Global NLP in Finance Market Analysis Report 2023: An $18.8 Billion Market by 2028 from $5.5 Billion in 2023 - Increasing Demand for Automated and Efficient Financial Services

DUBLIN, June 2, 2023 /PRNewswire/ -- The "Global NLP in Finance Market by Offering (Software, Services), Application (Customer Service & Support, Risk Management & Fraud Detection, Sentiment Analysis), Technology (Machine Learning, Deep Learning), Vertical, and Region - Forecast to 2028" report has been added to  ResearchAndMarkets.com's offering.

The global 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 market is anticipated to grow due to the increasing demand for automated and efficient financial services and rising need for accurate and real-time analysis of complex financial data.

By offering, managed services under services segment to register for fastest growing market rate during forecast period

The market for managed services in NLP in finance is expected to grow significantly in the coming years due to the increasing demand for NLP capabilities in the finance industry. The market is highly competitive, with several established players offering a wide range of NLP services to financial institutions of all sizes.

Some of the key players in this market include IBM, Amazon Web Services, Google, Microsoft, and SAS. These services allow financial institutions to focus on their core business while outsourcing NLP tasks to experts who have the necessary infrastructure, technology, and expertise to provide accurate and efficient NLP solutions.

By vertical, insurance segment to register fastest growing CAGR during forecast period

Insurance is a financial product that protects against unforeseen events or losses. NLP is increasingly used in the insurance industry to improve various processes, including underwriting, claims processing, customer service, and fraud detection.

One of the key areas where NLP is used in insurance is underwriting. Insurance companies use NLP to analyze large amounts of data from various sources, such as social media, credit scores, and medical records, to assess risk and determine premiums.

North America to account for the largest market size during the forecast period

The presence of a growing tech-savvy population, high internet penetration, and advances in AI has resulted in the growth of NLP solutions used in the finance sector. Most of the customers in North America have been leveraging NLP to improve their efficiency, reduce costs, and enhance the customer experience, ultimately leading to better business outcomes.

The rising popularity and higher reach of NLP further empower SMEs and startups in the region to harness NLP technology as a cost-effective and technologically advanced tool for building and promoting business, growing consumer base, and reaching out to a wider audience.

Premium Insights

• Increasing Popularity of Chatbots Across Finance and Improving Performance of NLP Models to Drive Market Growth
• Customer Service and Support Application Segment to Account for Highest Growth Rate
• Software and Banking to be Largest Shareholders in North America in 2023
• North America to Hold Largest Market Share in 2023

Market Dynamics

Drivers

• Increasing Demand for Automated and Efficient Financial Services Worldwide
• Rising Need for Accurate and Real-Time Analysis of Complex Financial Data
• Emergence of AI and ML Models

Restraints

• Lack of Standardization in NLP-based Financial Applications and Services
• Difficulty in Managing Large Volumes of Unstructured Data
• Complexity in Developing and Training Sophisticated NLP Models

Opportunities

• Development of Customized NLP Solutions for Specific Financial Services and Use Cases
• Integration of NLP with Blockchain and Big Data to Enhance Accuracy and Efficiency of Financial Operations
• Growing Adoption of NLP-Powered Chatbots and Virtual Assistants

Challenges

• High Implementation Costs Associated with NLP
• Limited Availability of Skilled Professionals
• Data Privacy Concerns Associated with Use of NLP

Ethics and Implications of NLP in Finance

• Bias and Fairness
• Privacy and Security
• Intellectual Property
• Accountability and Responsibility
• Societal and Economic Impact

Ecosystem Analysis

• NLP in Finance Technology Providers
• NLP in Finance Cloud Platform Providers
• NLP in Finance API and As-A-Service Providers
• NLP in Finance Hardware Providers
• NLP in Finance End-users
• NLP in Finance Regulators

NLP in Finance Tools and Framework

• Tensorflow
• Pytorch
• Keras
• Nltk
• Apache Opennlp
• Spacy
• Gensim
• Allennlp
• Flair
• Stanford Corenlp

Case Study Analysis

• Case Study 1: Natwest Improved Speed and Accuracy of Complaint-Handling Process Through IBM
• Case Study 2: Ayasdi's NLP Platform Helped J.P. Morgan Chase Ramp Up Risk Assessment Techniques
• Case Study 3: Capital One Eliminated Inefficiencies in Customer Query Resolution Through NLP
• Case Study 4: Blackrock Identified New Investment Avenues by Analyzing Large Volumes of Unstructured Data
• Case Study 5: Yseop Assisted Td Ameritrade in Discovering New Customer Insights
• Case Study 6: Allianz Witnessed Substantial Improvement in Insurance Claims Processing Through NLP
• Case Study 7: UBS Trained Datasets Through NLP to Augment Risk Management Processes
• Case Study 8: Citi Added Personalized Touch to Customer Recommendations Via NLP-based Query Analysis
• Case Study 9: Barclays Scaled Its Trading and Investment Analysis Processes Via Ayasdi's NLP Tool
• Case Study 10: Goldman Sachs Augmented Its Financial R&D Prowess
• Case Study 11: NLP Empowered Kabbage with Smarter Decision-Making for Loan Disbursal
• Case Study 12: Chainalysis Deployed NLP for Fraud Prevention in Crypto Trading

Best Practices in Market

• Domain-Specific Data Selection and Data Cleaning
• Feature Engineering
• Model Selection
• Evaluation Metrics
• Cross-Validation
• Regularization
• Hyperparameter Tuning
• Transfer Learning
• Interpretability
• Regulatory Compliance
• Backtesting and Deployment

Technology Roadmap of NLP in Finance

• NLP in Finance Roadmap Till 2030
• Pre-2020
• 2020-2022
• Short-Term (2023-2025)
• Mid-Term (2026-2028)
• Long-Term (2029-2030)

Current and Emerging Business Models

• SaaS Model
• Consulting Services Model
• Partner Programs (Revenue Sharing Model)
• Pay-Per-Use Model

NLP in Finance's Impact on Adjacent Niche Technologies

• High-Frequency Trading and Electronic Trading Platforms
• Financial Cybersecurity
• Regulatory Technology (RegTech)

Company Profiles

Key Players

• Microsoft
• IBM
• Google
• AWS
• Oracle
• SAS Institute
• Qualtrics
• Baidu
• Inbenta
• Basis Technology
• Nuance Communications
• Expert.AI
• Liveperson
• Veritone
• Automated Insights
• Bitext
• Conversica
• Accern
• Kasisto
• Kensho
• ABBYY
• Mosaic
• Uniphore

Startup/SME Profiles

• Observe.AI
• Lilt
• Cognigy
• Addepto
• Skit.AI
• Mindtitan
• Supertext.AI
• Narrativa
• Cresta

For more information about this report visit https://www.researchandmarkets.com/r/prvxqp

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