2.1 MICROSOFT

TABLE 239 MICROSOFT: BUSINESS OVERVIEW

FIGURE 48 MICROSOFT: COMPANY SNAPSHOT

TABLE 240 MICROSOFT: SOFTWARE/SERVICES OFFERED

TABLE 241 MICROSOFT: PRODUCT LAUNCHES AND ENHANCEMENTS

TABLE 242 MICROSOFT: DEALS

12.2.2 IBM

TABLE 243 IBM: BUSINESS OVERVIEW

FIGURE 49 IBM: COMPANY SNAPSHOT

TABLE 244 IBM: SOFTWARE/SERVICES OFFERED

TABLE 245 IBM: PRODUCT LAUNCHES AND ENHANCEMENTS

TABLE 246 IBM: DEALS

12.2.3 GOOGLE

TABLE 247 GOOGLE: BUSINESS OVERVIEW

FIGURE 50 GOOGLE: COMPANY SNAPSHOT

TABLE 248 GOOGLE: SOFTWARE/SERVICES OFFERED

TABLE 249 GOOGLE: PRODUCT LAUNCHES AND ENHANCEMENTS

TABLE 250 GOOGLE: DEALS

12.2.4 AWS

TABLE 251 AWS: BUSINESS OVERVIEW

FIGURE 51 AWS: COMPANY SNAPSHOT

TABLE 252 AWS: SOFTWARE/SERVICES OFFERED

TABLE 253 AWS: PRODUCT LAUNCHES AND ENHANCEMENTS

TABLE 254 AWS: DEALS

12.2.5 ORACLE

TABLE 255 ORACLE: BUSINESS OVERVIEW

FIGURE 52 ORACLE: COMPANY SNAPSHOT

TABLE 256 ORACLE: SOFTWARE/SERVICES OFFERED

TABLE 257 ORACLE: PRODUCT LAUNCHES AND ENHANCEMENTS

TABLE 258 ORACLE: DEALS

12.2.6 SAS INSTITUTE

TABLE 259 SAS INSTITUTE: BUSINESS OVERVIEW

TABLE 260 SAS INSTITUTE: SOFTWARE/SERVICES OFFERED

TABLE 261 SAS INSTITUTE: PRODUCT LAUNCHES AND ENHANCEMENTS

TABLE 262 SAS INSTITUTE: DEALS

12.2.7 QUALTRICS

TABLE 263 QUALTRICS: BUSINESS OVERVIEW

FIGURE 53 QUALTRICS: COMPANY SNAPSHOT

TABLE 264 QUALTRICS: SOFTWARE/SERVICES OFFERED

TABLE 265 QUALTRICS: PRODUCT LAUNCHES AND ENHANCEMENTS

TABLE 266 QUALTRICS: DEALS

12.2.8 BAIDU

TABLE 267 BAIDU: BUSINESS OVERVIEW

FIGURE 54 BAIDU: COMPANY SNAPSHOT

TABLE 268 BAIDU: SOFTWARE/SERVICES OFFERED

TABLE 269 BAIDU: PRODUCT LAUNCHES AND ENHANCEMENTS

12.2.9 INBENTA

TABLE 270 INBENTA: BUSINESS OVERVIEW

TABLE 271 INBENTA: SOFTWARE/SERVICES OFFERED

TABLE 272 INBENTA: PRODUCT LAUNCHES AND ENHANCEMENTS

TABLE 273 INBENTA: DEALS

12.2.10 BASIS TECHNOLOGY

TABLE 274 BASIS TECHNOLOGY: BUSINESS OVERVIEW

TABLE 275 BASIS TECHNOLOGY: SOFTWARE/SERVICES OFFERED

TABLE 276 BASIS TECHNOLOGY: PRODUCT LAUNCHES AND ENHANCEMENTS

TABLE 277 BASIS TECHNOLOGY: DEALS

12.2.11 NUANCE COMMUNICATIONS

TABLE 278 NUANCE COMMUNICATIONS: BUSINESS OVERVIEW

FIGURE 55 NUANCE COMMUNICATIONS: COMPANY SNAPSHOT

TABLE 279 NUANCE COMMUNICATIONS: SOFTWARE/SERVICES OFFERED

TABLE 280 NUANCE COMMUNICATIONS: PRODUCT LAUNCHES AND ENHANCEMENTS

TABLE 281 NUANCE COMMUNICATIONS: DEALS

12.2.12 EXPERT.AI

TABLE 282 EXPERT.AI: BUSINESS OVERVIEW

FIGURE 56 EXPERT.AI: COMPANY SNAPSHOT

TABLE 283 EXPERT.AI: SOFTWARE/SERVICES OFFERED

TABLE 284 EXPERT.AI: PRODUCT LAUNCHES AND ENHANCEMENTS

TABLE 285 EXPERT.AI: DEALS

12.2.13 LIVEPERSON

12.2.14 VERITONE

12.2.15 AUTOMATED INSIGHTS

12.2.16 BITEXT

12.2.17 CONVERSICA

12.2.18 ACCERN

12.2.19 KASISTO

12.2.20 KENSHO

12.2.21 ABBYY

12.2.22 MOSAIC

12.2.23 UNIPHORE

*Details on Business Overview, Software/Services offered, Recent Developments, MnM view, Key strengths, Strategic choices, Weakness and competitive threats might not be captured in case of unlisted companies.

12.3 STARTUP/SME PROFILES

12.3.1 OBSERVE.AI

12.3.2 LILT

12.3.3 COGNIGY

12.3.4 ADDEPTO

12.3.5 SKIT.AI

12.3.6 MINDTITAN

12.3.7 SUPERTEXT.AI

12.3.8 NARRATIVA

12.3.9 CRESTA

13 ADJACENT AND RELATED MARKETS (Page No. - 350)

13.1 NLP IN HEALTHCARE & LIFE SCIENCES

13.1.1 MARKET DEFINITION

13.1.2 MARKET OVERVIEW

13.1.2.1 NLP in healthcare & life sciences market, by component

TABLE 286 NLP IN HEALTHCARE & LIFE SCIENCES MARKET, BY COMPONENT, 2017–2021 (USD MILLION)

TABLE 287 NLP IN HEALTHCARE & LIFE SCIENCES MARKET, BY COMPONENT, 2022–2027 (USD MILLION)

TABLE 288 SOLUTIONS: NLP IN HEALTHCARE & LIFE SCIENCES MARKET, BY REGION, 2017–2021 (USD MILLION)

TABLE 289 SOLUTIONS: NLP IN HEALTHCARE & LIFE SCIENCES MARKET, BY REGION, 2022–2027 (USD MILLION)

TABLE 290 NLP IN HEALTHCARE & LIFE SCIENCES SOLUTIONS MARKET, BY TYPE, 2017–2021 (USD MILLION)

TABLE 291 NLP IN HEALTHCARE & LIFE SCIENCES SOLUTIONS MARKET, BY TYPE, 2022–2027 (USD MILLION)

TABLE 292 SERVICES: NLP IN HEALTHCARE & LIFE SCIENCES MARKET, BY TYPE, 2017–2021 (USD MILLION)

TABLE 293 SERVICES: NLP IN HEALTHCARE & LIFE SCIENCES MARKET, BY TYPE, 2022–2027 (USD MILLION)

TABLE 294 SERVICES: NLP IN HEALTHCARE & LIFE SCIENCES MARKET, BY REGION, 2017–2021 (USD MILLION)

TABLE 295 SERVICES: NLP IN HEALTHCARE & LIFE SCIENCES MARKET, BY REGION, 2022–2027 (USD MILLION)

13.1.2.2 NLP in healthcare & life sciences market, by type

TABLE 296 NLP IN HEALTHCARE & LIFE SCIENCES MARKET, BY TYPE, 2017–2021 (USD MILLION)

TABLE 297 NLP IN HEALTHCARE & LIFE SCIENCES MARKET, BY TYPE, 2022–2027 (USD MILLION)

13.1.2.3 NLP in healthcare & life sciences market, by application

TABLE 298 NLP IN HEALTHCARE & LIFE SCIENCES MARKET, BY APPLICATION, 2017–2021 (USD MILLION)

TABLE 299 NLP IN HEALTHCARE & LIFE SCIENCES MARKET, BY APPLICATION, 2022–2027 (USD MILLION)

13.1.2.4 NLP in healthcare & life sciences market, by size

TABLE 300 NLP IN HEALTHCARE & LIFE SCIENCES MARKET, BY SIZE, 2017–2021 (USD MILLION)

TABLE 301 NLP IN HEALTHCARE & LIFE SCIENCES MARKET, BY SIZE, 2022–2027 (USD MILLION)

13.1.2.5 NLP in healthcare & life sciences market, by deployment mode

TABLE 302 NLP IN HEALTHCARE & LIFE SCIENCES MARKET, BY DEPLOYMENT MODE, 2017–2021 (USD MILLION)

TABLE 303 NLP IN HEALTHCARE & LIFE SCIENCES MARKET, BY DEPLOYMENT MODE, 2022–2027 (USD MILLION)

13.1.2.6 NLP in healthcare & life sciences market, by technique

TABLE 304 NLP IN HEALTHCARE & LIFE SCIENCES MARKET, BY TECHNIQUE, 2017–2021 (USD MILLION)

TABLE 305 NLP IN HEALTHCARE & LIFE SCIENCES MARKET, BY TECHNIQUE, 2022–2027 (USD MILLION)

13.1.2.7 NLP in healthcare & life sciences market, by end user

TABLE 306 NLP IN HEALTHCARE & LIFE SCIENCES MARKET, BY END USER, 2017–2021 (USD MILLION)

TABLE 307 NLP IN HEALTHCARE & LIFE SCIENCES MARKET, BY END USER, 2022–2027 (USD MILLION)

13.1.2.8 NLP in healthcare & life sciences market, by region

TABLE 308 NLP IN HEALTHCARE & LIFE SCIENCES MARKET, BY REGION, 2017–2021 (USD MILLION)

TABLE 309 NLP IN HEALTHCARE & LIFE SCIENCES MARKET, BY REGION, 2022–2027 (USD MILLION)

13.2 SPEECH ANALYTICS MARKET

13.2.1 MARKET DEFINITION

13.2.2 MARKET OVERVIEW

13.2.2.1 Speech analytics market, by component

TABLE 310 SPEECH ANALYTICS MARKET, BY COMPONENT, 2018–2021(USD MILLION)

TABLE 311 SPEECH ANALYTICS MARKET, BY COMPONENT, 2022–2027(USD MILLION)

TABLE 312 SOLUTIONS: SPEECH ANALYTICS MARKET, BY REGION, 2018–2021(USD MILLION)

TABLE 313 SOLUTIONS: SPEECH ANALYTICS MARKET, BY REGION, 2022–2027(USD MILLION)

TABLE 314 SPEECH ANALYTICS MARKET, BY SERVICE, 2018–2021 (USD MILLION)

TABLE 315 SPEECH ANALYTICS MARKET, BY SERVICE, 2022–2027 (USD MILLION)

TABLE 316 SERVICES: SPEECH ANALYTICS MARKET, BY REGION, 2018–2021 (USD MILLION)

TABLE 317 SERVICES: SPEECH ANALYTICS MARKET, BY REGION, 2022–2027 (USD MILLION)

13.2.2.2 Speech analytics market, by business function

TABLE 318 SPEECH ANALYTICS MARKET, BY BUSINESS FUNCTION, 2018–2021(USD MILLION)

TABLE 319 SPEECH ANALYTICS MARKET, BY BUSINESS FUNCTION, 2022–2027 (USD MILLION)

13.2.2.3 Speech analytics market, by organization size

TABLE 320 SPEECH ANALYTICS MARKET, BY ORGANIZATION SIZE, 2018–2021 (USD MILLION)

TABLE 321 SPEECH ANALYTICS MARKET, BY ORGANIZATION SIZE, 2022–2027 (USD MILLION)

13.2.2.4 Speech analytics market, by deployment mode

TABLE 322 SPEECH ANALYTICS MARKET, BY DEPLOYMENT MODE, 2018–2021(USD MILLION)

TABLE 323 SPEECH ANALYTICS MARKET, BY DEPLOYMENT MODE, 2022–2027(USD MILLION)

13.2.2.5 Speech analytics market, by application

TABLE 324 SPEECH ANALYTICS MARKET, BY APPLICATION, 2017–2021 (USD MILLION)

TABLE 325 SPEECH ANALYTICS MARKET, BY APPLICATION, 2022–2027 (USD MILLION)

13.2.2.6 Speech analytics market, by vertical

TABLE 326 SPEECH ANALYTICS MARKET BY VERTICAL, 2017–2021 (USD MILLION)

TABLE 327 SPEECH ANALYTICS MARKET, BY VERTICAL, 2022–2027 (USD MILLION)

13.2.2.7 Speech analytics market, by region

TABLE 328 SPEECH ANALYTICS MARKET, BY REGION, 2017–2021 (USD MILLION)

TABLE 329 SPEECH ANALYTICS MARKET, BY REGION, 2022–2027 (USD MILLION)

14 APPENDIX (Page No. - 368)

14.1 DISCUSSION GUIDE

14.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL

14.3 CUSTOMIZATION OPTIONS

14.4 RELATED REPORTS

14.5 AUTHOR DETAILS

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:

NLP in Finance Market Size, and Share

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COMPANY NAME	DESIGNATION
Skit.ai	Senior Conversational User Experience Designer
Expert.ai	Senior Project Manager
Uniphore	Senior Solutions Consultant
Google	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:

NLP in Finance Market Size, and Share

To know about the assumptions considered for the study, Request for Free Sample Report

Global NLP in Finance Market Size: Top-down Approach

NLP in Finance Market Size, and Share

Data Triangulation

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: